diff --git a/build/package.json b/build/package.json
index 5d9c00d..40a7962 100644
--- a/build/package.json
+++ b/build/package.json
@@ -1,8 +1,8 @@
 {
     "name": "@vladmandic/face-api",
-    "version": "0.5.2",
+    "version": "0.5.3",
     "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
-    "main": "dist/face-api.node.js",
+    "main": "build/src/index.js",
     "browser": "dist/face-api.esm.js",
     "typings": "./build/src/index.d.ts",
     "engines": {
@@ -12,11 +12,11 @@
     "scripts": {
         "compile": "tsc",
         "clean": "rimraf build/ dist/",
-        "cjs": "esbuild --bundle --outfile=./dist/face-api.cjs.js --target=es2017 --platform=browser --format=cjs --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-        "node": "esbuild --bundle --outfile=./dist/face-api.node.js --target=es2017 --platform=node --format=esm --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-        "esm": "esbuild --bundle --outfile=./dist/face-api.esm.js --target=es2017 --platform=browser --format=esm --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-        "iife": "esbuild --bundle --outfile=./dist/face-api.iife.js --target=es2017 --platform=browser --format=iife --global-name=faceapi --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-        "minify": "esbuild --bundle --outfile=./dist/face-api.min.js --target=es2017 --platform=browser --format=iife --sourcemap --global-name=faceapi --log-level=error --minify --tsconfig=./tsconfig.json build/src/index.js",
+        "cjs": "esbuild --bundle --outfile=./dist/face-api.cjs.js --target=esnext --platform=browser --format=cjs --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "node": "esbuild --bundle --outfile=./dist/face-api.node.js --target=esnext --platform=node --format=esm --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "esm": "esbuild --bundle --outfile=./dist/face-api.esm.js --target=esnext --platform=browser --format=esm --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "iife": "esbuild --bundle --outfile=./dist/face-api.iife.js --target=esnext --platform=browser --format=iife --global-name=faceapi --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "minify": "esbuild --bundle --outfile=./dist/face-api.min.js --target=esnext --platform=browser --format=iife --sourcemap --global-name=faceapi --external:@tensorflow/tfjs --log-level=error --minify --tsconfig=./tsconfig.json build/src/index.js",
         "build": "npm run clean && npm run compile && npm run cjs && npm run esm && npm run iife && npm run node && npm run minify"
     },
     "keywords": [
@@ -42,14 +42,14 @@
     },
     "homepage": "https://github.com/vladmandic/face-api#readme",
     "dependencies": {
-        "@tensorflow/tfjs-core": "^2.4.0"
+        "@tensorflow/tfjs": "^2.6.0"
     },
     "devDependencies": {
-        "@types/node": "^14.10.2",
+        "@types/node": "^14.11.7",
         "esbuild": "^0.6.34",
         "rimraf": "^3.0.2",
         "ts-node": "^9.0.0",
-        "tslib": "^2.0.1",
-        "typescript": "^4.1.0-dev.20200916"
+        "tslib": "^2.0.2",
+        "typescript": "^4.1.0-dev.20201008"
     }
 }
diff --git a/build/src/NeuralNetwork.d.ts b/build/src/NeuralNetwork.d.ts
index 01078d8..faa20fe 100644
--- a/build/src/NeuralNetwork.d.ts
+++ b/build/src/NeuralNetwork.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from './common';
 export declare abstract class NeuralNetwork<TNetParams> {
     protected _name: string;
diff --git a/build/src/NeuralNetwork.d.ts.map b/build/src/NeuralNetwork.d.ts.map
index 65649b7..58a5ae3 100644
--- a/build/src/NeuralNetwork.d.ts.map
+++ b/build/src/NeuralNetwork.d.ts.map
@@ -1 +1 @@
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\ No newline at end of file
+{"version":3,"file":"NeuralNetwork.d.ts","sourceRoot":"","sources":["../../src/NeuralNetwork.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,YAAY,EAAE,MAAM,UAAU,CAAC;AAKxC,8BAAsB,aAAa,CAAC,UAAU;IAKhC,SAAS,CAAC,KAAK,EAAE,MAAM;IAHnC,SAAS,CAAC,OAAO,EAAE,UAAU,GAAG,SAAS,CAAY;IACrD,SAAS,CAAC,cAAc,EAAE,YAAY,EAAE,CAAK;gBAEvB,KAAK,EAAE,MAAM;IAWnC,IAAW,MAAM,IAAI,UAAU,GAAG,SAAS,CAAwB;IACnE,IAAW,aAAa,IAAI,YAAY,EAAE,CAA+B;IACzE,IAAW,QAAQ,IAAI,OAAO,CAAyB;IAEhD,gBAAgB,CAAC,SAAS,EAAE,MAAM,GAAG,EAAE,CAAC,MAAM;IAK9C,qBAAqB,CAAC,SAAS,EAAE,MAAM,EAAE,MAAM,EAAE,EAAE,CAAC,MAAM;IAM1D,YAAY;;;;IAOZ,kBAAkB;;;;IAIlB,eAAe;;;;IAIf,QAAQ;IAMR,MAAM;IAQN,OAAO,CAAC,gBAAgB,GAAE,OAAc;IAUxC,eAAe,IAAI,YAAY;IAQzB,IAAI,CAAC,YAAY,EAAE,YAAY,GAAG,MAAM,GAAG,SAAS,GAAG,OAAO,CAAC,IAAI,CAAC;IASpE,WAAW,CAAC,GAAG,EAAE,MAAM,GAAG,SAAS;IASnC,YAAY,CAAC,QAAQ,EAAE,MAAM,GAAG,SAAS;IAoB/C,iBAAiB,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;IAU9C,cAAc,CAAC,OAAO,EAAE,YAAY;IAU3C,OAAO,CAAC,oBAAoB;IAqB5B,SAAS,CAAC,QAAQ,CAAC,mBAAmB,IAAI,MAAM;IAChD,SAAS,CAAC,QAAQ,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc,GAAG;QAAE,MAAM,EAAE,UAAU,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;IAClI,SAAS,CAAC,QAAQ,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY,GAAG;QAAE,MAAM,EAAE,UAAU,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;CAC/G"}
\ No newline at end of file
diff --git a/build/src/NeuralNetwork.js b/build/src/NeuralNetwork.js
index 87ded09..cf6209a 100644
--- a/build/src/NeuralNetwork.js
+++ b/build/src/NeuralNetwork.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { getModelUris } from './common/getModelUris';
 import { loadWeightMap } from './dom';
 import { env } from './env';
diff --git a/build/src/NeuralNetwork.js.map b/build/src/NeuralNetwork.js.map
index b264e2c..6cd6e84 100644
--- a/build/src/NeuralNetwork.js.map
+++ b/build/src/NeuralNetwork.js.map
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/AgeGenderNet.d.ts b/build/src/ageGenderNet/AgeGenderNet.d.ts
index 341d671..cd032e3 100644
--- a/build/src/ageGenderNet/AgeGenderNet.d.ts
+++ b/build/src/ageGenderNet/AgeGenderNet.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { TinyXception } from '../xception/TinyXception';
 import { AgeAndGenderPrediction, NetOutput, NetParams } from './types';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/ageGenderNet/AgeGenderNet.d.ts.map b/build/src/ageGenderNet/AgeGenderNet.d.ts.map
index 6a4e697..3f33574 100644
--- a/build/src/ageGenderNet/AgeGenderNet.d.ts.map
+++ b/build/src/ageGenderNet/AgeGenderNet.d.ts.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/AgeGenderNet.js b/build/src/ageGenderNet/AgeGenderNet.js
index eab848c..0b04d0b 100644
--- a/build/src/ageGenderNet/AgeGenderNet.js
+++ b/build/src/ageGenderNet/AgeGenderNet.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { seperateWeightMaps } from '../faceProcessor/util';
 import { TinyXception } from '../xception/TinyXception';
diff --git a/build/src/ageGenderNet/AgeGenderNet.js.map b/build/src/ageGenderNet/AgeGenderNet.js.map
index 83c3ef7..6b06955 100644
--- a/build/src/ageGenderNet/AgeGenderNet.js.map
+++ b/build/src/ageGenderNet/AgeGenderNet.js.map
@@ -1 +1 @@
-{"version":3,"file":"AgeGenderNet.js","sourceRoot":"","sources":["../../../src/ageGenderNet/AgeGenderNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,+BAA+B,CAAC;AACpE,OAAO,EAAE,kBAAkB,EAAE,MAAM,uBAAuB,CAAC;AAC3D,OAAO,EAAE,YAAY,EAAE,MAAM,0BAA0B,CAAC;AACxD,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAC1E,OAAO,EAA0B,MAAM,EAAwB,MAAM,SAAS,CAAC;AAC/E,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,QAAQ,EAAa,UAAU,EAAE,MAAM,QAAQ,CAAC;AAEzD,MAAM,OAAO,YAAa,SAAQ,aAAwB;IAIxD,YAAY,uBAAqC,IAAI,YAAY,CAAC,CAAC,CAAC;QAClE,KAAK,CAAC,cAAc,CAAC,CAAA;QACrB,IAAI,CAAC,qBAAqB,GAAG,oBAAoB,CAAA;IACnD,CAAC;IAED,IAAW,oBAAoB;QAC7B,OAAO,IAAI,CAAC,qBAAqB,CAAA;IACnC,CAAC;IAEM,MAAM,CAAC,KAA6B;QAEzC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,gCAAgC,CAAC,CAAA;SAC/D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,kBAAkB,GAAG,KAAK,YAAY,QAAQ;gBAClD,CAAC,CAAC,IAAI,CAAC,oBAAoB,CAAC,YAAY,CAAC,KAAK,CAAC;gBAC/C,CAAC,CAAC,KAAK,CAAA;YAET,MAAM,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,kBAAkB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,IAAI,CAAC,kBAAkB,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC5G,MAAM,GAAG,GAAG,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,IAAI,EAAE,CAAA;YAC7D,MAAM,MAAM,GAAG,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,EAAE,CAAC,MAAM,CAAC,CAAA;YAC5D,OAAO,EAAE,GAAG,EAAE,MAAM,EAAE,CAAA;QACxB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,YAAY,CAAC,KAA6B;QAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,GAAG,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC1C,OAAO,EAAE,GAAG,EAAE,MAAM,EAAE,EAAE,CAAC,OAAO,CAAC,MAAM,CAAC,EAAE,CAAA;QAC5C,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,mBAAmB,CAAC,KAAgB;QAC/C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,GAAG,GAAG,MAAM,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAA;QAE7C,MAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,GAAG,CAAC,CAAA;QAChC,MAAM,OAAO,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,MAAM,CAAC,CAAA;QACtC,MAAM,mBAAmB,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,SAAS,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC;YACtD,SAAS;YACT,YAAY,EAAE,OAAO,CAAC,CAAC,CAAC;SACzB,CAAC,CAAC,CAAA;QAEH,MAAM,kBAAkB,GAAG,MAAM,OAAO,CAAC,GAAG,CAC1C,mBAAmB,CAAC,GAAG,CAAC,KAAK,EAAE,EAAE,SAAS,EAAE,YAAY,EAAE,EAAE,EAAE;YAC5D,MAAM,GAAG,GAAG,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACvC,MAAM,QAAQ,GAAG,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/C,MAAM,MAAM,GAAG,QAAQ,GAAG,GAAG,CAAA;YAC7B,MAAM,MAAM,GAAG,MAAM,CAAC,CAAC,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,MAAM,CAAA;YACnD,MAAM,iBAAiB,GAAG,MAAM,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,CAAA;YAE5D,SAAS,CAAC,OAAO,EAAE,CAAA;YACnB,YAAY,CAAC,OAAO,EAAE,CAAA;YACtB,OAAO,EAAE,GAAG,EAAE,MAAM,EAAE,iBAAiB,EAAE,CAAA;QAC3C,CAAC,CAAC,CACH,CAAA;QACD,GAAG,CAAC,GAAG,CAAC,OAAO,EAAE,CAAA;QACjB,GAAG,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;QAEpB,OAAO,QAAQ,CAAC,YAAY;YAC1B,CAAC,CAAC,kBAAkB;YACpB,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAA;IAC3B,CAAC;IAES,mBAAmB;QAC3B,OAAO,kBAAkB,CAAA;IAC3B,CAAC;IAEM,OAAO,CAAC,mBAA4B,IAAI;QAC7C,IAAI,CAAC,oBAAoB,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAA;QACnD,KAAK,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAA;IACjC,CAAC;IAEM,oBAAoB,CAAC,OAAqB;QAC/C,MAAM,EAAE,MAAM,EAAE,aAAa,EAAE,GAAG,IAAI,CAAC,uBAAuB,CAAC,OAAO,CAAC,CAAA;QACvE,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,uBAAuB,CAAC,OAAqB;QAClD,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAE/D,MAAM,EAAE,mBAAmB,EAAE,aAAa,EAAE,GAAG,kBAAkB,CAAC,SAAS,CAAC,CAAA;QAE5E,IAAI,CAAC,oBAAoB,CAAC,iBAAiB,CAAC,mBAAmB,CAAC,CAAA;QAEhE,OAAO,0BAA0B,CAAC,aAAa,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAE3C,MAAM,oBAAoB,GAAG,CAAC,GAAG,GAAG,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,GAAG,CAAC,GAAG,CAAC,CAAC,CAAA;QAE1D,MAAM,uBAAuB,GAAG,OAAO,CAAC,KAAK,CAAC,CAAC,EAAE,OAAO,CAAC,MAAM,GAAG,oBAAoB,CAAC,CAAA;QACvF,MAAM,iBAAiB,GAAG,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,GAAG,oBAAoB,CAAC,CAAA;QAE9E,IAAI,CAAC,oBAAoB,CAAC,cAAc,CAAC,uBAAuB,CAAC,CAAA;QACjE,OAAO,IAAI,CAAC,uBAAuB,CAAC,iBAAiB,CAAC,CAAA;IACxD,CAAC;CACF"}
\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts b/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts
index 163969a..6299baa 100644
--- a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts
+++ b/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { NetParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts.map b/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts.map
index 522e13b..6992cb9 100644
--- a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/ageGenderNet/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAmE,YAAY,EAAE,MAAM,WAAW,CAAC;AAC1G,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAEpC,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAsBtD"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/ageGenderNet/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAmE,YAAY,EAAE,MAAM,WAAW,CAAC;AAC1G,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAEpC,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAsBtD"}
\ No newline at end of file
diff --git a/build/src/ageGenderNet/types.d.ts b/build/src/ageGenderNet/types.d.ts
index 07b4b6c..ebdc03e 100644
--- a/build/src/ageGenderNet/types.d.ts
+++ b/build/src/ageGenderNet/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FCParams } from '../common';
 export declare type AgeAndGenderPrediction = {
     age: number;
diff --git a/build/src/ageGenderNet/types.d.ts.map b/build/src/ageGenderNet/types.d.ts.map
index 10bcec8..8a0f90d 100644
--- a/build/src/ageGenderNet/types.d.ts.map
+++ b/build/src/ageGenderNet/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/ageGenderNet/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,MAAM,WAAW,CAAC;AAErC,oBAAY,sBAAsB,GAAG;IACnC,GAAG,EAAE,MAAM,CAAA;IACX,MAAM,EAAE,MAAM,CAAA;IACd,iBAAiB,EAAE,MAAM,CAAA;CAC1B,CAAA;AAED,oBAAY,MAAM;IAChB,MAAM,WAAW;IACjB,IAAI,SAAS;CACd;AAED,oBAAY,SAAS,GAAG;IAAE,GAAG,EAAE,EAAE,CAAC,QAAQ,CAAC;IAAC,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CAAE,CAAA;AAEjE,oBAAY,SAAS,GAAG;IACtB,EAAE,EAAE;QACF,GAAG,EAAE,QAAQ,CAAA;QACb,MAAM,EAAE,QAAQ,CAAA;KACjB,CAAA;CACF,CAAA"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/ageGenderNet/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,MAAM,WAAW,CAAC;AAErC,oBAAY,sBAAsB,GAAG;IACnC,GAAG,EAAE,MAAM,CAAA;IACX,MAAM,EAAE,MAAM,CAAA;IACd,iBAAiB,EAAE,MAAM,CAAA;CAC1B,CAAA;AAED,oBAAY,MAAM;IAChB,MAAM,WAAW;IACjB,IAAI,SAAS;CACd;AAED,oBAAY,SAAS,GAAG;IAAE,GAAG,EAAE,EAAE,CAAC,QAAQ,CAAC;IAAC,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CAAE,CAAA;AAEjE,oBAAY,SAAS,GAAG;IACtB,EAAE,EAAE;QACF,GAAG,EAAE,QAAQ,CAAA;QACb,MAAM,EAAE,QAAQ,CAAA;KACjB,CAAA;CACF,CAAA"}
\ No newline at end of file
diff --git a/build/src/common/convLayer.d.ts b/build/src/common/convLayer.d.ts
index d1dc729..799519f 100644
--- a/build/src/common/convLayer.d.ts
+++ b/build/src/common/convLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvParams } from './types';
 export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;
 //# sourceMappingURL=convLayer.d.ts.map
\ No newline at end of file
diff --git a/build/src/common/convLayer.d.ts.map b/build/src/common/convLayer.d.ts.map
index f91411f..1f9c52e 100644
--- a/build/src/common/convLayer.d.ts.map
+++ b/build/src/common/convLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"convLayer.d.ts","sourceRoot":"","sources":["../../../src/common/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,UAAU,EAAE,MAAM,SAAS,CAAC;AAErC,wBAAgB,SAAS,CACvB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,UAAU,EAClB,OAAO,GAAE,OAAO,GAAG,MAAe,EAClC,QAAQ,GAAE,OAAe,GACxB,EAAE,CAAC,QAAQ,CASb"}
\ No newline at end of file
+{"version":3,"file":"convLayer.d.ts","sourceRoot":"","sources":["../../../src/common/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,UAAU,EAAE,MAAM,SAAS,CAAC;AAErC,wBAAgB,SAAS,CACvB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,UAAU,EAClB,OAAO,GAAE,OAAO,GAAG,MAAe,EAClC,QAAQ,GAAE,OAAe,GACxB,EAAE,CAAC,QAAQ,CASb"}
\ No newline at end of file
diff --git a/build/src/common/convLayer.js b/build/src/common/convLayer.js
index 411439a..596596c 100644
--- a/build/src/common/convLayer.js
+++ b/build/src/common/convLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function convLayer(x, params, padding = 'same', withRelu = false) {
     return tf.tidy(() => {
         const out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
diff --git a/build/src/common/convLayer.js.map b/build/src/common/convLayer.js.map
index 81cc539..9fb033f 100644
--- a/build/src/common/convLayer.js.map
+++ b/build/src/common/convLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/common/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,SAAS,CACvB,CAAc,EACd,MAAkB,EAClB,UAA4B,MAAM,EAClC,WAAoB,KAAK;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/common/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,SAAS,CACvB,CAAc,EACd,MAAkB,EAClB,UAA4B,MAAM,EAClC,WAAoB,KAAK;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/depthwiseSeparableConv.d.ts b/build/src/common/depthwiseSeparableConv.d.ts
index 1fff291..926b9bb 100644
--- a/build/src/common/depthwiseSeparableConv.d.ts
+++ b/build/src/common/depthwiseSeparableConv.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { SeparableConvParams } from './types';
 export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams, stride: [number, number]): tf.Tensor4D;
 //# sourceMappingURL=depthwiseSeparableConv.d.ts.map
\ No newline at end of file
diff --git a/build/src/common/depthwiseSeparableConv.d.ts.map b/build/src/common/depthwiseSeparableConv.d.ts.map
index 754cd9d..e95bbab 100644
--- a/build/src/common/depthwiseSeparableConv.d.ts.map
+++ b/build/src/common/depthwiseSeparableConv.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"depthwiseSeparableConv.d.ts","sourceRoot":"","sources":["../../../src/common/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,sBAAsB,CACpC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB,EAC3B,MAAM,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,GACvB,EAAE,CAAC,QAAQ,CAMb"}
\ No newline at end of file
+{"version":3,"file":"depthwiseSeparableConv.d.ts","sourceRoot":"","sources":["../../../src/common/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,sBAAsB,CACpC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB,EAC3B,MAAM,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,GACvB,EAAE,CAAC,QAAQ,CAMb"}
\ No newline at end of file
diff --git a/build/src/common/depthwiseSeparableConv.js b/build/src/common/depthwiseSeparableConv.js
index a91a632..dcf2be1 100644
--- a/build/src/common/depthwiseSeparableConv.js
+++ b/build/src/common/depthwiseSeparableConv.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function depthwiseSeparableConv(x, params, stride) {
     return tf.tidy(() => {
         let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
diff --git a/build/src/common/depthwiseSeparableConv.js.map b/build/src/common/depthwiseSeparableConv.js.map
index 9390288..995ec5f 100644
--- a/build/src/common/depthwiseSeparableConv.js.map
+++ b/build/src/common/depthwiseSeparableConv.js.map
@@ -1 +1 @@
-{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/common/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,sBAAsB,CACpC,CAAc,EACd,MAA2B,EAC3B,MAAwB;IAExB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,EAAE,MAAM,CAAC,CAAA;QACjG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAC9B,OAAO,GAAG,CAAA;IACZ,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/common/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,sBAAsB,CACpC,CAAc,EACd,MAA2B,EAC3B,MAAwB;IAExB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,EAAE,MAAM,CAAC,CAAA;QACjG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAC9B,OAAO,GAAG,CAAA;IACZ,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractConvParamsFactory.js b/build/src/common/extractConvParamsFactory.js
index 5aec5cd..3d5e53f 100644
--- a/build/src/common/extractConvParamsFactory.js
+++ b/build/src/common/extractConvParamsFactory.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function extractConvParamsFactory(extractWeights, paramMappings) {
     return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
         const filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
diff --git a/build/src/common/extractConvParamsFactory.js.map b/build/src/common/extractConvParamsFactory.js.map
index a74a76c..29b8a26 100644
--- a/build/src/common/extractConvParamsFactory.js.map
+++ b/build/src/common/extractConvParamsFactory.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,wBAAwB,CACtC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAGpB,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,wBAAwB,CACtC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAGpB,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractFCParamsFactory.js b/build/src/common/extractFCParamsFactory.js
index 0065198..5e61287 100644
--- a/build/src/common/extractFCParamsFactory.js
+++ b/build/src/common/extractFCParamsFactory.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function extractFCParamsFactory(extractWeights, paramMappings) {
     return function (channelsIn, channelsOut, mappedPrefix) {
         const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
diff --git a/build/src/common/extractFCParamsFactory.js.map b/build/src/common/extractFCParamsFactory.js.map
index da079d4..5ef4c60 100644
--- a/build/src/common/extractFCParamsFactory.js.map
+++ b/build/src/common/extractFCParamsFactory.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractFCParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractFCParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAK5C,MAAM,UAAU,sBAAsB,CACpC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,YAAoB;QAGpB,MAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QACnG,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAExD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,EAAE,UAAU;YACnB,IAAI,EAAE,OAAO;SACd,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractFCParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractFCParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAKvC,MAAM,UAAU,sBAAsB,CACpC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,YAAoB;QAGpB,MAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QACnG,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAExD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,EAAE,UAAU;YACnB,IAAI,EAAE,OAAO;SACd,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractSeparableConvParamsFactory.js b/build/src/common/extractSeparableConvParamsFactory.js
index 5d9a7a8..d78f851 100644
--- a/build/src/common/extractSeparableConvParamsFactory.js
+++ b/build/src/common/extractSeparableConvParamsFactory.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { SeparableConvParams } from './types';
 export function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
     return function (channelsIn, channelsOut, mappedPrefix) {
diff --git a/build/src/common/extractSeparableConvParamsFactory.js.map b/build/src/common/extractSeparableConvParamsFactory.js.map
index fb2fcd5..bdc9a9b 100644
--- a/build/src/common/extractSeparableConvParamsFactory.js.map
+++ b/build/src/common/extractSeparableConvParamsFactory.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractSeparableConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractSeparableConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAwC,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAEpF,MAAM,UAAU,iCAAiC,CAC/C,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UAAS,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAC3E,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,CAAC,CAAC,CAAA;QAC/F,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAC/G,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,8BAA8B,CAC5C,kBAAqE;IAGrE,OAAO,UAAU,MAAc;QAC7B,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractSeparableConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractSeparableConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAwC,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAEpF,MAAM,UAAU,iCAAiC,CAC/C,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UAAS,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAC3E,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,CAAC,CAAC,CAAA;QAC/F,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAC/G,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,8BAA8B,CAC5C,kBAAqE;IAGrE,OAAO,UAAU,MAAc;QAC7B,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/fullyConnectedLayer.d.ts b/build/src/common/fullyConnectedLayer.d.ts
index 4e4acbc..0264f31 100644
--- a/build/src/common/fullyConnectedLayer.d.ts
+++ b/build/src/common/fullyConnectedLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FCParams } from './types';
 export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;
 //# sourceMappingURL=fullyConnectedLayer.d.ts.map
\ No newline at end of file
diff --git a/build/src/common/fullyConnectedLayer.d.ts.map b/build/src/common/fullyConnectedLayer.d.ts.map
index 77b3a09..22b3548 100644
--- a/build/src/common/fullyConnectedLayer.d.ts.map
+++ b/build/src/common/fullyConnectedLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"fullyConnectedLayer.d.ts","sourceRoot":"","sources":["../../../src/common/fullyConnectedLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,MAAM,SAAS,CAAC;AAEnC,wBAAgB,mBAAmB,CACjC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,QAAQ,GACf,EAAE,CAAC,QAAQ,CAOb"}
\ No newline at end of file
+{"version":3,"file":"fullyConnectedLayer.d.ts","sourceRoot":"","sources":["../../../src/common/fullyConnectedLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,MAAM,SAAS,CAAC;AAEnC,wBAAgB,mBAAmB,CACjC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,QAAQ,GACf,EAAE,CAAC,QAAQ,CAOb"}
\ No newline at end of file
diff --git a/build/src/common/fullyConnectedLayer.js b/build/src/common/fullyConnectedLayer.js
index 9ac4178..992044a 100644
--- a/build/src/common/fullyConnectedLayer.js
+++ b/build/src/common/fullyConnectedLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function fullyConnectedLayer(x, params) {
     return tf.tidy(() => tf.add(tf.matMul(x, params.weights), params.bias));
 }
diff --git a/build/src/common/fullyConnectedLayer.js.map b/build/src/common/fullyConnectedLayer.js.map
index ca4cd54..828c7d2 100644
--- a/build/src/common/fullyConnectedLayer.js.map
+++ b/build/src/common/fullyConnectedLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"fullyConnectedLayer.js","sourceRoot":"","sources":["../../../src/common/fullyConnectedLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,mBAAmB,CACjC,CAAc,EACd,MAAgB;IAEhB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAClB,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAC5B,MAAM,CAAC,IAAI,CACZ,CACF,CAAA;AACH,CAAC"}
\ No newline at end of file
+{"version":3,"file":"fullyConnectedLayer.js","sourceRoot":"","sources":["../../../src/common/fullyConnectedLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,mBAAmB,CACjC,CAAc,EACd,MAAgB;IAEhB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAClB,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAC5B,MAAM,CAAC,IAAI,CACZ,CACF,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/types.d.ts b/build/src/common/types.d.ts
index 0c19928..60bb7f5 100644
--- a/build/src/common/types.d.ts
+++ b/build/src/common/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
 export declare type ParamMapping = {
     originalPath?: string;
diff --git a/build/src/common/types.d.ts.map b/build/src/common/types.d.ts.map
index 4da6364..765e315 100644
--- a/build/src/common/types.d.ts.map
+++ b/build/src/common/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/common/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,oBAAY,sBAAsB,GAAG,CAAC,UAAU,EAAE,MAAM,KAAK,YAAY,CAAA;AAEzE,oBAAY,YAAY,GAAG;IACzB,YAAY,CAAC,EAAE,MAAM,CAAA;IACrB,SAAS,EAAE,MAAM,CAAA;CAClB,CAAA;AAED,oBAAY,UAAU,GAAG;IACvB,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;CAClB,CAAA;AAED,oBAAY,QAAQ,GAAG;IACrB,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;CAClB,CAAA;AAED,qBAAa,mBAAmB;IAErB,gBAAgB,EAAE,EAAE,CAAC,QAAQ;IAC7B,gBAAgB,EAAE,EAAE,CAAC,QAAQ;IAC7B,IAAI,EAAE,EAAE,CAAC,QAAQ;gBAFjB,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,IAAI,EAAE,EAAE,CAAC,QAAQ;CAE3B"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/common/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,oBAAY,sBAAsB,GAAG,CAAC,UAAU,EAAE,MAAM,KAAK,YAAY,CAAA;AAEzE,oBAAY,YAAY,GAAG;IACzB,YAAY,CAAC,EAAE,MAAM,CAAA;IACrB,SAAS,EAAE,MAAM,CAAA;CAClB,CAAA;AAED,oBAAY,UAAU,GAAG;IACvB,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;CAClB,CAAA;AAED,oBAAY,QAAQ,GAAG;IACrB,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;CAClB,CAAA;AAED,qBAAa,mBAAmB;IAErB,gBAAgB,EAAE,EAAE,CAAC,QAAQ;IAC7B,gBAAgB,EAAE,EAAE,CAAC,QAAQ;IAC7B,IAAI,EAAE,EAAE,CAAC,QAAQ;gBAFjB,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,IAAI,EAAE,EAAE,CAAC,QAAQ;CAE3B"}
\ No newline at end of file
diff --git a/build/src/dom/NetInput.d.ts b/build/src/dom/NetInput.d.ts
index 8a253f1..8ba31ac 100644
--- a/build/src/dom/NetInput.d.ts
+++ b/build/src/dom/NetInput.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Dimensions } from '../classes/Dimensions';
 import { TResolvedNetInput } from './types';
 export declare class NetInput {
diff --git a/build/src/dom/NetInput.d.ts.map b/build/src/dom/NetInput.d.ts.map
index 34be108..cf6df42 100644
--- a/build/src/dom/NetInput.d.ts.map
+++ b/build/src/dom/NetInput.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"NetInput.d.ts","sourceRoot":"","sources":["../../../src/dom/NetInput.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,UAAU,EAAE,MAAM,uBAAuB,CAAC;AAMnD,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE5C,qBAAa,QAAQ;IACnB,OAAO,CAAC,aAAa,CAAuC;IAC5D,OAAO,CAAC,SAAS,CAA0B;IAC3C,OAAO,CAAC,UAAU,CAAQ;IAC1B,OAAO,CAAC,kBAAkB,CAAiB;IAE3C,OAAO,CAAC,gBAAgB,CAAiB;IACzC,OAAO,CAAC,UAAU,CAAQ;gBAGxB,MAAM,EAAE,KAAK,CAAC,iBAAiB,CAAC,EAChC,iBAAiB,GAAE,OAAe;IAkCpC,IAAW,YAAY,IAAI,KAAK,CAAC,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAE1D;IAED,IAAW,QAAQ,IAAI,iBAAiB,EAAE,CAEzC;IAED,IAAW,YAAY,IAAI,OAAO,CAEjC;IAED,IAAW,SAAS,IAAI,MAAM,CAE7B;IAED,IAAW,eAAe,IAAI,MAAM,EAAE,EAAE,CAEvC;IAED,IAAW,SAAS,IAAI,MAAM,GAAG,SAAS,CAEzC;IAED,IAAW,uBAAuB,IAAI,UAAU,EAAE,CAIjD;IAEM,QAAQ,CAAC,QAAQ,EAAE,MAAM,GAAG,EAAE,CAAC,QAAQ,GAAI,EAAE,CAAC,QAAQ,GAAG,iBAAiB;IAI1E,kBAAkB,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM,EAAE;IAI9C,cAAc,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM;IAIxC,aAAa,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM;IAIvC,0BAA0B,CAAC,QAAQ,EAAE,MAAM,GAAG,UAAU;IAU/D;;;;;;;;OAQG;IACI,aAAa,CAAC,SAAS,EAAE,MAAM,EAAE,cAAc,GAAE,OAAc,GAAG,EAAE,CAAC,QAAQ;CAmCrF"}
\ No newline at end of file
+{"version":3,"file":"NetInput.d.ts","sourceRoot":"","sources":["../../../src/dom/NetInput.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,UAAU,EAAE,MAAM,uBAAuB,CAAC;AAMnD,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE5C,qBAAa,QAAQ;IACnB,OAAO,CAAC,aAAa,CAAuC;IAC5D,OAAO,CAAC,SAAS,CAA0B;IAC3C,OAAO,CAAC,UAAU,CAAQ;IAC1B,OAAO,CAAC,kBAAkB,CAAiB;IAE3C,OAAO,CAAC,gBAAgB,CAAiB;IACzC,OAAO,CAAC,UAAU,CAAQ;gBAGxB,MAAM,EAAE,KAAK,CAAC,iBAAiB,CAAC,EAChC,iBAAiB,GAAE,OAAe;IAkCpC,IAAW,YAAY,IAAI,KAAK,CAAC,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAE1D;IAED,IAAW,QAAQ,IAAI,iBAAiB,EAAE,CAEzC;IAED,IAAW,YAAY,IAAI,OAAO,CAEjC;IAED,IAAW,SAAS,IAAI,MAAM,CAE7B;IAED,IAAW,eAAe,IAAI,MAAM,EAAE,EAAE,CAEvC;IAED,IAAW,SAAS,IAAI,MAAM,GAAG,SAAS,CAEzC;IAED,IAAW,uBAAuB,IAAI,UAAU,EAAE,CAIjD;IAEM,QAAQ,CAAC,QAAQ,EAAE,MAAM,GAAG,EAAE,CAAC,QAAQ,GAAI,EAAE,CAAC,QAAQ,GAAG,iBAAiB;IAI1E,kBAAkB,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM,EAAE;IAI9C,cAAc,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM;IAIxC,aAAa,CAAC,QAAQ,EAAE,MAAM,GAAG,MAAM;IAIvC,0BAA0B,CAAC,QAAQ,EAAE,MAAM,GAAG,UAAU;IAU/D;;;;;;;;OAQG;IACI,aAAa,CAAC,SAAS,EAAE,MAAM,EAAE,cAAc,GAAE,OAAc,GAAG,EAAE,CAAC,QAAQ;CAmCrF"}
\ No newline at end of file
diff --git a/build/src/dom/NetInput.js b/build/src/dom/NetInput.js
index 1f913b0..d946fd2 100644
--- a/build/src/dom/NetInput.js
+++ b/build/src/dom/NetInput.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { env } from '../env';
 import { padToSquare } from '../ops/padToSquare';
 import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
diff --git a/build/src/dom/NetInput.js.map b/build/src/dom/NetInput.js.map
index 289156b..b5fd6a9 100644
--- a/build/src/dom/NetInput.js.map
+++ b/build/src/dom/NetInput.js.map
@@ -1 +1 @@
-{"version":3,"file":"NetInput.js","sourceRoot":"","sources":["../../../src/dom/NetInput.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,WAAW,EAAE,MAAM,oBAAoB,CAAC;AACjD,OAAO,EAAE,yBAAyB,EAAE,UAAU,EAAE,UAAU,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACpF,OAAO,EAAE,qBAAqB,EAAE,MAAM,gBAAgB,CAAC;AACvD,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAGhD,MAAM,OAAO,QAAQ;IASnB,YACE,MAAgC,EAChC,oBAA6B,KAAK;QAV5B,kBAAa,GAAqC,EAAE,CAAA;QACpD,cAAS,GAAwB,EAAE,CAAA;QAEnC,uBAAkB,GAAY,KAAK,CAAA;QAEnC,qBAAgB,GAAe,EAAE,CAAA;QAOvC,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,EAAE;YAC1B,MAAM,IAAI,KAAK,CAAC,4HAA4H,MAAM,EAAE,CAAC,CAAA;SACtJ;QAED,IAAI,CAAC,kBAAkB,GAAG,iBAAiB,CAAA;QAC3C,IAAI,CAAC,UAAU,GAAG,MAAM,CAAC,MAAM,CAAA;QAE/B,MAAM,CAAC,OAAO,CAAC,CAAC,KAAK,EAAE,GAAG,EAAE,EAAE;YAE5B,IAAI,UAAU,CAAC,KAAK,CAAC,EAAE;gBACrB,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,GAAG,KAAK,CAAA;gBAC/B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,KAAK,CAAC,KAAK,CAAA;gBACxC,OAAM;aACP;YAED,IAAI,UAAU,CAAC,KAAK,CAAC,EAAE;gBACrB,MAAM,SAAS,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;gBAChC,IAAI,SAAS,KAAK,CAAC,EAAE;oBACnB,MAAM,IAAI,KAAK,CAAC,yCAAyC,SAAS,2CAA2C,CAAC,CAAA;iBAC/G;gBAED,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,GAAG,KAAK,CAAA;gBAC/B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;gBACjD,OAAM;aACP;YAED,MAAM,MAAM,GAAG,KAAK,YAAY,GAAG,CAAC,MAAM,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;YAC1F,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,GAAG,MAAM,CAAA;YAC5B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,CAAC,MAAM,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,EAAE,CAAC,CAAC,CAAA;QAC/D,CAAC,CAAC,CAAA;IACJ,CAAC;IAED,IAAW,YAAY;QACrB,OAAO,IAAI,CAAC,aAAa,CAAA;IAC3B,CAAC;IAED,IAAW,QAAQ;QACjB,OAAO,IAAI,CAAC,SAAS,CAAA;IACvB,CAAC;IAED,IAAW,YAAY;QACrB,OAAO,IAAI,CAAC,SAAS,GAAG,CAAC,IAAI,IAAI,CAAC,kBAAkB,CAAA;IACtD,CAAC;IAED,IAAW,SAAS;QAClB,OAAO,IAAI,CAAC,UAAU,CAAA;IACxB,CAAC;IAED,IAAW,eAAe;QACxB,OAAO,IAAI,CAAC,gBAAgB,CAAA;IAC9B,CAAC;IAED,IAAW,SAAS;QAClB,OAAO,IAAI,CAAC,UAAU,CAAA;IACxB,CAAC;IAED,IAAW,uBAAuB;QAChC,OAAO,KAAK,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CACpC,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CAAC,IAAI,CAAC,0BAA0B,CAAC,QAAQ,CAAC,CAC3D,CAAA;IACH,CAAC;IAEM,QAAQ,CAAC,QAAgB;QAC9B,OAAO,IAAI,CAAC,QAAQ,CAAC,QAAQ,CAAC,IAAI,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAA;IAC/D,CAAC;IAEM,kBAAkB,CAAC,QAAgB;QACxC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAA;IACxC,CAAC;IAEM,cAAc,CAAC,QAAgB;QACpC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;IAC3C,CAAC;IAEM,aAAa,CAAC,QAAgB;QACnC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;IAC3C,CAAC;IAEM,0BAA0B,CAAC,QAAgB;QAChD,IAAI,OAAO,IAAI,CAAC,SAAS,KAAK,QAAQ,EAAE;YACtC,MAAM,IAAI,KAAK,CAAC,uFAAuF,CAAC,CAAA;SACzG;QAED,MAAM,KAAK,GAAG,IAAI,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAA;QAC1C,MAAM,MAAM,GAAG,IAAI,CAAC,cAAc,CAAC,QAAQ,CAAC,CAAA;QAC5C,OAAO,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,IAAI,CAAC,SAAS,CAAC,CAAA;IACrE,CAAC;IAED;;;;;;;;OAQG;IACI,aAAa,CAAC,SAAiB,EAAE,iBAA0B,IAAI;QAEpE,IAAI,CAAC,UAAU,GAAG,SAAS,CAAA;QAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAElB,MAAM,YAAY,GAAG,KAAK,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAE;gBAC9D,MAAM,KAAK,GAAG,IAAI,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAA;gBAErC,IAAI,KAAK,YAAY,EAAE,CAAC,MAAM,EAAE;oBAC9B,2FAA2F;oBAC3F,IAAI,SAAS,GAAG,UAAU,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,UAAU,EAAc,CAAA;oBAC1E,2FAA2F;oBAC3F,SAAS,GAAG,WAAW,CAAC,SAAS,EAAE,cAAc,CAAC,CAAA;oBAElD,IAAI,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,SAAS,IAAI,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,SAAS,EAAE;wBACxE,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,cAAc,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC,CAAA;qBACvE;oBAED,OAAO,SAAS,CAAC,IAAI,CAAC,SAAS,EAAE,SAAS,EAAE,CAAC,CAAC,CAAA;iBAC/C;gBAED,IAAI,KAAK,YAAY,GAAG,CAAC,MAAM,EAAE,CAAC,MAAM,EAAE;oBACxC,OAAO,EAAE,CAAC,OAAO,CAAC,UAAU,CAAC,aAAa,CAAC,KAAK,EAAE,SAAS,EAAE,cAAc,CAAC,CAAC,CAAA;iBAC9E;gBAED,MAAM,IAAI,KAAK,CAAC,+BAA+B,QAAQ,6FAA6F,KAAK,EAAE,CAAC,CAAA;YAC9J,CAAC,CAAC,CAAA;YAEF,iHAAiH;YACjH,MAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,YAAY,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,CAAC,CAAC,CAAA;YAExH,OAAO,WAAW,CAAA;QACpB,CAAC,CAAC,CAAA;IACJ,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"NetInput.js","sourceRoot":"","sources":["../../../src/dom/NetInput.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,WAAW,EAAE,MAAM,oBAAoB,CAAC;AACjD,OAAO,EAAE,yBAAyB,EAAE,UAAU,EAAE,UAAU,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACpF,OAAO,EAAE,qBAAqB,EAAE,MAAM,gBAAgB,CAAC;AACvD,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAGhD,MAAM,OAAO,QAAQ;IASnB,YACE,MAAgC,EAChC,oBAA6B,KAAK;QAV5B,kBAAa,GAAqC,EAAE,CAAA;QACpD,cAAS,GAAwB,EAAE,CAAA;QAEnC,uBAAkB,GAAY,KAAK,CAAA;QAEnC,qBAAgB,GAAe,EAAE,CAAA;QAOvC,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,EAAE;YAC1B,MAAM,IAAI,KAAK,CAAC,4HAA4H,MAAM,EAAE,CAAC,CAAA;SACtJ;QAED,IAAI,CAAC,kBAAkB,GAAG,iBAAiB,CAAA;QAC3C,IAAI,CAAC,UAAU,GAAG,MAAM,CAAC,MAAM,CAAA;QAE/B,MAAM,CAAC,OAAO,CAAC,CAAC,KAAK,EAAE,GAAG,EAAE,EAAE;YAE5B,IAAI,UAAU,CAAC,KAAK,CAAC,EAAE;gBACrB,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,GAAG,KAAK,CAAA;gBAC/B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,KAAK,CAAC,KAAK,CAAA;gBACxC,OAAM;aACP;YAED,IAAI,UAAU,CAAC,KAAK,CAAC,EAAE;gBACrB,MAAM,SAAS,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;gBAChC,IAAI,SAAS,KAAK,CAAC,EAAE;oBACnB,MAAM,IAAI,KAAK,CAAC,yCAAyC,SAAS,2CAA2C,CAAC,CAAA;iBAC/G;gBAED,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,GAAG,KAAK,CAAA;gBAC/B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;gBACjD,OAAM;aACP;YAED,MAAM,MAAM,GAAG,KAAK,YAAY,GAAG,CAAC,MAAM,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;YAC1F,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,GAAG,MAAM,CAAA;YAC5B,IAAI,CAAC,gBAAgB,CAAC,GAAG,CAAC,GAAG,CAAC,MAAM,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,EAAE,CAAC,CAAC,CAAA;QAC/D,CAAC,CAAC,CAAA;IACJ,CAAC;IAED,IAAW,YAAY;QACrB,OAAO,IAAI,CAAC,aAAa,CAAA;IAC3B,CAAC;IAED,IAAW,QAAQ;QACjB,OAAO,IAAI,CAAC,SAAS,CAAA;IACvB,CAAC;IAED,IAAW,YAAY;QACrB,OAAO,IAAI,CAAC,SAAS,GAAG,CAAC,IAAI,IAAI,CAAC,kBAAkB,CAAA;IACtD,CAAC;IAED,IAAW,SAAS;QAClB,OAAO,IAAI,CAAC,UAAU,CAAA;IACxB,CAAC;IAED,IAAW,eAAe;QACxB,OAAO,IAAI,CAAC,gBAAgB,CAAA;IAC9B,CAAC;IAED,IAAW,SAAS;QAClB,OAAO,IAAI,CAAC,UAAU,CAAA;IACxB,CAAC;IAED,IAAW,uBAAuB;QAChC,OAAO,KAAK,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CACpC,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CAAC,IAAI,CAAC,0BAA0B,CAAC,QAAQ,CAAC,CAC3D,CAAA;IACH,CAAC;IAEM,QAAQ,CAAC,QAAgB;QAC9B,OAAO,IAAI,CAAC,QAAQ,CAAC,QAAQ,CAAC,IAAI,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAA;IAC/D,CAAC;IAEM,kBAAkB,CAAC,QAAgB;QACxC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAA;IACxC,CAAC;IAEM,cAAc,CAAC,QAAgB;QACpC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;IAC3C,CAAC;IAEM,aAAa,CAAC,QAAgB;QACnC,OAAO,IAAI,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;IAC3C,CAAC;IAEM,0BAA0B,CAAC,QAAgB;QAChD,IAAI,OAAO,IAAI,CAAC,SAAS,KAAK,QAAQ,EAAE;YACtC,MAAM,IAAI,KAAK,CAAC,uFAAuF,CAAC,CAAA;SACzG;QAED,MAAM,KAAK,GAAG,IAAI,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAA;QAC1C,MAAM,MAAM,GAAG,IAAI,CAAC,cAAc,CAAC,QAAQ,CAAC,CAAA;QAC5C,OAAO,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,IAAI,CAAC,SAAS,CAAC,CAAA;IACrE,CAAC;IAED;;;;;;;;OAQG;IACI,aAAa,CAAC,SAAiB,EAAE,iBAA0B,IAAI;QAEpE,IAAI,CAAC,UAAU,GAAG,SAAS,CAAA;QAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAElB,MAAM,YAAY,GAAG,KAAK,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAE;gBAC9D,MAAM,KAAK,GAAG,IAAI,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAA;gBAErC,IAAI,KAAK,YAAY,EAAE,CAAC,MAAM,EAAE;oBAC9B,2FAA2F;oBAC3F,IAAI,SAAS,GAAG,UAAU,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,UAAU,EAAc,CAAA;oBAC1E,2FAA2F;oBAC3F,SAAS,GAAG,WAAW,CAAC,SAAS,EAAE,cAAc,CAAC,CAAA;oBAElD,IAAI,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,SAAS,IAAI,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,SAAS,EAAE;wBACxE,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,cAAc,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC,CAAA;qBACvE;oBAED,OAAO,SAAS,CAAC,IAAI,CAAC,SAAS,EAAE,SAAS,EAAE,CAAC,CAAC,CAAA;iBAC/C;gBAED,IAAI,KAAK,YAAY,GAAG,CAAC,MAAM,EAAE,CAAC,MAAM,EAAE;oBACxC,OAAO,EAAE,CAAC,OAAO,CAAC,UAAU,CAAC,aAAa,CAAC,KAAK,EAAE,SAAS,EAAE,cAAc,CAAC,CAAC,CAAA;iBAC9E;gBAED,MAAM,IAAI,KAAK,CAAC,+BAA+B,QAAQ,6FAA6F,KAAK,EAAE,CAAC,CAAA;YAC9J,CAAC,CAAC,CAAA;YAEF,iHAAiH;YACjH,MAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,YAAY,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,CAAC,CAAC,CAAA;YAExH,OAAO,WAAW,CAAA;QACpB,CAAC,CAAC,CAAA;IACJ,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/dom/extractFaceTensors.d.ts b/build/src/dom/extractFaceTensors.d.ts
index 517d7f0..460db84 100644
--- a/build/src/dom/extractFaceTensors.d.ts
+++ b/build/src/dom/extractFaceTensors.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
 /**
diff --git a/build/src/dom/extractFaceTensors.d.ts.map b/build/src/dom/extractFaceTensors.d.ts.map
index bfca74c..3775f27 100644
--- a/build/src/dom/extractFaceTensors.d.ts.map
+++ b/build/src/dom/extractFaceTensors.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractFaceTensors.d.ts","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,IAAI,EAAE,MAAM,YAAY,CAAC;AAClC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAGzD;;;;;;;;;GASG;AACH,wBAAsB,kBAAkB,CACtC,WAAW,EAAE,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,EACtC,UAAU,EAAE,KAAK,CAAC,aAAa,GAAG,IAAI,CAAC,GACtC,OAAO,CAAC,EAAE,CAAC,QAAQ,EAAE,CAAC,CA0BxB"}
\ No newline at end of file
+{"version":3,"file":"extractFaceTensors.d.ts","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,IAAI,EAAE,MAAM,YAAY,CAAC;AAClC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAGzD;;;;;;;;;GASG;AACH,wBAAsB,kBAAkB,CACtC,WAAW,EAAE,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,EACtC,UAAU,EAAE,KAAK,CAAC,aAAa,GAAG,IAAI,CAAC,GACtC,OAAO,CAAC,EAAE,CAAC,QAAQ,EAAE,CAAC,CA0BxB"}
\ No newline at end of file
diff --git a/build/src/dom/extractFaceTensors.js b/build/src/dom/extractFaceTensors.js
index 61e50e5..87b90ad 100644
--- a/build/src/dom/extractFaceTensors.js
+++ b/build/src/dom/extractFaceTensors.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FaceDetection } from '../classes/FaceDetection';
 import { isTensor3D, isTensor4D } from '../utils';
 /**
diff --git a/build/src/dom/extractFaceTensors.js.map b/build/src/dom/extractFaceTensors.js.map
index 0b6e025..1b41f98 100644
--- a/build/src/dom/extractFaceTensors.js.map
+++ b/build/src/dom/extractFaceTensors.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,UAAU,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAElD;;;;;;;;;GASG;AACH,MAAM,CAAC,KAAK,UAAU,kBAAkB,CACtC,WAAsC,EACtC,UAAuC;IAGvC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,EAAE;QACxD,MAAM,IAAI,KAAK,CAAC,2DAA2D,CAAC,CAAA;KAC7E;IAED,IAAI,UAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;QACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;KACpE;IAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,GAAG,WAAW,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAEnG,MAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,aAAa;YACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,GAAG;YACtC,CAAC,CAAC,GAAG,CACR;aACE,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,CAAA;QAE1D,MAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE,CACxD,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,CACxG,CAAA;QAED,OAAO,WAAW,CAAA;IACpB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,UAAU,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAElD;;;;;;;;;GASG;AACH,MAAM,CAAC,KAAK,UAAU,kBAAkB,CACtC,WAAsC,EACtC,UAAuC;IAGvC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,EAAE;QACxD,MAAM,IAAI,KAAK,CAAC,2DAA2D,CAAC,CAAA;KAC7E;IAED,IAAI,UAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;QACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;KACpE;IAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,GAAG,WAAW,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAEnG,MAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,aAAa;YACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,GAAG;YACtC,CAAC,CAAC,GAAG,CACR;aACE,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,CAAA;QAE1D,MAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE,CACxD,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,CACxG,CAAA;QAED,OAAO,WAAW,CAAA;IACpB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/imageTensorToCanvas.d.ts b/build/src/dom/imageTensorToCanvas.d.ts
index 6ba317d..7f520e7 100644
--- a/build/src/dom/imageTensorToCanvas.d.ts
+++ b/build/src/dom/imageTensorToCanvas.d.ts
@@ -1,3 +1,3 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare function imageTensorToCanvas(imgTensor: tf.Tensor, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
 //# sourceMappingURL=imageTensorToCanvas.d.ts.map
\ No newline at end of file
diff --git a/build/src/dom/imageTensorToCanvas.d.ts.map b/build/src/dom/imageTensorToCanvas.d.ts.map
index 2d31d3f..01273c0 100644
--- a/build/src/dom/imageTensorToCanvas.d.ts.map
+++ b/build/src/dom/imageTensorToCanvas.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"imageTensorToCanvas.d.ts","sourceRoot":"","sources":["../../../src/dom/imageTensorToCanvas.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAK5C,wBAAsB,mBAAmB,CACvC,SAAS,EAAE,EAAE,CAAC,MAAM,EACpB,MAAM,CAAC,EAAE,iBAAiB,GACzB,OAAO,CAAC,iBAAiB,CAAC,CAW5B"}
\ No newline at end of file
+{"version":3,"file":"imageTensorToCanvas.d.ts","sourceRoot":"","sources":["../../../src/dom/imageTensorToCanvas.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAKvC,wBAAsB,mBAAmB,CACvC,SAAS,EAAE,EAAE,CAAC,MAAM,EACpB,MAAM,CAAC,EAAE,iBAAiB,GACzB,OAAO,CAAC,iBAAiB,CAAC,CAW5B"}
\ No newline at end of file
diff --git a/build/src/dom/imageTensorToCanvas.js b/build/src/dom/imageTensorToCanvas.js
index 9725276..c8b7bfd 100644
--- a/build/src/dom/imageTensorToCanvas.js
+++ b/build/src/dom/imageTensorToCanvas.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { env } from '../env';
 import { isTensor4D } from '../utils';
 export async function imageTensorToCanvas(imgTensor, canvas) {
diff --git a/build/src/dom/imageTensorToCanvas.js.map b/build/src/dom/imageTensorToCanvas.js.map
index d054882..c144610 100644
--- a/build/src/dom/imageTensorToCanvas.js.map
+++ b/build/src/dom/imageTensorToCanvas.js.map
@@ -1 +1 @@
-{"version":3,"file":"imageTensorToCanvas.js","sourceRoot":"","sources":["../../../src/dom/imageTensorToCanvas.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAEtC,MAAM,CAAC,KAAK,UAAU,mBAAmB,CACvC,SAAoB,EACpB,MAA0B;IAG1B,MAAM,YAAY,GAAG,MAAM,IAAI,GAAG,CAAC,MAAM,EAAE,CAAC,mBAAmB,EAAE,CAAA;IAEjE,MAAM,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACzF,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,KAAK,EAAE,CAAC,CAAA;IACrF,MAAM,EAAE,CAAC,OAAO,CAAC,QAAQ,CAAC,WAAW,EAAE,YAAY,CAAC,CAAA;IAEpD,WAAW,CAAC,OAAO,EAAE,CAAA;IAErB,OAAO,YAAY,CAAA;AACrB,CAAC"}
\ No newline at end of file
+{"version":3,"file":"imageTensorToCanvas.js","sourceRoot":"","sources":["../../../src/dom/imageTensorToCanvas.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAEtC,MAAM,CAAC,KAAK,UAAU,mBAAmB,CACvC,SAAoB,EACpB,MAA0B;IAG1B,MAAM,YAAY,GAAG,MAAM,IAAI,GAAG,CAAC,MAAM,EAAE,CAAC,mBAAmB,EAAE,CAAA;IAEjE,MAAM,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACzF,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,KAAK,EAAE,CAAC,CAAA;IACrF,MAAM,EAAE,CAAC,OAAO,CAAC,QAAQ,CAAC,WAAW,EAAE,YAAY,CAAC,CAAA;IAEpD,WAAW,CAAC,OAAO,EAAE,CAAA;IAErB,OAAO,YAAY,CAAA;AACrB,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/loadWeightMap.d.ts b/build/src/dom/loadWeightMap.d.ts
index 7017212..50aad16 100644
--- a/build/src/dom/loadWeightMap.d.ts
+++ b/build/src/dom/loadWeightMap.d.ts
@@ -1,3 +1,3 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<tf.NamedTensorMap>;
 //# sourceMappingURL=loadWeightMap.d.ts.map
\ No newline at end of file
diff --git a/build/src/dom/loadWeightMap.d.ts.map b/build/src/dom/loadWeightMap.d.ts.map
index 6889e35..fa58400 100644
--- a/build/src/dom/loadWeightMap.d.ts.map
+++ b/build/src/dom/loadWeightMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"loadWeightMap.d.ts","sourceRoot":"","sources":["../../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAK5C,wBAAsB,aAAa,CACjC,GAAG,EAAE,MAAM,GAAG,SAAS,EACvB,gBAAgB,EAAE,MAAM,GACvB,OAAO,CAAC,EAAE,CAAC,cAAc,CAAC,CAK5B"}
\ No newline at end of file
+{"version":3,"file":"loadWeightMap.d.ts","sourceRoot":"","sources":["../../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAKvC,wBAAsB,aAAa,CACjC,GAAG,EAAE,MAAM,GAAG,SAAS,EACvB,gBAAgB,EAAE,MAAM,GACvB,OAAO,CAAC,EAAE,CAAC,cAAc,CAAC,CAK5B"}
\ No newline at end of file
diff --git a/build/src/dom/loadWeightMap.js b/build/src/dom/loadWeightMap.js
index 25cfa2b..0177c4b 100644
--- a/build/src/dom/loadWeightMap.js
+++ b/build/src/dom/loadWeightMap.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { getModelUris } from '../common/getModelUris';
 import { fetchJson } from './fetchJson';
 export async function loadWeightMap(uri, defaultModelName) {
diff --git a/build/src/dom/loadWeightMap.js.map b/build/src/dom/loadWeightMap.js.map
index 509f9a1..6d574f5 100644
--- a/build/src/dom/loadWeightMap.js.map
+++ b/build/src/dom/loadWeightMap.js.map
@@ -1 +1 @@
-{"version":3,"file":"loadWeightMap.js","sourceRoot":"","sources":["../../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,YAAY,EAAE,MAAM,wBAAwB,CAAC;AACtD,OAAO,EAAE,SAAS,EAAE,MAAM,aAAa,CAAC;AAExC,MAAM,CAAC,KAAK,UAAU,aAAa,CACjC,GAAuB,EACvB,gBAAwB;IAExB,MAAM,EAAE,WAAW,EAAE,YAAY,EAAE,GAAG,YAAY,CAAC,GAAG,EAAE,gBAAgB,CAAC,CAAA;IACzE,IAAI,QAAQ,GAAG,MAAM,SAAS,CAA8B,WAAW,CAAC,CAAA;IACxE,2EAA2E;IAC3E,OAAO,EAAE,CAAC,EAAE,CAAC,WAAW,CAAC,QAAQ,EAAE,YAAY,CAAC,CAAA;AAClD,CAAC"}
\ No newline at end of file
+{"version":3,"file":"loadWeightMap.js","sourceRoot":"","sources":["../../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,YAAY,EAAE,MAAM,wBAAwB,CAAC;AACtD,OAAO,EAAE,SAAS,EAAE,MAAM,aAAa,CAAC;AAExC,MAAM,CAAC,KAAK,UAAU,aAAa,CACjC,GAAuB,EACvB,gBAAwB;IAExB,MAAM,EAAE,WAAW,EAAE,YAAY,EAAE,GAAG,YAAY,CAAC,GAAG,EAAE,gBAAgB,CAAC,CAAA;IACzE,IAAI,QAAQ,GAAG,MAAM,SAAS,CAA8B,WAAW,CAAC,CAAA;IACxE,2EAA2E;IAC3E,OAAO,EAAE,CAAC,EAAE,CAAC,WAAW,CAAC,QAAQ,EAAE,YAAY,CAAC,CAAA;AAClD,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/types.d.ts b/build/src/dom/types.d.ts
index 06d1349..894ef52 100644
--- a/build/src/dom/types.d.ts
+++ b/build/src/dom/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput } from './NetInput';
 export declare type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement;
 export declare type TResolvedNetInput = TMediaElement | tf.Tensor3D | tf.Tensor4D;
diff --git a/build/src/dom/types.d.ts.map b/build/src/dom/types.d.ts.map
index fcc20dc..2450063 100644
--- a/build/src/dom/types.d.ts.map
+++ b/build/src/dom/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/dom/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AAEtC,oBAAY,aAAa,GAAG,gBAAgB,GAAG,gBAAgB,GAAG,iBAAiB,CAAA;AAEnF,oBAAY,iBAAiB,GAAG,aAAa,GAAG,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA;AAEzE,oBAAY,YAAY,GAAG,MAAM,GAAG,iBAAiB,CAAA;AAErD,oBAAY,SAAS,GAAG,YAAY,GAAG,KAAK,CAAC,YAAY,CAAC,GAAG,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/dom/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AAEtC,oBAAY,aAAa,GAAG,gBAAgB,GAAG,gBAAgB,GAAG,iBAAiB,CAAA;AAEnF,oBAAY,iBAAiB,GAAG,aAAa,GAAG,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA;AAEzE,oBAAY,YAAY,GAAG,MAAM,GAAG,iBAAiB,CAAA;AAErD,oBAAY,SAAS,GAAG,YAAY,GAAG,KAAK,CAAC,YAAY,CAAC,GAAG,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA"}
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.d.ts b/build/src/faceExpressionNet/FaceExpressionNet.d.ts
index 5e485dd..4b6efe1 100644
--- a/build/src/faceExpressionNet/FaceExpressionNet.d.ts
+++ b/build/src/faceExpressionNet/FaceExpressionNet.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '../dom';
 import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
 import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.d.ts.map b/build/src/faceExpressionNet/FaceExpressionNet.d.ts.map
index d8db61c..7e878b3 100644
--- a/build/src/faceExpressionNet/FaceExpressionNet.d.ts.map
+++ b/build/src/faceExpressionNet/FaceExpressionNet.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"FaceExpressionNet.d.ts","sourceRoot":"","sources":["../../../src/faceExpressionNet/FaceExpressionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,oBAAoB,EAAE,MAAM,8CAA8C,CAAC;AACpF,OAAO,EAAE,0BAA0B,EAAE,MAAM,+BAA+B,CAAC;AAC3E,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AAEpD,qBAAa,iBAAkB,SAAQ,aAAa,CAAC,0BAA0B,CAAC;gBAElE,oBAAoB,GAAE,oBAAiD;IAI5E,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAIlD,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,kBAAkB,CAAC,KAAK,EAAE,SAAS;IAkBhD,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,uBAAuB,IAAI,MAAM;IAI3C,SAAS,CAAC,wBAAwB,IAAI,MAAM;CAG7C"}
\ No newline at end of file
+{"version":3,"file":"FaceExpressionNet.d.ts","sourceRoot":"","sources":["../../../src/faceExpressionNet/FaceExpressionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,oBAAoB,EAAE,MAAM,8CAA8C,CAAC;AACpF,OAAO,EAAE,0BAA0B,EAAE,MAAM,+BAA+B,CAAC;AAC3E,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AAEpD,qBAAa,iBAAkB,SAAQ,aAAa,CAAC,0BAA0B,CAAC;gBAElE,oBAAoB,GAAE,oBAAiD;IAI5E,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAIlD,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,kBAAkB,CAAC,KAAK,EAAE,SAAS;IAkBhD,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,uBAAuB,IAAI,MAAM;IAI3C,SAAS,CAAC,wBAAwB,IAAI,MAAM;CAG7C"}
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.js b/build/src/faceExpressionNet/FaceExpressionNet.js
index afbfe8f..299bcbd 100644
--- a/build/src/faceExpressionNet/FaceExpressionNet.js
+++ b/build/src/faceExpressionNet/FaceExpressionNet.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { toNetInput } from '../dom';
 import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
 import { FaceProcessor } from '../faceProcessor/FaceProcessor';
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.js.map b/build/src/faceExpressionNet/FaceExpressionNet.js.map
index 346f830..883bf14 100644
--- a/build/src/faceExpressionNet/FaceExpressionNet.js.map
+++ b/build/src/faceExpressionNet/FaceExpressionNet.js.map
@@ -1 +1 @@
-{"version":3,"file":"FaceExpressionNet.js","sourceRoot":"","sources":["../../../src/faceExpressionNet/FaceExpressionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,oBAAoB,EAAE,MAAM,8CAA8C,CAAC;AAEpF,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AAEpD,MAAM,OAAO,iBAAkB,SAAQ,aAAyC;IAE9E,YAAY,uBAA6C,IAAI,oBAAoB,EAAE;QACjF,KAAK,CAAC,mBAAmB,EAAE,oBAAoB,CAAC,CAAA;IAClD,CAAC;IAEM,YAAY,CAAC,KAA6B;QAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IACtD,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,kBAAkB,CAAC,KAAgB;QAC9C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,GAAG,GAAG,MAAM,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAA;QAC7C,MAAM,mBAAmB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,KAAK,EAAC,CAAC,EAAC,EAAE;YAC1E,MAAM,IAAI,GAAG,MAAM,CAAC,CAAC,IAAI,EAAE,CAAA;YAC3B,CAAC,CAAC,OAAO,EAAE,CAAA;YACX,OAAO,IAAI,CAAA;QACb,CAAC,CAAC,CAAC,CAAA;QACH,GAAG,CAAC,OAAO,EAAE,CAAA;QAEb,MAAM,kBAAkB,GAAG,mBAAmB;aAC3C,GAAG,CAAC,YAAY,CAAC,EAAE,CAAC,IAAI,eAAe,CAAC,YAA4B,CAAC,CAAC,CAAA;QAEzE,OAAO,QAAQ,CAAC,YAAY;YAC1B,CAAC,CAAC,kBAAkB;YACpB,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAA;IAC3B,CAAC;IAES,mBAAmB;QAC3B,OAAO,uBAAuB,CAAA;IAChC,CAAC;IAES,uBAAuB;QAC/B,OAAO,GAAG,CAAA;IACZ,CAAC;IAES,wBAAwB;QAChC,OAAO,CAAC,CAAA;IACV,CAAC;CACF"}
\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts b/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts
index 5f3c371..51a9946 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts.map b/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts.map
index 210ea45..e60a8de 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts.map
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"FaceFeatureExtractor.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAE,0BAA0B,EAAE,qBAAqB,EAAE,MAAM,SAAS,CAAC;AAE5E,qBAAa,oBAAqB,SAAQ,aAAa,CAAC,0BAA0B,CAAE,YAAW,qBAAqB,CAAC,0BAA0B,CAAC;;IAMvI,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAuBpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
+{"version":3,"file":"FaceFeatureExtractor.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAE,0BAA0B,EAAE,qBAAqB,EAAE,MAAM,SAAS,CAAC;AAE5E,qBAAa,oBAAqB,SAAQ,aAAa,CAAC,0BAA0B,CAAE,YAAW,qBAAqB,CAAC,0BAA0B,CAAC;;IAMvI,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAuBpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
index fa0b16d..42eb883 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { normalize } from '../ops';
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
index 3bad405..46db88e 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
@@ -1 +1 @@
-{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts
index 6e197cd..97343c4 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts.map b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts.map
index dee3985..fdac899 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts.map
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"TinyFaceFeatureExtractor.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAE,qBAAqB,EAAE,8BAA8B,EAAE,MAAM,SAAS,CAAC;AAEhF,qBAAa,wBAAyB,SAAQ,aAAa,CAAC,8BAA8B,CAAE,YAAW,qBAAqB,CAAC,8BAA8B,CAAC;;IAMnJ,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAsBpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
+{"version":3,"file":"TinyFaceFeatureExtractor.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAE,qBAAqB,EAAE,8BAA8B,EAAE,MAAM,SAAS,CAAC;AAEhF,qBAAa,wBAAyB,SAAQ,aAAa,CAAC,8BAA8B,CAAE,YAAW,qBAAqB,CAAC,8BAA8B,CAAC;;IAMnJ,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAsBpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
index ef167e2..ddd86d2 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { normalize } from '../ops';
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
index 1790610..f9ed712 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
@@ -1 +1 @@
-{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/denseBlock.d.ts b/build/src/faceFeatureExtractor/denseBlock.d.ts
index c4db76b..d214421 100644
--- a/build/src/faceFeatureExtractor/denseBlock.d.ts
+++ b/build/src/faceFeatureExtractor/denseBlock.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { DenseBlock3Params, DenseBlock4Params } from './types';
 export declare function denseBlock3(x: tf.Tensor4D, denseBlockParams: DenseBlock3Params, isFirstLayer?: boolean): tf.Tensor4D;
 export declare function denseBlock4(x: tf.Tensor4D, denseBlockParams: DenseBlock4Params, isFirstLayer?: boolean, isScaleDown?: boolean): tf.Tensor4D;
diff --git a/build/src/faceFeatureExtractor/denseBlock.d.ts.map b/build/src/faceFeatureExtractor/denseBlock.d.ts.map
index f46c2c5..e49ae6a 100644
--- a/build/src/faceFeatureExtractor/denseBlock.d.ts.map
+++ b/build/src/faceFeatureExtractor/denseBlock.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"denseBlock.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/denseBlock.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,OAAO,EAAE,iBAAiB,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE/D,wBAAgB,WAAW,CACzB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,gBAAgB,EAAE,iBAAiB,EACnC,YAAY,GAAE,OAAe,GAC5B,EAAE,CAAC,QAAQ,CAiBb;AAED,wBAAgB,WAAW,CACzB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,gBAAgB,EAAE,iBAAiB,EACnC,YAAY,GAAE,OAAe,EAC7B,WAAW,GAAE,OAAc,GAC1B,EAAE,CAAC,QAAQ,CAoBb"}
\ No newline at end of file
+{"version":3,"file":"denseBlock.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/denseBlock.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,OAAO,EAAE,iBAAiB,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE/D,wBAAgB,WAAW,CACzB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,gBAAgB,EAAE,iBAAiB,EACnC,YAAY,GAAE,OAAe,GAC5B,EAAE,CAAC,QAAQ,CAiBb;AAED,wBAAgB,WAAW,CACzB,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,gBAAgB,EAAE,iBAAiB,EACnC,YAAY,GAAE,OAAe,EAC7B,WAAW,GAAE,OAAc,GAC1B,EAAE,CAAC,QAAQ,CAoBb"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/denseBlock.js b/build/src/faceFeatureExtractor/denseBlock.js
index ec9e68a..700c01e 100644
--- a/build/src/faceFeatureExtractor/denseBlock.js
+++ b/build/src/faceFeatureExtractor/denseBlock.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';
 export function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
     return tf.tidy(() => {
diff --git a/build/src/faceFeatureExtractor/denseBlock.js.map b/build/src/faceFeatureExtractor/denseBlock.js.map
index 69ef1ac..d069c8a 100644
--- a/build/src/faceFeatureExtractor/denseBlock.js.map
+++ b/build/src/faceFeatureExtractor/denseBlock.js.map
@@ -1 +1 @@
-{"version":3,"file":"denseBlock.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/denseBlock.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,sBAAsB,EAAE,MAAM,kCAAkC,CAAC;AAG1E,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EAC5E,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CACtE,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK,EAC7B,cAAuB,IAAI;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EACnG,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAC7F,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;QACpE,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC,CAAgB,CAAA;IAC/E,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"denseBlock.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/denseBlock.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,sBAAsB,EAAE,MAAM,kCAAkC,CAAC;AAG1E,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EAC5E,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CACtE,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK,EAC7B,cAAuB,IAAI;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EACnG,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAC7F,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;QACpE,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC,CAAgB,CAAA;IAC/E,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts
index 5a0d1f9..32eecf5 100644
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts
+++ b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { FaceFeatureExtractorParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts.map b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts.map
index 18c084c..3916e34 100644
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAA8B,YAAY,EAAE,MAAM,WAAW,CAAC;AAErE,OAAO,EAAE,0BAA0B,EAAE,MAAM,SAAS,CAAC;AAErD,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,0BAA0B,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAkBvE"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAA8B,YAAY,EAAE,MAAM,WAAW,CAAC;AAErE,OAAO,EAAE,0BAA0B,EAAE,MAAM,SAAS,CAAC;AAErD,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,0BAA0B,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAkBvE"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts
index 2c98e51..4b5d36e 100644
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts
+++ b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { TinyFaceFeatureExtractorParams } from './types';
 export declare function extractParamsFromWeigthMapTiny(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts.map b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts.map
index 81d5ff8..080a191 100644
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts.map
+++ b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMapTiny.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAA8B,YAAY,EAAE,MAAM,WAAW,CAAC;AAErE,OAAO,EAAE,8BAA8B,EAAE,MAAM,SAAS,CAAC;AAEzD,wBAAgB,8BAA8B,CAC5C,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,8BAA8B,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAiB3E"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMapTiny.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAA8B,YAAY,EAAE,MAAM,WAAW,CAAC;AAErE,OAAO,EAAE,8BAA8B,EAAE,MAAM,SAAS,CAAC;AAEzD,wBAAgB,8BAA8B,CAC5C,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,8BAA8B,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAiB3E"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/types.d.ts b/build/src/faceFeatureExtractor/types.d.ts
index bcd9b64..82c9128 100644
--- a/build/src/faceFeatureExtractor/types.d.ts
+++ b/build/src/faceFeatureExtractor/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '..';
 import { ConvParams, SeparableConvParams } from '../common';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/faceFeatureExtractor/types.d.ts.map b/build/src/faceFeatureExtractor/types.d.ts.map
index c7c1bf8..e72bb96 100644
--- a/build/src/faceFeatureExtractor/types.d.ts.map
+++ b/build/src/faceFeatureExtractor/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAE,MAAM,IAAI,CAAC;AACzC,OAAO,EAAE,UAAU,EAAE,mBAAmB,EAAE,MAAM,WAAW,CAAC;AAC5D,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAEjD,oBAAY,uBAAuB,GAAG,eAAe,GAAG;IACtD,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AAED,oBAAY,eAAe,GAAG;IAC5B,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;IACjB,QAAQ,EAAE,EAAE,CAAC,QAAQ,CAAA;IACrB,KAAK,EAAE,EAAE,CAAC,QAAQ,CAAA;IAClB,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AAED,oBAAY,gCAAgC,GAAG;IAC7C,SAAS,EAAE,uBAAuB,CAAA;IAClC,SAAS,EAAE,uBAAuB,CAAA;CACnC,CAAA;AAED,oBAAY,iBAAiB,GAAG;IAC9B,KAAK,EAAE,mBAAmB,GAAG,UAAU,CAAA;IACvC,KAAK,EAAE,mBAAmB,CAAA;IAC1B,KAAK,EAAE,mBAAmB,CAAA;CAC3B,CAAA;AAED,oBAAY,iBAAiB,GAAG,iBAAiB,GAAG;IAClD,KAAK,EAAE,mBAAmB,CAAA;CAC3B,CAAA;AAED,oBAAY,8BAA8B,GAAG;IAC3C,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;CAC1B,CAAA;AAED,oBAAY,0BAA0B,GAAG;IACvC,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;CAC1B,CAAA;AAED,MAAM,WAAW,qBAAqB,CAAC,UAAU,SAAS,8BAA8B,GAAG,0BAA0B,CAAE,SAAQ,aAAa,CAAC,UAAU,CAAC;IACtJ,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA;IAC1C,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAA;CAChD"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAE,MAAM,IAAI,CAAC;AACzC,OAAO,EAAE,UAAU,EAAE,mBAAmB,EAAE,MAAM,WAAW,CAAC;AAC5D,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAEjD,oBAAY,uBAAuB,GAAG,eAAe,GAAG;IACtD,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AAED,oBAAY,eAAe,GAAG;IAC5B,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAA;IACjB,QAAQ,EAAE,EAAE,CAAC,QAAQ,CAAA;IACrB,KAAK,EAAE,EAAE,CAAC,QAAQ,CAAA;IAClB,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AAED,oBAAY,gCAAgC,GAAG;IAC7C,SAAS,EAAE,uBAAuB,CAAA;IAClC,SAAS,EAAE,uBAAuB,CAAA;CACnC,CAAA;AAED,oBAAY,iBAAiB,GAAG;IAC9B,KAAK,EAAE,mBAAmB,GAAG,UAAU,CAAA;IACvC,KAAK,EAAE,mBAAmB,CAAA;IAC1B,KAAK,EAAE,mBAAmB,CAAA;CAC3B,CAAA;AAED,oBAAY,iBAAiB,GAAG,iBAAiB,GAAG;IAClD,KAAK,EAAE,mBAAmB,CAAA;CAC3B,CAAA;AAED,oBAAY,8BAA8B,GAAG;IAC3C,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;CAC1B,CAAA;AAED,oBAAY,0BAA0B,GAAG;IACvC,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;IACzB,MAAM,EAAE,iBAAiB,CAAA;CAC1B,CAAA;AAED,MAAM,WAAW,qBAAqB,CAAC,UAAU,SAAS,8BAA8B,GAAG,0BAA0B,CAAE,SAAQ,aAAa,CAAC,UAAU,CAAC;IACtJ,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAA;IAC1C,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAA;CAChD"}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts b/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts
index 5f0ccee..d21be90 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { IDimensions } from '../classes';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { NetInput, TNetInput } from '../dom';
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts.map b/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts.map
index b55e949..223477e 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts.map
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"FaceLandmark68NetBase.d.ts","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,WAAW,EAAS,MAAM,YAAY,CAAC;AAChD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,0BAA0B,EAAE,8BAA8B,EAAE,MAAM,+BAA+B,CAAC;AAC3G,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAG/D,8BAAsB,qBAAqB,CACzC,gBAAgB,SAAS,0BAA0B,GAAG,8BAA8B,CAEpF,SAAQ,aAAa,CAAC,gBAAgB,CAAC;IAEhC,WAAW,CAAC,MAAM,EAAE,EAAE,CAAC,QAAQ,EAAE,SAAS,EAAE,MAAM,EAAE,kBAAkB,EAAE,WAAW,EAAE,GAAG,EAAE,CAAC,QAAQ;IA6CnG,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAWpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,eAAe,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,eAAe,GAAG,eAAe,EAAE,CAAC;IA6B5F,SAAS,CAAC,wBAAwB,IAAI,MAAM;CAG7C"}
\ No newline at end of file
+{"version":3,"file":"FaceLandmark68NetBase.d.ts","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,WAAW,EAAS,MAAM,YAAY,CAAC;AAChD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,0BAA0B,EAAE,8BAA8B,EAAE,MAAM,+BAA+B,CAAC;AAC3G,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAG/D,8BAAsB,qBAAqB,CACzC,gBAAgB,SAAS,0BAA0B,GAAG,8BAA8B,CAEpF,SAAQ,aAAa,CAAC,gBAAgB,CAAC;IAEhC,WAAW,CAAC,MAAM,EAAE,EAAE,CAAC,QAAQ,EAAE,SAAS,EAAE,MAAM,EAAE,kBAAkB,EAAE,WAAW,EAAE,GAAG,EAAE,CAAC,QAAQ;IA6CnG,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAWpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,eAAe,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,eAAe,GAAG,eAAe,EAAE,CAAC;IA6B5F,SAAS,CAAC,wBAAwB,IAAI,MAAM;CAG7C"}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
index 5801a8e..c6cc20f 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Point } from '../classes';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { toNetInput } from '../dom';
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
index 74fe78a..0b2b4a3 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/faceProcessor/FaceProcessor.d.ts b/build/src/faceProcessor/FaceProcessor.d.ts
index 40bacdc..5201f07 100644
--- a/build/src/faceProcessor/FaceProcessor.d.ts
+++ b/build/src/faceProcessor/FaceProcessor.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput } from '../dom';
 import { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/faceProcessor/FaceProcessor.d.ts.map b/build/src/faceProcessor/FaceProcessor.d.ts.map
index 06600de..b94b4db 100644
--- a/build/src/faceProcessor/FaceProcessor.d.ts.map
+++ b/build/src/faceProcessor/FaceProcessor.d.ts.map
@@ -1 +1 @@
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\ No newline at end of file
+{"version":3,"file":"FaceProcessor.d.ts","sourceRoot":"","sources":["../../../src/faceProcessor/FaceProcessor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,QAAQ,EAAE,MAAM,QAAQ,CAAC;AAClC,OAAO,EACL,0BAA0B,EAC1B,qBAAqB,EACrB,8BAA8B,EAC/B,MAAM,+BAA+B,CAAC;AACvC,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAGjD,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAGpC,8BAAsB,aAAa,CACjC,gBAAgB,SAAS,0BAA0B,GAAG,8BAA8B,CAEpF,SAAQ,aAAa,CAAC,SAAS,CAAC;IAEhC,SAAS,CAAC,qBAAqB,EAAE,qBAAqB,CAAC,gBAAgB,CAAC,CAAA;gBAE5D,KAAK,EAAE,MAAM,EAAE,oBAAoB,EAAE,qBAAqB,CAAC,gBAAgB,CAAC;IAKxF,IAAW,oBAAoB,IAAI,qBAAqB,CAAC,gBAAgB,CAAC,CAEzE;IAED,SAAS,CAAC,QAAQ,CAAC,mBAAmB,IAAI,MAAM;IAChD,SAAS,CAAC,QAAQ,CAAC,uBAAuB,IAAI,MAAM;IACpD,SAAS,CAAC,QAAQ,CAAC,wBAAwB,IAAI,MAAM;IAE9C,MAAM,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ;IAgBlD,OAAO,CAAC,gBAAgB,GAAE,OAAc;IAKxC,oBAAoB,CAAC,OAAO,EAAE,YAAY;IAM1C,uBAAuB,CAAC,OAAO,EAAE,YAAY;;;;IAIpD,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IASjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAY9C"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/FaceProcessor.js b/build/src/faceProcessor/FaceProcessor.js
index 6d6fe34..656d609 100644
--- a/build/src/faceProcessor/FaceProcessor.js
+++ b/build/src/faceProcessor/FaceProcessor.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { NetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/faceProcessor/FaceProcessor.js.map b/build/src/faceProcessor/FaceProcessor.js.map
index c73cb8f..5db2132 100644
--- a/build/src/faceProcessor/FaceProcessor.js.map
+++ b/build/src/faceProcessor/FaceProcessor.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts b/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts
index 163969a..6299baa 100644
--- a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts
+++ b/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { NetParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts.map b/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts.map
index b603178..19e21a1 100644
--- a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceProcessor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAmE,YAAY,EAAE,MAAM,WAAW,CAAC;AAC1G,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAEpC,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAmBtD"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceProcessor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAmE,YAAY,EAAE,MAAM,WAAW,CAAC;AAC1G,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAEpC,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAmBtD"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/util.d.ts b/build/src/faceProcessor/util.d.ts
index 5f2be3c..73ac843 100644
--- a/build/src/faceProcessor/util.d.ts
+++ b/build/src/faceProcessor/util.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare function seperateWeightMaps(weightMap: tf.NamedTensorMap): {
     featureExtractorMap: tf.NamedTensorMap;
     classifierMap: tf.NamedTensorMap;
diff --git a/build/src/faceProcessor/util.d.ts.map b/build/src/faceProcessor/util.d.ts.map
index bf2d02f..343392d 100644
--- a/build/src/faceProcessor/util.d.ts.map
+++ b/build/src/faceProcessor/util.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"util.d.ts","sourceRoot":"","sources":["../../../src/faceProcessor/util.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,wBAAgB,kBAAkB,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;EAY9D"}
\ No newline at end of file
+{"version":3,"file":"util.d.ts","sourceRoot":"","sources":["../../../src/faceProcessor/util.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,wBAAgB,kBAAkB,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;EAY9D"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts b/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts
index e8ce6e9..f89d5a7 100644
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts
+++ b/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { NetParams } from './types';
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts.map b/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts.map
index 64cbb6e..537e603 100644
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts.map
+++ b/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"FaceRecognitionNet.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAMjD,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAGpC,qBAAa,kBAAmB,SAAQ,aAAa,CAAC,SAAS,CAAC;;IAMvD,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IA2CpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,qBAAqB,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,YAAY,GAAC,YAAY,EAAE,CAAC;IAkB1F,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
+{"version":3,"file":"FaceRecognitionNet.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAMjD,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAGpC,qBAAa,kBAAmB,SAAQ,aAAa,CAAC,SAAS,CAAC;;IAMvD,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IA2CpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI/C,qBAAqB,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,YAAY,GAAC,YAAY,EAAE,CAAC;IAkB1F,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.js b/build/src/faceRecognitionNet/FaceRecognitionNet.js
index 02f4dc6..bfa9a36 100644
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.js
+++ b/build/src/faceRecognitionNet/FaceRecognitionNet.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { normalize } from '../ops';
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.js.map b/build/src/faceRecognitionNet/FaceRecognitionNet.js.map
index c48996c..55c17d8 100644
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.js.map
+++ b/build/src/faceRecognitionNet/FaceRecognitionNet.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/convLayer.d.ts b/build/src/faceRecognitionNet/convLayer.d.ts
index 1773542..e17da31 100644
--- a/build/src/faceRecognitionNet/convLayer.d.ts
+++ b/build/src/faceRecognitionNet/convLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvLayerParams } from './types';
 export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor4D;
 export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor4D;
diff --git a/build/src/faceRecognitionNet/convLayer.d.ts.map b/build/src/faceRecognitionNet/convLayer.d.ts.map
index e187f39..83f620c 100644
--- a/build/src/faceRecognitionNet/convLayer.d.ts.map
+++ b/build/src/faceRecognitionNet/convLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"convLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,eAAe,EAAE,MAAM,SAAS,CAAC;AAkB1C,wBAAgB,IAAI,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAE3D;AAED,wBAAgB,UAAU,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAEjE;AAED,wBAAgB,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAE/D"}
\ No newline at end of file
+{"version":3,"file":"convLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,eAAe,EAAE,MAAM,SAAS,CAAC;AAkB1C,wBAAgB,IAAI,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAE3D;AAED,wBAAgB,UAAU,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAEjE;AAED,wBAAgB,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,eAE/D"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/convLayer.js b/build/src/faceRecognitionNet/convLayer.js
index 9aa5ca9..ab55bce 100644
--- a/build/src/faceRecognitionNet/convLayer.js
+++ b/build/src/faceRecognitionNet/convLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { scale } from './scaleLayer';
 function convLayer(x, params, strides, withRelu, padding = 'same') {
     const { filters, bias } = params.conv;
diff --git a/build/src/faceRecognitionNet/convLayer.js.map b/build/src/faceRecognitionNet/convLayer.js.map
index 283a05f..b3a333a 100644
--- a/build/src/faceRecognitionNet/convLayer.js.map
+++ b/build/src/faceRecognitionNet/convLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,SAAS,SAAS,CAChB,CAAc,EACd,MAAuB,EACvB,OAAyB,EACzB,QAAiB,EACjB,UAA4B,MAAM;IAElC,MAAM,EAAE,OAAO,EAAE,IAAI,EAAE,GAAG,MAAM,CAAC,IAAI,CAAA;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,UAAU,IAAI,CAAC,CAAc,EAAE,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAED,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC"}
\ No newline at end of file
+{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,SAAS,SAAS,CAChB,CAAc,EACd,MAAuB,EACvB,OAAyB,EACzB,QAAiB,EACjB,UAA4B,MAAM;IAElC,MAAM,EAAE,OAAO,EAAE,IAAI,EAAE,GAAG,MAAM,CAAC,IAAI,CAAA;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,UAAU,IAAI,CAAC,CAAc,EAAE,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAED,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParams.js b/build/src/faceRecognitionNet/extractParams.js
index 01c5701..e57710a 100644
--- a/build/src/faceRecognitionNet/extractParams.js
+++ b/build/src/faceRecognitionNet/extractParams.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { extractWeightsFactory } from '../common';
 import { isFloat } from '../utils';
 function extractorsFactory(extractWeights, paramMappings) {
diff --git a/build/src/faceRecognitionNet/extractParams.js.map b/build/src/faceRecognitionNet/extractParams.js.map
index 6ea26ca..5fc7920 100644
--- a/build/src/faceRecognitionNet/extractParams.js.map
+++ b/build/src/faceRecognitionNet/extractParams.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
index 163969a..6299baa 100644
--- a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
+++ b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { NetParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts.map b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts.map
index ef7efff..ddc6e5b 100644
--- a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAyD,YAAY,EAAE,MAAM,WAAW,CAAC;AAEhG,OAAO,EAAmB,SAAS,EAAyC,MAAM,SAAS,CAAC;AAqC5F,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAyDtD"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAyD,YAAY,EAAE,MAAM,WAAW,CAAC;AAEhG,OAAO,EAAmB,SAAS,EAAyC,MAAM,SAAS,CAAC;AAqC5F,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAyDtD"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/residualLayer.d.ts b/build/src/faceRecognitionNet/residualLayer.d.ts
index 4c102e1..572aec4 100644
--- a/build/src/faceRecognitionNet/residualLayer.d.ts
+++ b/build/src/faceRecognitionNet/residualLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ResidualLayerParams } from './types';
 export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
 export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
diff --git a/build/src/faceRecognitionNet/residualLayer.d.ts.map b/build/src/faceRecognitionNet/residualLayer.d.ts.map
index ac0a6c2..184a297 100644
--- a/build/src/faceRecognitionNet/residualLayer.d.ts.map
+++ b/build/src/faceRecognitionNet/residualLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"residualLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAMjF;AAED,wBAAgB,YAAY,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CA0BrF"}
\ No newline at end of file
+{"version":3,"file":"residualLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAMjF;AAED,wBAAgB,YAAY,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CA0BrF"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/residualLayer.js b/build/src/faceRecognitionNet/residualLayer.js
index 33b411e..7b6a126 100644
--- a/build/src/faceRecognitionNet/residualLayer.js
+++ b/build/src/faceRecognitionNet/residualLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { conv, convDown, convNoRelu } from './convLayer';
 export function residual(x, params) {
     let out = conv(x, params.conv1);
diff --git a/build/src/faceRecognitionNet/residualLayer.js.map b/build/src/faceRecognitionNet/residualLayer.js.map
index 60109e3..1d1c3ae 100644
--- a/build/src/faceRecognitionNet/residualLayer.js.map
+++ b/build/src/faceRecognitionNet/residualLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"residualLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,IAAI,EAAE,QAAQ,EAAE,UAAU,EAAE,MAAM,aAAa,CAAC;AAGzD,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAA2B;IAClE,IAAI,GAAG,GAAG,IAAI,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC/B,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,UAAU,YAAY,CAAC,CAAc,EAAE,MAA2B;IACtE,IAAI,GAAG,GAAG,QAAQ,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAEnC,IAAI,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAgB,CAAA;IACxD,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAa,MAAM,CAAC,KAAK,CAAC,CAAA;IAChD,MAAM,KAAK,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC9C,MAAM,aAAa,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAE1F,IAAI,aAAa,EAAE;QACjB,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;QAEjC,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;KAClC;IAED,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;IACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAgB,CAAA;IAExC,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"residualLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,IAAI,EAAE,QAAQ,EAAE,UAAU,EAAE,MAAM,aAAa,CAAC;AAGzD,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAA2B;IAClE,IAAI,GAAG,GAAG,IAAI,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC/B,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,UAAU,YAAY,CAAC,CAAc,EAAE,MAA2B;IACtE,IAAI,GAAG,GAAG,QAAQ,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAEnC,IAAI,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAgB,CAAA;IACxD,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAa,MAAM,CAAC,KAAK,CAAC,CAAA;IAChD,MAAM,KAAK,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC9C,MAAM,aAAa,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAE1F,IAAI,aAAa,EAAE;QACjB,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;QAEjC,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;KAClC;IAED,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;IACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAgB,CAAA;IAExC,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/scaleLayer.d.ts b/build/src/faceRecognitionNet/scaleLayer.d.ts
index c87771c..edd370d 100644
--- a/build/src/faceRecognitionNet/scaleLayer.d.ts
+++ b/build/src/faceRecognitionNet/scaleLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ScaleLayerParams } from './types';
 export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
 //# sourceMappingURL=scaleLayer.d.ts.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/scaleLayer.d.ts.map b/build/src/faceRecognitionNet/scaleLayer.d.ts.map
index 272c63a..deca33b 100644
--- a/build/src/faceRecognitionNet/scaleLayer.d.ts.map
+++ b/build/src/faceRecognitionNet/scaleLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"scaleLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,gBAAgB,EAAE,MAAM,SAAS,CAAC;AAE3C,wBAAgB,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAE3E"}
\ No newline at end of file
+{"version":3,"file":"scaleLayer.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,gBAAgB,EAAE,MAAM,SAAS,CAAC;AAE3C,wBAAgB,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAE3E"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/scaleLayer.js b/build/src/faceRecognitionNet/scaleLayer.js
index 6c1b911..026ac6a 100644
--- a/build/src/faceRecognitionNet/scaleLayer.js
+++ b/build/src/faceRecognitionNet/scaleLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function scale(x, params) {
     return tf.add(tf.mul(x, params.weights), params.biases);
 }
diff --git a/build/src/faceRecognitionNet/scaleLayer.js.map b/build/src/faceRecognitionNet/scaleLayer.js.map
index 82536d2..9a717a8 100644
--- a/build/src/faceRecognitionNet/scaleLayer.js.map
+++ b/build/src/faceRecognitionNet/scaleLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,KAAK,CAAC,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file
+{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,KAAK,CAAC,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/types.d.ts b/build/src/faceRecognitionNet/types.d.ts
index 477e933..f8e4455 100644
--- a/build/src/faceRecognitionNet/types.d.ts
+++ b/build/src/faceRecognitionNet/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvParams } from '../common';
 export declare type ScaleLayerParams = {
     weights: tf.Tensor1D;
diff --git a/build/src/faceRecognitionNet/types.d.ts.map b/build/src/faceRecognitionNet/types.d.ts.map
index 420503a..3a39d5a 100644
--- a/build/src/faceRecognitionNet/types.d.ts.map
+++ b/build/src/faceRecognitionNet/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,UAAU,EAAE,MAAM,WAAW,CAAC;AAEvC,oBAAY,gBAAgB,GAAG;IAC7B,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AACD,oBAAY,mBAAmB,GAAG;IAChC,KAAK,EAAE,eAAe,CAAA;IACtB,KAAK,EAAE,eAAe,CAAA;CACvB,CAAA;AAED,oBAAY,eAAe,GAAG;IAC5B,IAAI,EAAE,UAAU,CAAA;IAChB,KAAK,EAAE,gBAAgB,CAAA;CACxB,CAAA;AAED,oBAAY,SAAS,GAAG;IACtB,WAAW,EAAE,eAAe,CAAA;IAC5B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,WAAW,EAAE,mBAAmB,CAAA;IAChC,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,YAAY,EAAE,mBAAmB,CAAA;IACjC,SAAS,EAAE,mBAAmB,CAAA;IAC9B,SAAS,EAAE,mBAAmB,CAAA;IAC9B,YAAY,EAAE,mBAAmB,CAAA;IACjC,SAAS,EAAE,mBAAmB,CAAA;IAC9B,SAAS,EAAE,mBAAmB,CAAA;IAC9B,gBAAgB,EAAE,mBAAmB,CAAA;IACrC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAA;CAChB,CAAA"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/faceRecognitionNet/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,UAAU,EAAE,MAAM,WAAW,CAAC;AAEvC,oBAAY,gBAAgB,GAAG;IAC7B,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,MAAM,EAAE,EAAE,CAAC,QAAQ,CAAA;CACpB,CAAA;AACD,oBAAY,mBAAmB,GAAG;IAChC,KAAK,EAAE,eAAe,CAAA;IACtB,KAAK,EAAE,eAAe,CAAA;CACvB,CAAA;AAED,oBAAY,eAAe,GAAG;IAC5B,IAAI,EAAE,UAAU,CAAA;IAChB,KAAK,EAAE,gBAAgB,CAAA;CACxB,CAAA;AAED,oBAAY,SAAS,GAAG;IACtB,WAAW,EAAE,eAAe,CAAA;IAC5B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,WAAW,EAAE,mBAAmB,CAAA;IAChC,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,QAAQ,EAAE,mBAAmB,CAAA;IAC7B,YAAY,EAAE,mBAAmB,CAAA;IACjC,SAAS,EAAE,mBAAmB,CAAA;IAC9B,SAAS,EAAE,mBAAmB,CAAA;IAC9B,YAAY,EAAE,mBAAmB,CAAA;IACjC,SAAS,EAAE,mBAAmB,CAAA;IAC9B,SAAS,EAAE,mBAAmB,CAAA;IAC9B,gBAAgB,EAAE,mBAAmB,CAAA;IACrC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAA;CAChB,CAAA"}
\ No newline at end of file
diff --git a/build/src/globalApi/DetectFaceLandmarksTasks.js b/build/src/globalApi/DetectFaceLandmarksTasks.js
index 96c7a6f..5a8c009 100644
--- a/build/src/globalApi/DetectFaceLandmarksTasks.js
+++ b/build/src/globalApi/DetectFaceLandmarksTasks.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { extractFaces, extractFaceTensors } from '../dom';
 import { extendWithFaceLandmarks } from '../factories/WithFaceLandmarks';
 import { ComposableTask } from './ComposableTask';
diff --git a/build/src/globalApi/DetectFaceLandmarksTasks.js.map b/build/src/globalApi/DetectFaceLandmarksTasks.js.map
index f8dbf81..e7f9673 100644
--- a/build/src/globalApi/DetectFaceLandmarksTasks.js.map
+++ b/build/src/globalApi/DetectFaceLandmarksTasks.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/globalApi/PredictAgeAndGenderTask.d.ts b/build/src/globalApi/PredictAgeAndGenderTask.d.ts
index e1094d9..5d69424 100644
--- a/build/src/globalApi/PredictAgeAndGenderTask.d.ts
+++ b/build/src/globalApi/PredictAgeAndGenderTask.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { TNetInput } from '../dom';
 import { WithAge } from '../factories/WithAge';
 import { WithFaceDetection } from '../factories/WithFaceDetection';
diff --git a/build/src/globalApi/PredictAgeAndGenderTask.d.ts.map b/build/src/globalApi/PredictAgeAndGenderTask.d.ts.map
index 8ee2307..5d49043 100644
--- a/build/src/globalApi/PredictAgeAndGenderTask.d.ts.map
+++ b/build/src/globalApi/PredictAgeAndGenderTask.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"PredictAgeAndGenderTask.d.ts","sourceRoot":"","sources":["../../../src/globalApi/PredictAgeAndGenderTask.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAiB,OAAO,EAAE,MAAM,sBAAsB,CAAC;AAC9D,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAoB,UAAU,EAAE,MAAM,yBAAyB,CAAC;AACvE,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,6BAA6B,EAAE,+BAA+B,EAAE,MAAM,+BAA+B,CAAC;AAG/G,OAAO,EACL,6BAA6B,EAC7B,8CAA8C,EAC9C,gCAAgC,EAChC,iDAAiD,EAClD,MAAM,8BAA8B,CAAC;AAEtC,qBAAa,2BAA2B,CAAC,OAAO,EAAE,aAAa,CAAE,SAAQ,cAAc,CAAC,OAAO,CAAC;IAE5F,SAAS,CAAC,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC;IAC5E,SAAS,CAAC,KAAK,EAAE,SAAS;IAC1B,SAAS,CAAC,cAAc,CAAC;gBAFf,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC,EAClE,KAAK,EAAE,SAAS,EAChB,cAAc,CAAC,iDAAwC;CAIpE;AAED,qBAAa,0BAA0B,CACrC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACrC,SAAQ,2BAA2B,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,EAAE,EAAE,OAAO,EAAE,CAAC;IAEjE,GAAG,IAAI,OAAO,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC;IAmB3D,mBAAmB;CAGpB;AAED,qBAAa,6BAA6B,CACxC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACpC,SAAQ,2BAA2B,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,GAAG,SAAS,EAAE,OAAO,GAAG,SAAS,CAAC;IAEtF,GAAG,IAAI,OAAO,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,GAAG,SAAS,CAAC;IAiBrE,mBAAmB;CAGpB;AAED,qBAAa,2CAA2C,CACtD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,0BAA0B,CAAC,OAAO,CAAC;IAE3C,mBAAmB;IAInB,mBAAmB;CAGpB;AAED,qBAAa,8CAA8C,CACzD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,6BAA6B,CAAC,OAAO,CAAC;IAE9C,mBAAmB;IAInB,kBAAkB;CAGnB"}
\ No newline at end of file
+{"version":3,"file":"PredictAgeAndGenderTask.d.ts","sourceRoot":"","sources":["../../../src/globalApi/PredictAgeAndGenderTask.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAiB,OAAO,EAAE,MAAM,sBAAsB,CAAC;AAC9D,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAoB,UAAU,EAAE,MAAM,yBAAyB,CAAC;AACvE,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,6BAA6B,EAAE,+BAA+B,EAAE,MAAM,+BAA+B,CAAC;AAG/G,OAAO,EACL,6BAA6B,EAC7B,8CAA8C,EAC9C,gCAAgC,EAChC,iDAAiD,EAClD,MAAM,8BAA8B,CAAC;AAEtC,qBAAa,2BAA2B,CAAC,OAAO,EAAE,aAAa,CAAE,SAAQ,cAAc,CAAC,OAAO,CAAC;IAE5F,SAAS,CAAC,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC;IAC5E,SAAS,CAAC,KAAK,EAAE,SAAS;IAC1B,SAAS,CAAC,cAAc,CAAC;gBAFf,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC,EAClE,KAAK,EAAE,SAAS,EAChB,cAAc,CAAC,iDAAwC;CAIpE;AAED,qBAAa,0BAA0B,CACrC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACrC,SAAQ,2BAA2B,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,EAAE,EAAE,OAAO,EAAE,CAAC;IAEjE,GAAG,IAAI,OAAO,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC;IAmB3D,mBAAmB;CAGpB;AAED,qBAAa,6BAA6B,CACxC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACpC,SAAQ,2BAA2B,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,GAAG,SAAS,EAAE,OAAO,GAAG,SAAS,CAAC;IAEtF,GAAG,IAAI,OAAO,CAAC,OAAO,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,GAAG,SAAS,CAAC;IAiBrE,mBAAmB;CAGpB;AAED,qBAAa,2CAA2C,CACtD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,0BAA0B,CAAC,OAAO,CAAC;IAE3C,mBAAmB;IAInB,mBAAmB;CAGpB;AAED,qBAAa,8CAA8C,CACzD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,6BAA6B,CAAC,OAAO,CAAC;IAE9C,mBAAmB;IAInB,kBAAkB;CAGnB"}
\ No newline at end of file
diff --git a/build/src/globalApi/PredictFaceExpressionsTask.d.ts b/build/src/globalApi/PredictFaceExpressionsTask.d.ts
index d70b1be..c0fadc8 100644
--- a/build/src/globalApi/PredictFaceExpressionsTask.d.ts
+++ b/build/src/globalApi/PredictFaceExpressionsTask.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { TNetInput } from '../dom';
 import { WithFaceDetection } from '../factories/WithFaceDetection';
 import { WithFaceExpressions } from '../factories/WithFaceExpressions';
diff --git a/build/src/globalApi/PredictFaceExpressionsTask.d.ts.map b/build/src/globalApi/PredictFaceExpressionsTask.d.ts.map
index d5d522c..00a5def 100644
--- a/build/src/globalApi/PredictFaceExpressionsTask.d.ts.map
+++ b/build/src/globalApi/PredictFaceExpressionsTask.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"PredictFaceExpressionsTask.d.ts","sourceRoot":"","sources":["../../../src/globalApi/PredictFaceExpressionsTask.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AAEnC,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAA6B,mBAAmB,EAAE,MAAM,kCAAkC,CAAC;AAClG,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,6BAA6B,EAAE,+BAA+B,EAAE,MAAM,+BAA+B,CAAC;AAG/G,OAAO,EACL,0BAA0B,EAC1B,2CAA2C,EAC3C,6BAA6B,EAC7B,8CAA8C,EAC/C,MAAM,2BAA2B,CAAC;AAEnC,qBAAa,8BAA8B,CAAC,OAAO,EAAE,aAAa,CAAE,SAAQ,cAAc,CAAC,OAAO,CAAC;IAE/F,SAAS,CAAC,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC;IAC5E,SAAS,CAAC,KAAK,EAAE,SAAS;IAC1B,SAAS,CAAC,cAAc,CAAC;gBAFf,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC,EAClE,KAAK,EAAE,SAAS,EAChB,cAAc,CAAC,iDAAwC;CAIpE;AAED,qBAAa,6BAA6B,CACxC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACrC,SAAQ,8BAA8B,CAAC,mBAAmB,CAAC,OAAO,CAAC,EAAE,EAAE,OAAO,EAAE,CAAC;IAEpE,GAAG,IAAI,OAAO,CAAC,mBAAmB,CAAC,OAAO,CAAC,EAAE,CAAC;IAkB3D,gBAAgB;CAGjB;AAED,qBAAa,gCAAgC,CAC3C,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACpC,SAAQ,8BAA8B,CAAC,mBAAmB,CAAC,OAAO,CAAC,GAAG,SAAS,EAAE,OAAO,GAAG,SAAS,CAAC;IAEzF,GAAG,IAAI,OAAO,CAAC,mBAAmB,CAAC,OAAO,CAAC,GAAG,SAAS,CAAC;IAiBrE,gBAAgB;CAGjB;AAED,qBAAa,8CAA8C,CACzD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,6BAA6B,CAAC,OAAO,CAAC;IAE9C,gBAAgB;IAIhB,mBAAmB;CAGpB;AAED,qBAAa,iDAAiD,CAC5D,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,gCAAgC,CAAC,OAAO,CAAC;IAEjD,gBAAgB;IAIhB,kBAAkB;CAGnB"}
\ No newline at end of file
+{"version":3,"file":"PredictFaceExpressionsTask.d.ts","sourceRoot":"","sources":["../../../src/globalApi/PredictFaceExpressionsTask.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AAEnC,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAA6B,mBAAmB,EAAE,MAAM,kCAAkC,CAAC;AAClG,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,6BAA6B,EAAE,+BAA+B,EAAE,MAAM,+BAA+B,CAAC;AAG/G,OAAO,EACL,0BAA0B,EAC1B,2CAA2C,EAC3C,6BAA6B,EAC7B,8CAA8C,EAC/C,MAAM,2BAA2B,CAAC;AAEnC,qBAAa,8BAA8B,CAAC,OAAO,EAAE,aAAa,CAAE,SAAQ,cAAc,CAAC,OAAO,CAAC;IAE/F,SAAS,CAAC,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC;IAC5E,SAAS,CAAC,KAAK,EAAE,SAAS;IAC1B,SAAS,CAAC,cAAc,CAAC;gBAFf,UAAU,EAAE,cAAc,CAAC,aAAa,CAAC,GAAG,OAAO,CAAC,aAAa,CAAC,EAClE,KAAK,EAAE,SAAS,EAChB,cAAc,CAAC,iDAAwC;CAIpE;AAED,qBAAa,6BAA6B,CACxC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACrC,SAAQ,8BAA8B,CAAC,mBAAmB,CAAC,OAAO,CAAC,EAAE,EAAE,OAAO,EAAE,CAAC;IAEpE,GAAG,IAAI,OAAO,CAAC,mBAAmB,CAAC,OAAO,CAAC,EAAE,CAAC;IAkB3D,gBAAgB;CAGjB;AAED,qBAAa,gCAAgC,CAC3C,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,CACpC,SAAQ,8BAA8B,CAAC,mBAAmB,CAAC,OAAO,CAAC,GAAG,SAAS,EAAE,OAAO,GAAG,SAAS,CAAC;IAEzF,GAAG,IAAI,OAAO,CAAC,mBAAmB,CAAC,OAAO,CAAC,GAAG,SAAS,CAAC;IAiBrE,gBAAgB;CAGjB;AAED,qBAAa,8CAA8C,CACzD,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,6BAA6B,CAAC,OAAO,CAAC;IAE9C,gBAAgB;IAIhB,mBAAmB;CAGpB;AAED,qBAAa,iDAAiD,CAC5D,OAAO,SAAS,iBAAiB,CAAC,iBAAiB,CAAC,EAAE,CAAC,CAAC,CACxD,SAAQ,gCAAgC,CAAC,OAAO,CAAC;IAEjD,gBAAgB;IAIhB,kBAAkB;CAGnB"}
\ No newline at end of file
diff --git a/build/src/globalApi/extractFacesAndComputeResults.d.ts b/build/src/globalApi/extractFacesAndComputeResults.d.ts
index cb51276..86b6345 100644
--- a/build/src/globalApi/extractFacesAndComputeResults.d.ts
+++ b/build/src/globalApi/extractFacesAndComputeResults.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FaceDetection } from '../classes/FaceDetection';
 import { TNetInput } from '../dom';
 import { WithFaceDetection } from '../factories/WithFaceDetection';
diff --git a/build/src/globalApi/extractFacesAndComputeResults.d.ts.map b/build/src/globalApi/extractFacesAndComputeResults.d.ts.map
index 4bb8fd1..949366c 100644
--- a/build/src/globalApi/extractFacesAndComputeResults.d.ts.map
+++ b/build/src/globalApi/extractFacesAndComputeResults.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractFacesAndComputeResults.d.ts","sourceRoot":"","sources":["../../../src/globalApi/extractFacesAndComputeResults.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAoC,SAAS,EAAE,MAAM,QAAQ,CAAC;AACrE,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAuB,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AAExF,wBAAsB,gCAAgC,CAAC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,EAAE,OAAO,EACnG,aAAa,EAAE,OAAO,EAAE,EACxB,KAAK,EAAE,SAAS,EAChB,cAAc,EAAE,CAAC,KAAK,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,KAAK,OAAO,CAAC,OAAO,CAAC,EACnF,cAAc,CAAC,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,GAAG,IAAI,EAC9D,mBAAmB,GAAE,CAAC,YAAY,EAAE,iBAAiB,CAAC,OAAO,EAAE,GAAG,CAAC,KAAK,aAAgD,oBAkBzH;AAED,wBAAsB,iCAAiC,CAAC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,EAAE,OAAO,EACpG,YAAY,EAAE,OAAO,EACrB,KAAK,EAAE,SAAS,EAChB,aAAa,EAAE,CAAC,IAAI,EAAE,iBAAiB,GAAG,EAAE,CAAC,QAAQ,KAAK,OAAO,CAAC,OAAO,CAAC,EAC1E,cAAc,CAAC,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,GAAG,IAAI,EAC9D,mBAAmB,CAAC,EAAE,CAAC,YAAY,EAAE,iBAAiB,CAAC,OAAO,EAAE,GAAG,CAAC,KAAK,aAAa,oBASvF"}
\ No newline at end of file
+{"version":3,"file":"extractFacesAndComputeResults.d.ts","sourceRoot":"","sources":["../../../src/globalApi/extractFacesAndComputeResults.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAoC,SAAS,EAAE,MAAM,QAAQ,CAAC;AACrE,OAAO,EAAE,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AACnE,OAAO,EAAuB,iBAAiB,EAAE,MAAM,gCAAgC,CAAC;AAExF,wBAAsB,gCAAgC,CAAC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,EAAE,OAAO,EACnG,aAAa,EAAE,OAAO,EAAE,EACxB,KAAK,EAAE,SAAS,EAChB,cAAc,EAAE,CAAC,KAAK,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,KAAK,OAAO,CAAC,OAAO,CAAC,EACnF,cAAc,CAAC,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,GAAG,IAAI,EAC9D,mBAAmB,GAAE,CAAC,YAAY,EAAE,iBAAiB,CAAC,OAAO,EAAE,GAAG,CAAC,KAAK,aAAgD,oBAkBzH;AAED,wBAAsB,iCAAiC,CAAC,OAAO,SAAS,iBAAiB,CAAC,EAAE,CAAC,EAAE,OAAO,EACpG,YAAY,EAAE,OAAO,EACrB,KAAK,EAAE,SAAS,EAChB,aAAa,EAAE,CAAC,IAAI,EAAE,iBAAiB,GAAG,EAAE,CAAC,QAAQ,KAAK,OAAO,CAAC,OAAO,CAAC,EAC1E,cAAc,CAAC,EAAE,KAAK,CAAC,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,GAAG,IAAI,EAC9D,mBAAmB,CAAC,EAAE,CAAC,YAAY,EAAE,iBAAiB,CAAC,OAAO,EAAE,GAAG,CAAC,KAAK,aAAa,oBASvF"}
\ No newline at end of file
diff --git a/build/src/globalApi/extractFacesAndComputeResults.js b/build/src/globalApi/extractFacesAndComputeResults.js
index eb045d4..d941f4f 100644
--- a/build/src/globalApi/extractFacesAndComputeResults.js
+++ b/build/src/globalApi/extractFacesAndComputeResults.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { extractFaces, extractFaceTensors } from '../dom';
 import { isWithFaceLandmarks } from '../factories/WithFaceLandmarks';
 export async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) {
diff --git a/build/src/globalApi/extractFacesAndComputeResults.js.map b/build/src/globalApi/extractFacesAndComputeResults.js.map
index 35becd1..8e83107 100644
--- a/build/src/globalApi/extractFacesAndComputeResults.js.map
+++ b/build/src/globalApi/extractFacesAndComputeResults.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractFacesAndComputeResults.js","sourceRoot":"","sources":["../../../src/globalApi/extractFacesAndComputeResults.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,YAAY,EAAE,kBAAkB,EAAa,MAAM,QAAQ,CAAC;AAErE,OAAO,EAAE,mBAAmB,EAAqB,MAAM,gCAAgC,CAAC;AAExF,MAAM,CAAC,KAAK,UAAU,gCAAgC,CACpD,aAAwB,EACxB,KAAgB,EAChB,cAAmF,EACnF,cAA8D,EAC9D,sBAAwF,CAAC,EAAE,WAAW,EAAE,EAAE,EAAE,CAAC,WAAW;IAExH,MAAM,SAAS,GAAG,aAAa,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE,CACjD,mBAAmB,CAAC,YAAY,CAAC;QAC/B,CAAC,CAAC,mBAAmB,CAAC,YAAY,CAAC;QACnC,CAAC,CAAC,YAAY,CAAC,SAAS,CAC3B,CAAA;IACD,MAAM,KAAK,GAA2C,cAAc,IAAI,CACtE,KAAK,YAAY,EAAE,CAAC,MAAM;QACxB,CAAC,CAAC,MAAM,kBAAkB,CAAC,KAAK,EAAE,SAAS,CAAC;QAC5C,CAAC,CAAC,MAAM,YAAY,CAAC,KAAK,EAAE,SAAS,CAAC,CACzC,CAAA;IAED,MAAM,OAAO,GAAG,MAAM,cAAc,CAAC,KAAK,CAAC,CAAA;IAE3C,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,YAAY,EAAE,CAAC,MAAM,IAAI,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;IAEzD,OAAO,OAAO,CAAA;AAChB,CAAC;AAED,MAAM,CAAC,KAAK,UAAU,iCAAiC,CACrD,YAAqB,EACrB,KAAgB,EAChB,aAA0E,EAC1E,cAA8D,EAC9D,mBAAsF;IAEtF,OAAO,gCAAgC,CACrC,CAAC,YAAY,CAAC,EACd,KAAK,EACL,KAAK,EAAC,KAAK,EAAC,EAAE,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EACtC,cAAc,EACd,mBAAmB,CACpB,CAAA;AACH,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractFacesAndComputeResults.js","sourceRoot":"","sources":["../../../src/globalApi/extractFacesAndComputeResults.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,YAAY,EAAE,kBAAkB,EAAa,MAAM,QAAQ,CAAC;AAErE,OAAO,EAAE,mBAAmB,EAAqB,MAAM,gCAAgC,CAAC;AAExF,MAAM,CAAC,KAAK,UAAU,gCAAgC,CACpD,aAAwB,EACxB,KAAgB,EAChB,cAAmF,EACnF,cAA8D,EAC9D,sBAAwF,CAAC,EAAE,WAAW,EAAE,EAAE,EAAE,CAAC,WAAW;IAExH,MAAM,SAAS,GAAG,aAAa,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE,CACjD,mBAAmB,CAAC,YAAY,CAAC;QAC/B,CAAC,CAAC,mBAAmB,CAAC,YAAY,CAAC;QACnC,CAAC,CAAC,YAAY,CAAC,SAAS,CAC3B,CAAA;IACD,MAAM,KAAK,GAA2C,cAAc,IAAI,CACtE,KAAK,YAAY,EAAE,CAAC,MAAM;QACxB,CAAC,CAAC,MAAM,kBAAkB,CAAC,KAAK,EAAE,SAAS,CAAC;QAC5C,CAAC,CAAC,MAAM,YAAY,CAAC,KAAK,EAAE,SAAS,CAAC,CACzC,CAAA;IAED,MAAM,OAAO,GAAG,MAAM,cAAc,CAAC,KAAK,CAAC,CAAA;IAE3C,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,YAAY,EAAE,CAAC,MAAM,IAAI,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;IAEzD,OAAO,OAAO,CAAA;AAChB,CAAC;AAED,MAAM,CAAC,KAAK,UAAU,iCAAiC,CACrD,YAAqB,EACrB,KAAgB,EAChB,aAA0E,EAC1E,cAA8D,EAC9D,mBAAsF;IAEtF,OAAO,gCAAgC,CACrC,CAAC,YAAY,CAAC,EACd,KAAK,EACL,KAAK,EAAC,KAAK,EAAC,EAAE,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EACtC,cAAc,EACd,mBAAmB,CACpB,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/index.d.ts b/build/src/index.d.ts
index 4d2b4bf..488aaf3 100644
--- a/build/src/index.d.ts
+++ b/build/src/index.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import * as draw from './draw';
 import * as utils from './utils';
 export { draw, utils, tf };
@@ -22,7 +22,5 @@ export declare const version: {
     faceapi: string;
     node: string | boolean;
     browser: string | boolean;
-    tfjs: string;
-    backend: string;
 };
 //# sourceMappingURL=index.d.ts.map
\ No newline at end of file
diff --git a/build/src/index.d.ts.map b/build/src/index.d.ts.map
index 4d6b2c8..39b80c2 100644
--- a/build/src/index.d.ts.map
+++ b/build/src/index.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAGA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,KAAK,IAAI,MAAM,QAAQ,CAAC;AAC/B,OAAO,KAAK,KAAK,MAAM,SAAS,CAAC;AACjC,OAAO,EAAE,IAAI,EAAE,KAAK,EAAE,EAAE,EAAE,CAAC;AAE3B,cAAc,sBAAsB,CAAC;AACrC,cAAc,iBAAiB,CAAC;AAChC,cAAc,aAAa,CAAA;AAC3B,cAAc,aAAa,CAAC;AAC5B,cAAc,2BAA2B,CAAC;AAC1C,cAAc,yBAAyB,CAAC;AACxC,cAAc,4BAA4B,CAAC;AAC3C,cAAc,mBAAmB,CAAC;AAClC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,wBAAwB,CAAC;AACvC,cAAc,0BAA0B,CAAC;AACzC,cAAc,oBAAoB,CAAC;AACnC,cAAc,qBAAqB,CAAC;AACpC,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAKhC,eAAO,MAAM,OAAO;;;;;;CAA2F,CAAC"}
\ No newline at end of file
+{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAGA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AACvC,OAAO,KAAK,IAAI,MAAM,QAAQ,CAAC;AAC/B,OAAO,KAAK,KAAK,MAAM,SAAS,CAAC;AACjC,OAAO,EAAE,IAAI,EAAE,KAAK,EAAE,EAAE,EAAE,CAAC;AAE3B,cAAc,sBAAsB,CAAC;AACrC,cAAc,iBAAiB,CAAC;AAChC,cAAc,aAAa,CAAA;AAC3B,cAAc,aAAa,CAAC;AAC5B,cAAc,2BAA2B,CAAC;AAC1C,cAAc,yBAAyB,CAAC;AACxC,cAAc,4BAA4B,CAAC;AAC3C,cAAc,mBAAmB,CAAC;AAClC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,wBAAwB,CAAC;AACvC,cAAc,0BAA0B,CAAC;AACzC,cAAc,oBAAoB,CAAC;AACnC,cAAc,qBAAqB,CAAC;AACpC,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAKhC,eAAO,MAAM,OAAO;;;;CAA0C,CAAC"}
\ No newline at end of file
diff --git a/build/src/index.js b/build/src/index.js
index 0a69751..086e893 100644
--- a/build/src/index.js
+++ b/build/src/index.js
@@ -1,6 +1,6 @@
 // const os = require('os');
 // const process = require('process');
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import * as draw from './draw';
 import * as utils from './utils';
 export { draw, utils, tf };
@@ -23,5 +23,5 @@ export * from './resizeResults';
 import * as pkg from '../package.json';
 const node = typeof process !== 'undefined' ? process.version : false;
 const browser = typeof navigator !== 'undefined' ? navigator.userAgent : false;
-export const version = { faceapi: pkg.version, node, browser, tfjs: tf.version_core, backend: tf.getBackend() };
+export const version = { faceapi: pkg.version, node, browser };
 //# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/index.js.map b/build/src/index.js.map
index 0164fb0..67d1bd1 100644
--- a/build/src/index.js.map
+++ b/build/src/index.js.map
@@ -1 +1 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,4BAA4B;AAC5B,sCAAsC;AAEtC,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAC5C,OAAO,KAAK,IAAI,MAAM,QAAQ,CAAC;AAC/B,OAAO,KAAK,KAAK,MAAM,SAAS,CAAC;AACjC,OAAO,EAAE,IAAI,EAAE,KAAK,EAAE,EAAE,EAAE,CAAC;AAE3B,cAAc,sBAAsB,CAAC;AACrC,cAAc,iBAAiB,CAAC;AAChC,cAAc,aAAa,CAAA;AAC3B,cAAc,aAAa,CAAC;AAC5B,cAAc,2BAA2B,CAAC;AAC1C,cAAc,yBAAyB,CAAC;AACxC,cAAc,4BAA4B,CAAC;AAC3C,cAAc,mBAAmB,CAAC;AAClC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,wBAAwB,CAAC;AACvC,cAAc,0BAA0B,CAAC;AACzC,cAAc,oBAAoB,CAAC;AACnC,cAAc,qBAAqB,CAAC;AACpC,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAEhC,OAAO,KAAK,GAAG,MAAM,iBAAiB,CAAC;AACvC,MAAM,IAAI,GAAG,OAAO,OAAO,KAAK,WAAW,CAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAA;AACrE,MAAM,OAAO,GAAG,OAAO,SAAS,KAAK,WAAW,CAAC,CAAC,CAAC,SAAS,CAAC,SAAS,CAAC,CAAC,CAAC,KAAK,CAAA;AAC9E,MAAM,CAAC,MAAM,OAAO,GAAG,EAAE,OAAO,EAAE,GAAG,CAAC,OAAO,EAAE,IAAI,EAAE,OAAO,EAAE,IAAI,EAAE,EAAE,CAAC,YAAY,EAAE,OAAO,EAAE,EAAE,CAAC,UAAU,EAAE,EAAE,CAAC"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,4BAA4B;AAC5B,sCAAsC;AAEtC,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AACvC,OAAO,KAAK,IAAI,MAAM,QAAQ,CAAC;AAC/B,OAAO,KAAK,KAAK,MAAM,SAAS,CAAC;AACjC,OAAO,EAAE,IAAI,EAAE,KAAK,EAAE,EAAE,EAAE,CAAC;AAE3B,cAAc,sBAAsB,CAAC;AACrC,cAAc,iBAAiB,CAAC;AAChC,cAAc,aAAa,CAAA;AAC3B,cAAc,aAAa,CAAC;AAC5B,cAAc,2BAA2B,CAAC;AAC1C,cAAc,yBAAyB,CAAC;AACxC,cAAc,4BAA4B,CAAC;AAC3C,cAAc,mBAAmB,CAAC;AAClC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,wBAAwB,CAAC;AACvC,cAAc,0BAA0B,CAAC;AACzC,cAAc,oBAAoB,CAAC;AACnC,cAAc,qBAAqB,CAAC;AACpC,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAEhC,OAAO,KAAK,GAAG,MAAM,iBAAiB,CAAC;AACvC,MAAM,IAAI,GAAG,OAAO,OAAO,KAAK,WAAW,CAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAA;AACrE,MAAM,OAAO,GAAG,OAAO,SAAS,KAAK,WAAW,CAAC,CAAC,CAAC,SAAS,CAAC,SAAS,CAAC,CAAC,CAAC,KAAK,CAAA;AAC9E,MAAM,CAAC,MAAM,OAAO,GAAG,EAAE,OAAO,EAAE,GAAG,CAAC,OAAO,EAAE,IAAI,EAAE,OAAO,EAAE,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/normalize.d.ts b/build/src/ops/normalize.d.ts
index 5896e9c..0fc7fbf 100644
--- a/build/src/ops/normalize.d.ts
+++ b/build/src/ops/normalize.d.ts
@@ -1,3 +1,3 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D;
 //# sourceMappingURL=normalize.d.ts.map
\ No newline at end of file
diff --git a/build/src/ops/normalize.d.ts.map b/build/src/ops/normalize.d.ts.map
index c25eaa8..faf2df6 100644
--- a/build/src/ops/normalize.d.ts.map
+++ b/build/src/ops/normalize.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"normalize.d.ts","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,wBAAgB,SAAS,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,OAAO,EAAE,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAUxE"}
\ No newline at end of file
+{"version":3,"file":"normalize.d.ts","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,wBAAgB,SAAS,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,OAAO,EAAE,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAUxE"}
\ No newline at end of file
diff --git a/build/src/ops/normalize.js b/build/src/ops/normalize.js
index e489c02..3a15dc5 100644
--- a/build/src/ops/normalize.js
+++ b/build/src/ops/normalize.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function normalize(x, meanRgb) {
     return tf.tidy(() => {
         const [r, g, b] = meanRgb;
diff --git a/build/src/ops/normalize.js.map b/build/src/ops/normalize.js.map
index 4e13939..727c6c8 100644
--- a/build/src/ops/normalize.js.map
+++ b/build/src/ops/normalize.js.map
@@ -1 +1 @@
-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/padToSquare.d.ts b/build/src/ops/padToSquare.d.ts
index b546993..86a7ce6 100644
--- a/build/src/ops/padToSquare.d.ts
+++ b/build/src/ops/padToSquare.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 /**
  * Pads the smaller dimension of an image tensor with zeros, such that width === height.
  *
diff --git a/build/src/ops/padToSquare.d.ts.map b/build/src/ops/padToSquare.d.ts.map
index c63a128..48886dc 100644
--- a/build/src/ops/padToSquare.d.ts.map
+++ b/build/src/ops/padToSquare.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"padToSquare.d.ts","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C;;;;;;;GAOG;AACH,wBAAgB,WAAW,CACzB,SAAS,EAAE,EAAE,CAAC,QAAQ,EACtB,aAAa,GAAE,OAAe,GAC7B,EAAE,CAAC,QAAQ,CAoCb"}
\ No newline at end of file
+{"version":3,"file":"padToSquare.d.ts","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC;;;;;;;GAOG;AACH,wBAAgB,WAAW,CACzB,SAAS,EAAE,EAAE,CAAC,QAAQ,EACtB,aAAa,GAAE,OAAe,GAC7B,EAAE,CAAC,QAAQ,CAoCb"}
\ No newline at end of file
diff --git a/build/src/ops/padToSquare.js b/build/src/ops/padToSquare.js
index 267aa5d..8a08f14 100644
--- a/build/src/ops/padToSquare.js
+++ b/build/src/ops/padToSquare.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 /**
  * Pads the smaller dimension of an image tensor with zeros, such that width === height.
  *
diff --git a/build/src/ops/padToSquare.js.map b/build/src/ops/padToSquare.js.map
index f54cbe6..7dc059c 100644
--- a/build/src/ops/padToSquare.js.map
+++ b/build/src/ops/padToSquare.js.map
@@ -1 +1 @@
-{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C;;;;;;;GAOG;AACH,MAAM,UAAU,WAAW,CACzB,SAAsB,EACtB,gBAAyB,KAAK;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,MAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,MAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,MAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,MAAM,mBAAmB,GAAG,CAAC,aAAqB,EAAa,EAAE;YAC/D,MAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,CAAA;YAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,kBAAkB,EAAE,CAAC,CAAC,CAAA;QACvC,CAAC,CAAA;QAED,MAAM,mBAAmB,GAAG,mBAAmB,CAAC,aAAa,CAAC,CAAA;QAC9D,MAAM,sBAAsB,GAAG,OAAO,GAAI,mBAAmB,CAAC,KAAK,CAAC,WAAW,CAAY,CAAA;QAE3F,MAAM,oBAAoB,GAAG,aAAa,IAAI,sBAAsB;YAClE,CAAC,CAAC,mBAAmB,CAAC,sBAAsB,CAAC;YAC7C,CAAC,CAAC,IAAI,CAAA;QAER,MAAM,cAAc,GAAG;YACrB,oBAAoB;YACpB,SAAS;YACT,mBAAmB;SACpB;aACE,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;YACjB,uDAAuD;aACtD,GAAG,CAAC,CAAC,CAAY,EAAE,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAkB,CAAA;QAChE,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAE/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC;;;;;;;GAOG;AACH,MAAM,UAAU,WAAW,CACzB,SAAsB,EACtB,gBAAyB,KAAK;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,MAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,MAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,MAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,MAAM,mBAAmB,GAAG,CAAC,aAAqB,EAAa,EAAE;YAC/D,MAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,CAAA;YAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,kBAAkB,EAAE,CAAC,CAAC,CAAA;QACvC,CAAC,CAAA;QAED,MAAM,mBAAmB,GAAG,mBAAmB,CAAC,aAAa,CAAC,CAAA;QAC9D,MAAM,sBAAsB,GAAG,OAAO,GAAI,mBAAmB,CAAC,KAAK,CAAC,WAAW,CAAY,CAAA;QAE3F,MAAM,oBAAoB,GAAG,aAAa,IAAI,sBAAsB;YAClE,CAAC,CAAC,mBAAmB,CAAC,sBAAsB,CAAC;YAC7C,CAAC,CAAC,IAAI,CAAA;QAER,MAAM,cAAc,GAAG;YACrB,oBAAoB;YACpB,SAAS;YACT,mBAAmB;SACpB;aACE,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;YACjB,uDAAuD;aACtD,GAAG,CAAC,CAAC,CAAY,EAAE,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAkB,CAAA;QAChE,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAE/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts b/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts
index f19cb15..6c1632c 100644
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts
+++ b/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FaceDetection } from '../classes/FaceDetection';
 import { NetInput, TNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts.map b/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts.map
index e5e628e..50a3abc 100644
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts.map
+++ b/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"SsdMobilenetv1.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/SsdMobilenetv1.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAOjD,OAAO,EAAE,sBAAsB,EAAyB,MAAM,yBAAyB,CAAC;AACxF,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAGpC,qBAAa,cAAe,SAAQ,aAAa,CAAC,SAAS,CAAC;;IAMnD,YAAY,CAAC,KAAK,EAAE,QAAQ;;;;IAwBtB,OAAO,CAAC,KAAK,EAAE,SAAS;;;;IAIxB,WAAW,CACtB,KAAK,EAAE,SAAS,EAChB,OAAO,GAAE,sBAA2B,GACnC,OAAO,CAAC,aAAa,EAAE,CAAC;IAqE3B,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
+{"version":3,"file":"SsdMobilenetv1.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/SsdMobilenetv1.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAOjD,OAAO,EAAE,sBAAsB,EAAyB,MAAM,yBAAyB,CAAC;AACxF,OAAO,EAAE,SAAS,EAAE,MAAM,SAAS,CAAC;AAGpC,qBAAa,cAAe,SAAQ,aAAa,CAAC,SAAS,CAAC;;IAMnD,YAAY,CAAC,KAAK,EAAE,QAAQ;;;;IAwBtB,OAAO,CAAC,KAAK,EAAE,SAAS;;;;IAIxB,WAAW,CACtB,KAAK,EAAE,SAAS,EAChB,OAAO,GAAE,sBAA2B,GACnC,OAAO,CAAC,aAAa,EAAE,CAAC;IAqE3B,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.js b/build/src/ssdMobilenetv1/SsdMobilenetv1.js
index 74b27a5..7e63c7a 100644
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.js
+++ b/build/src/ssdMobilenetv1/SsdMobilenetv1.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
 import { toNetInput } from '../dom';
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map b/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map
index 6f28c85..b0c93e1 100644
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map
+++ b/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts b/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts
index df8fcfd..61df356 100644
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts
+++ b/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { BoxPredictionParams } from './types';
 export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
     boxPredictionEncoding: tf.Tensor<tf.Rank>;
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts.map b/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts.map
index bb35ebd..78ef023 100644
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts.map
+++ b/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"boxPredictionLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAG9C,wBAAgB,kBAAkB,CAChC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB;;;EAoB5B"}
\ No newline at end of file
+{"version":3,"file":"boxPredictionLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAG9C,wBAAgB,kBAAkB,CAChC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB;;;EAoB5B"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.js b/build/src/ssdMobilenetv1/boxPredictionLayer.js
index 7018d0c..dbbe21f 100644
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.js
+++ b/build/src/ssdMobilenetv1/boxPredictionLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { convLayer } from '../common';
 export function boxPredictionLayer(x, params) {
     return tf.tidy(() => {
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.js.map b/build/src/ssdMobilenetv1/boxPredictionLayer.js.map
index 4201e38..23ec4e5 100644
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.js.map
+++ b/build/src/ssdMobilenetv1/boxPredictionLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,SAAS,EAAE,MAAM,WAAW,CAAC;AAItC,MAAM,UAAU,kBAAkB,CAChC,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,MAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,MAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB;YACrB,eAAe;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/boxPredictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,SAAS,EAAE,MAAM,WAAW,CAAC;AAItC,MAAM,UAAU,kBAAkB,CAChC,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,MAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,MAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB;YACrB,eAAe;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParams.js b/build/src/ssdMobilenetv1/extractParams.js
index 6de523c..61f1362 100644
--- a/build/src/ssdMobilenetv1/extractParams.js
+++ b/build/src/ssdMobilenetv1/extractParams.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { extractWeightsFactory } from '../common';
 function extractorsFactory(extractWeights, paramMappings) {
     function extractDepthwiseConvParams(numChannels, mappedPrefix) {
diff --git a/build/src/ssdMobilenetv1/extractParams.js.map b/build/src/ssdMobilenetv1/extractParams.js.map
index 3acc043..6c0d2d2 100644
--- a/build/src/ssdMobilenetv1/extractParams.js.map
+++ b/build/src/ssdMobilenetv1/extractParams.js.map
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
index 163969a..6299baa 100644
--- a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
+++ b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { NetParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
diff --git a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts.map b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts.map
index f8992bb..2ce572e 100644
--- a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAqE,YAAY,EAAE,MAAM,WAAW,CAAC;AAE5G,OAAO,EAAoC,SAAS,EAA8C,MAAM,SAAS,CAAC;AAwGlH,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CA2BtD"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAqE,YAAY,EAAE,MAAM,WAAW,CAAC;AAE5G,OAAO,EAAoC,SAAS,EAA8C,MAAM,SAAS,CAAC;AAwGlH,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,GAC3B;IAAE,MAAM,EAAE,SAAS,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CA2BtD"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.d.ts b/build/src/ssdMobilenetv1/mobileNetV1.d.ts
index 1d11181..bceeee0 100644
--- a/build/src/ssdMobilenetv1/mobileNetV1.d.ts
+++ b/build/src/ssdMobilenetv1/mobileNetV1.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { MobileNetV1 } from './types';
 export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
     out: tf.Tensor4D;
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.d.ts.map b/build/src/ssdMobilenetv1/mobileNetV1.d.ts.map
index 01df15d..a6fbca0 100644
--- a/build/src/ssdMobilenetv1/mobileNetV1.d.ts.map
+++ b/build/src/ssdMobilenetv1/mobileNetV1.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"mobileNetV1.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/mobileNetV1.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,WAAW,EAAE,MAAM,SAAS,CAAC;AA6BtC,wBAAgB,WAAW,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,WAAW,CAAC,MAAM;;;EA0CrE"}
\ No newline at end of file
+{"version":3,"file":"mobileNetV1.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/mobileNetV1.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,WAAW,EAAE,MAAM,SAAS,CAAC;AA6BtC,wBAAgB,WAAW,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,WAAW,CAAC,MAAM;;;EA0CrE"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.js b/build/src/ssdMobilenetv1/mobileNetV1.js
index 37f8bd2..766b0cf 100644
--- a/build/src/ssdMobilenetv1/mobileNetV1.js
+++ b/build/src/ssdMobilenetv1/mobileNetV1.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { pointwiseConvLayer } from './pointwiseConvLayer';
 const epsilon = 0.0010000000474974513;
 function depthwiseConvLayer(x, params, strides) {
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.js.map b/build/src/ssdMobilenetv1/mobileNetV1.js.map
index 1ca5e39..0b8977f 100644
--- a/build/src/ssdMobilenetv1/mobileNetV1.js.map
+++ b/build/src/ssdMobilenetv1/mobileNetV1.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/outputLayer.d.ts b/build/src/ssdMobilenetv1/outputLayer.d.ts
index f8ab338..3a7621d 100644
--- a/build/src/ssdMobilenetv1/outputLayer.d.ts
+++ b/build/src/ssdMobilenetv1/outputLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { OutputLayerParams } from './types';
 export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
     boxes: tf.Tensor2D[];
diff --git a/build/src/ssdMobilenetv1/outputLayer.d.ts.map b/build/src/ssdMobilenetv1/outputLayer.d.ts.map
index 7bfbafc..ad54e27 100644
--- a/build/src/ssdMobilenetv1/outputLayer.d.ts.map
+++ b/build/src/ssdMobilenetv1/outputLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"outputLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/outputLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AA+C5C,wBAAgB,WAAW,CACzB,cAAc,EAAE,EAAE,CAAC,QAAQ,EAC3B,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,MAAM,EAAE,iBAAiB;;;EAgC1B"}
\ No newline at end of file
+{"version":3,"file":"outputLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/outputLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AA+C5C,wBAAgB,WAAW,CACzB,cAAc,EAAE,EAAE,CAAC,QAAQ,EAC3B,gBAAgB,EAAE,EAAE,CAAC,QAAQ,EAC7B,MAAM,EAAE,iBAAiB;;;EAgC1B"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/outputLayer.js b/build/src/ssdMobilenetv1/outputLayer.js
index df80ca7..91ab727 100644
--- a/build/src/ssdMobilenetv1/outputLayer.js
+++ b/build/src/ssdMobilenetv1/outputLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 function getCenterCoordinatesAndSizesLayer(x) {
     const vec = tf.unstack(tf.transpose(x, [1, 0]));
     const sizes = [
diff --git a/build/src/ssdMobilenetv1/outputLayer.js.map b/build/src/ssdMobilenetv1/outputLayer.js.map
index d68ec4d..5fbfec5 100644
--- a/build/src/ssdMobilenetv1/outputLayer.js.map
+++ b/build/src/ssdMobilenetv1/outputLayer.js.map
@@ -1 +1 @@
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\ No newline at end of file
+{"version":3,"file":"outputLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/outputLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAKvC,SAAS,iCAAiC,CAAC,CAAc;IACvD,MAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAE/C,MAAM,KAAK,GAAG;QACZ,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB,CAAA;IAED,MAAM,OAAO,GAAG;QACd,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/C,CAAA;IAED,OAAO;QACL,KAAK;QACL,OAAO;KACR,CAAA;AACH,CAAC;AAED,SAAS,gBAAgB,CAAC,EAAe,EAAE,EAAe;IACxD,MAAM,EACJ,KAAK,EACL,OAAO,EACR,GAAG,iCAAiC,CAAC,EAAE,CAAC,CAAA;IAEzC,MAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAEhD,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,OAAO,EAAE,CAAC,SAAS,CACjB,EAAE,CAAC,KAAK,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;KAC3B,CAAC,EACF,CAAC,CAAC,EAAE,CAAC,CAAC,CACP,CAAA;AACH,CAAC;AAED,MAAM,UAAU,WAAW,CACzB,cAA2B,EAC3B,gBAA6B,EAC7B,MAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAEzC,IAAI,KAAK,GAAG,gBAAgB,CAC1B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,EAChF,EAAE,CAAC,OAAO,CAAC,cAAc,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CACnD,CAAA;QACD,KAAK,GAAG,EAAE,CAAC,OAAO,CAChB,KAAK,EACL,CAAC,SAAS,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,EAAE,CAAC,CAAC,CAC7C,CAAA;QAED,MAAM,gBAAgB,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACxF,IAAI,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAc,CAAA;QAE5E,MAAM,GAAG,EAAE,CAAC,OAAO,CACjB,MAAM,EACN,CAAC,SAAS,EAAE,MAAM,CAAC,KAAK,CAAC,CAAC,CAAW,CAAC,CACvC,CAAA;QAED,MAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,CAAkB,CAAA;QACvD,MAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,MAAM,CAAkB,CAAA;QAEzD,OAAO;YACL,KAAK,EAAE,YAAY;YACnB,MAAM,EAAE,aAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts b/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts
index 1782b95..e98c25d 100644
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts
+++ b/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { PointwiseConvParams } from './types';
 export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor4D;
 //# sourceMappingURL=pointwiseConvLayer.d.ts.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts.map b/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts.map
index 80f7f63..a9ed7e5 100644
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts.map
+++ b/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"pointwiseConvLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,kBAAkB,CAChC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB,EAC3B,OAAO,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,eAS1B"}
\ No newline at end of file
+{"version":3,"file":"pointwiseConvLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,kBAAkB,CAChC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB,EAC3B,OAAO,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,eAS1B"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.js b/build/src/ssdMobilenetv1/pointwiseConvLayer.js
index 7f5dace..3826de9 100644
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.js
+++ b/build/src/ssdMobilenetv1/pointwiseConvLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function pointwiseConvLayer(x, params, strides) {
     return tf.tidy(() => {
         let out = tf.conv2d(x, params.filters, strides, 'same');
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map b/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map
index 627f706..6882ebb 100644
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map
+++ b/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,UAAU,kBAAkB,CAChC,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,MAAM,UAAU,kBAAkB,CAChC,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/predictionLayer.d.ts b/build/src/ssdMobilenetv1/predictionLayer.d.ts
index 9f8662d..5368338 100644
--- a/build/src/ssdMobilenetv1/predictionLayer.d.ts
+++ b/build/src/ssdMobilenetv1/predictionLayer.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { PredictionLayerParams } from './types';
 export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
     boxPredictions: tf.Tensor4D;
diff --git a/build/src/ssdMobilenetv1/predictionLayer.d.ts.map b/build/src/ssdMobilenetv1/predictionLayer.d.ts.map
index 46d2ee5..3ce0447 100644
--- a/build/src/ssdMobilenetv1/predictionLayer.d.ts.map
+++ b/build/src/ssdMobilenetv1/predictionLayer.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"predictionLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,OAAO,EAAE,qBAAqB,EAAE,MAAM,SAAS,CAAC;AAEhD,wBAAgB,eAAe,CAC7B,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,EAAE,CAAC,QAAQ,EACnB,MAAM,EAAE,qBAAqB;;;EA2C9B"}
\ No newline at end of file
+{"version":3,"file":"predictionLayer.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAIvC,OAAO,EAAE,qBAAqB,EAAE,MAAM,SAAS,CAAC;AAEhD,wBAAgB,eAAe,CAC7B,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,EAAE,CAAC,QAAQ,EACnB,MAAM,EAAE,qBAAqB;;;EA2C9B"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/predictionLayer.js b/build/src/ssdMobilenetv1/predictionLayer.js
index 5e4814a..dd9dfd9 100644
--- a/build/src/ssdMobilenetv1/predictionLayer.js
+++ b/build/src/ssdMobilenetv1/predictionLayer.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { boxPredictionLayer } from './boxPredictionLayer';
 import { pointwiseConvLayer } from './pointwiseConvLayer';
 export function predictionLayer(x, conv11, params) {
diff --git a/build/src/ssdMobilenetv1/predictionLayer.js.map b/build/src/ssdMobilenetv1/predictionLayer.js.map
index 0305394..0a2f409 100644
--- a/build/src/ssdMobilenetv1/predictionLayer.js.map
+++ b/build/src/ssdMobilenetv1/predictionLayer.js.map
@@ -1 +1 @@
-{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAG1D,MAAM,UAAU,eAAe,CAC7B,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,KAAK,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,MAAM,cAAc,GAAG,kBAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,MAAM,cAAc,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,MAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,MAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc;YACd,gBAAgB;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAG1D,MAAM,UAAU,eAAe,CAC7B,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,KAAK,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,MAAM,cAAc,GAAG,kBAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,MAAM,cAAc,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,MAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,MAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc;YACd,gBAAgB;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/types.d.ts b/build/src/ssdMobilenetv1/types.d.ts
index 25ba550..eeb8614 100644
--- a/build/src/ssdMobilenetv1/types.d.ts
+++ b/build/src/ssdMobilenetv1/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvParams } from '../common';
 export declare type PointwiseConvParams = {
     filters: tf.Tensor4D;
diff --git a/build/src/ssdMobilenetv1/types.d.ts.map b/build/src/ssdMobilenetv1/types.d.ts.map
index b84f680..5d8d5c0 100644
--- a/build/src/ssdMobilenetv1/types.d.ts.map
+++ b/build/src/ssdMobilenetv1/types.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,UAAU,EAAE,MAAM,WAAW,CAAC;AAEvC,oBAAY,mBAAmB,GAAG;IAChC,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,iBAAiB,EAAE,EAAE,CAAC,QAAQ,CAAA;CAC/B,CAAA;AAED,yBAAiB,WAAW,CAAC;IAE3B,KAAY,mBAAmB,GAAG;QAChC,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;QACpB,gBAAgB,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC7B,iBAAiB,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC9B,eAAe,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC5B,mBAAmB,EAAE,EAAE,CAAC,QAAQ,CAAA;KACjC,CAAA;IAED,KAAY,cAAc,GAAG;QAC3B,cAAc,EAAE,mBAAmB,CAAA;QACnC,cAAc,EAAE,mBAAmB,CAAA;KACpC,CAAA;IAED,KAAY,MAAM,GAAG;QACnB,MAAM,EAAE,mBAAmB,CAAA;QAC3B,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;KACxB,CAAA;CAEF;AAED,oBAAY,mBAAmB,GAAG;IAChC,sBAAsB,EAAE,UAAU,CAAA;IAClC,eAAe,EAAE,UAAU,CAAA;CAC5B,CAAA;AAED,oBAAY,qBAAqB,GAAG;IAClC,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;CACrC,CAAA;AAED,oBAAY,iBAAiB,GAAG;IAC9B,SAAS,EAAE,EAAE,CAAC,QAAQ,CAAA;CACvB,CAAA;AAED,oBAAY,SAAS,GAAG;IACtB,WAAW,EAAE,WAAW,CAAC,MAAM,CAAC;IAChC,gBAAgB,EAAE,qBAAqB,CAAC;IACxC,YAAY,EAAE,iBAAiB,CAAA;CAChC,CAAA"}
\ No newline at end of file
+{"version":3,"file":"types.d.ts","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,UAAU,EAAE,MAAM,WAAW,CAAC;AAEvC,oBAAY,mBAAmB,GAAG;IAChC,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;IACpB,iBAAiB,EAAE,EAAE,CAAC,QAAQ,CAAA;CAC/B,CAAA;AAED,yBAAiB,WAAW,CAAC;IAE3B,KAAY,mBAAmB,GAAG;QAChC,OAAO,EAAE,EAAE,CAAC,QAAQ,CAAA;QACpB,gBAAgB,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC7B,iBAAiB,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC9B,eAAe,EAAE,EAAE,CAAC,QAAQ,CAAA;QAC5B,mBAAmB,EAAE,EAAE,CAAC,QAAQ,CAAA;KACjC,CAAA;IAED,KAAY,cAAc,GAAG;QAC3B,cAAc,EAAE,mBAAmB,CAAA;QACnC,cAAc,EAAE,mBAAmB,CAAA;KACpC,CAAA;IAED,KAAY,MAAM,GAAG;QACnB,MAAM,EAAE,mBAAmB,CAAA;QAC3B,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,MAAM,EAAE,cAAc,CAAA;QACtB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;QACvB,OAAO,EAAE,cAAc,CAAA;KACxB,CAAA;CAEF;AAED,oBAAY,mBAAmB,GAAG;IAChC,sBAAsB,EAAE,UAAU,CAAA;IAClC,eAAe,EAAE,UAAU,CAAA;CAC5B,CAAA;AAED,oBAAY,qBAAqB,GAAG;IAClC,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,MAAM,EAAE,mBAAmB,CAAA;IAC3B,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;IACpC,eAAe,EAAE,mBAAmB,CAAA;CACrC,CAAA;AAED,oBAAY,iBAAiB,GAAG;IAC9B,SAAS,EAAE,EAAE,CAAC,QAAQ,CAAA;CACvB,CAAA;AAED,oBAAY,SAAS,GAAG;IACtB,WAAW,EAAE,WAAW,CAAC,MAAM,CAAC;IAChC,gBAAgB,EAAE,qBAAqB,CAAC;IACxC,YAAY,EAAE,iBAAiB,CAAA;CAChC,CAAA"}
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts b/build/src/tinyFaceDetector/TinyFaceDetector.d.ts
index 6fc8aa7..2998567 100644
--- a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts
+++ b/build/src/tinyFaceDetector/TinyFaceDetector.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common';
 import { TNetInput } from '../dom';
diff --git a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts.map b/build/src/tinyFaceDetector/TinyFaceDetector.d.ts.map
index 3b895db..a7c9333 100644
--- a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts.map
+++ b/build/src/tinyFaceDetector/TinyFaceDetector.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"TinyFaceDetector.d.ts","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetector.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,aAAa,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AAClD,OAAO,EAAE,YAAY,EAAE,MAAM,WAAW,CAAC;AACzC,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,kBAAkB,EAAE,MAAM,eAAe,CAAC;AACnD,OAAO,EAAE,cAAc,EAAE,MAAM,8BAA8B,CAAC;AAC9D,OAAO,EAAE,mBAAmB,EAAE,MAAM,qBAAqB,CAAC;AAG1D,qBAAa,gBAAiB,SAAQ,cAAc;;IAgBlD,IAAW,OAAO,IAAI,KAAK,EAAE,CAE5B;IAEY,WAAW,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,EAAE,kBAAkB,GAAG,OAAO,CAAC,aAAa,EAAE,CAAC;IAKvG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc,GAAG;QAAE,MAAM,EAAE,mBAAmB,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;CAGnI"}
\ No newline at end of file
+{"version":3,"file":"TinyFaceDetector.d.ts","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetector.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,aAAa,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AAClD,OAAO,EAAE,YAAY,EAAE,MAAM,WAAW,CAAC;AACzC,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,kBAAkB,EAAE,MAAM,eAAe,CAAC;AACnD,OAAO,EAAE,cAAc,EAAE,MAAM,8BAA8B,CAAC;AAC9D,OAAO,EAAE,mBAAmB,EAAE,MAAM,qBAAqB,CAAC;AAG1D,qBAAa,gBAAiB,SAAQ,cAAc;;IAgBlD,IAAW,OAAO,IAAI,KAAK,EAAE,CAE5B;IAEY,WAAW,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,EAAE,kBAAkB,GAAG,OAAO,CAAC,aAAa,EAAE,CAAC;IAKvG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc,GAAG;QAAE,MAAM,EAAE,mBAAmB,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;CAGnI"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2.d.ts b/build/src/tinyYolov2/TinyYolov2.d.ts
index d1c6ee4..1bb0a32 100644
--- a/build/src/tinyYolov2/TinyYolov2.d.ts
+++ b/build/src/tinyYolov2/TinyYolov2.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common/types';
 import { TNetInput } from '../dom/types';
diff --git a/build/src/tinyYolov2/TinyYolov2.d.ts.map b/build/src/tinyYolov2/TinyYolov2.d.ts.map
index 3108af1..cbd5866 100644
--- a/build/src/tinyYolov2/TinyYolov2.d.ts.map
+++ b/build/src/tinyYolov2/TinyYolov2.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"TinyYolov2.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/TinyYolov2.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,aAAa,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AAClD,OAAO,EAAE,YAAY,EAAE,MAAM,iBAAiB,CAAC;AAC/C,OAAO,EAAE,SAAS,EAAE,MAAM,cAAc,CAAC;AASzC,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,kBAAkB,EAAE,MAAM,qBAAqB,CAAC;AACzD,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,qBAAa,UAAW,SAAQ,cAAc;gBAEhC,kBAAkB,GAAE,OAAc;IAoB9C,IAAW,kBAAkB,IAAI,OAAO,CAEvC;IAED,IAAW,OAAO,IAAI,KAAK,EAAE,CAE5B;IAEY,WAAW,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,EAAE,kBAAkB,GAAG,OAAO,CAAC,aAAa,EAAE,CAAC;IAKvG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc,GAAG;QAAE,MAAM,EAAE,mBAAmB,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;CAGnI"}
\ No newline at end of file
+{"version":3,"file":"TinyYolov2.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/TinyYolov2.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,aAAa,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AAClD,OAAO,EAAE,YAAY,EAAE,MAAM,iBAAiB,CAAC;AAC/C,OAAO,EAAE,SAAS,EAAE,MAAM,cAAc,CAAC;AASzC,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,kBAAkB,EAAE,MAAM,qBAAqB,CAAC;AACzD,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,qBAAa,UAAW,SAAQ,cAAc;gBAEhC,kBAAkB,GAAE,OAAc;IAoB9C,IAAW,kBAAkB,IAAI,OAAO,CAEvC;IAED,IAAW,OAAO,IAAI,KAAK,EAAE,CAE5B;IAEY,WAAW,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,EAAE,kBAAkB,GAAG,OAAO,CAAC,aAAa,EAAE,CAAC;IAKvG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc,GAAG;QAAE,MAAM,EAAE,mBAAmB,CAAC;QAAC,aAAa,EAAE,YAAY,EAAE,CAAA;KAAE;CAGnI"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2Base.d.ts b/build/src/tinyYolov2/TinyYolov2Base.d.ts
index abd6a84..903b370 100644
--- a/build/src/tinyYolov2/TinyYolov2Base.d.ts
+++ b/build/src/tinyYolov2/TinyYolov2Base.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Dimensions } from '../classes/Dimensions';
 import { ObjectDetection } from '../classes/ObjectDetection';
 import { NetInput } from '../dom/NetInput';
diff --git a/build/src/tinyYolov2/TinyYolov2Base.d.ts.map b/build/src/tinyYolov2/TinyYolov2Base.d.ts.map
index de906ed..471817b 100644
--- a/build/src/tinyYolov2/TinyYolov2Base.d.ts.map
+++ b/build/src/tinyYolov2/TinyYolov2Base.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"TinyYolov2Base.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/TinyYolov2Base.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,UAAU,EAAE,MAAM,uBAAuB,CAAC;AACnD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAI7D,OAAO,EAAE,QAAQ,EAAE,MAAM,iBAAiB,CAAC;AAC3C,OAAO,EAAE,SAAS,EAAE,MAAM,cAAc,CAAC;AACzC,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAIjD,OAAO,EAAE,gBAAgB,EAAkB,MAAM,UAAU,CAAC;AAM5D,OAAO,EAAE,kBAAkB,EAAqB,MAAM,qBAAqB,CAAC;AAC5E,OAAO,EAAE,0BAA0B,EAAE,eAAe,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE3F,qBAAa,cAAe,SAAQ,aAAa,CAAC,mBAAmB,CAAC;IAEpE,OAAc,oBAAoB,WAEjC;IAED,OAAO,CAAC,OAAO,CAAkB;gBAErB,MAAM,EAAE,gBAAgB;IAMpC,IAAW,MAAM,IAAI,gBAAgB,CAEpC;IAED,IAAW,eAAe,IAAI,OAAO,CAEpC;IAED,IAAW,eAAe,IAAI,MAAM,CAEnC;IAEM,aAAa,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,0BAA0B,GAAG,EAAE,CAAC,QAAQ;IAoB9E,YAAY,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,GAAG,EAAE,CAAC,QAAQ;IAsBlE,YAAY,CAAC,KAAK,EAAE,QAAQ,EAAE,SAAS,EAAE,MAAM,GAAG,EAAE,CAAC,QAAQ;IAuBvD,OAAO,CAAC,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,MAAM,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAIlE,MAAM,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,GAAE,kBAAuB,GAAG,OAAO,CAAC,eAAe,EAAE,CAAC;IA0CzG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;cAU7B,YAAY,CAC1B,YAAY,EAAE,EAAE,CAAC,QAAQ,EACzB,mBAAmB,EAAE,UAAU,EAC/B,cAAc,CAAC,EAAE,MAAM;YAgEX,qBAAqB;CAWpC"}
\ No newline at end of file
+{"version":3,"file":"TinyYolov2Base.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/TinyYolov2Base.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,UAAU,EAAE,MAAM,uBAAuB,CAAC;AACnD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAI7D,OAAO,EAAE,QAAQ,EAAE,MAAM,iBAAiB,CAAC;AAC3C,OAAO,EAAE,SAAS,EAAE,MAAM,cAAc,CAAC;AACzC,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAIjD,OAAO,EAAE,gBAAgB,EAAkB,MAAM,UAAU,CAAC;AAM5D,OAAO,EAAE,kBAAkB,EAAqB,MAAM,qBAAqB,CAAC;AAC5E,OAAO,EAAE,0BAA0B,EAAE,eAAe,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE3F,qBAAa,cAAe,SAAQ,aAAa,CAAC,mBAAmB,CAAC;IAEpE,OAAc,oBAAoB,WAEjC;IAED,OAAO,CAAC,OAAO,CAAkB;gBAErB,MAAM,EAAE,gBAAgB;IAMpC,IAAW,MAAM,IAAI,gBAAgB,CAEpC;IAED,IAAW,eAAe,IAAI,OAAO,CAEpC;IAED,IAAW,eAAe,IAAI,MAAM,CAEnC;IAEM,aAAa,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,0BAA0B,GAAG,EAAE,CAAC,QAAQ;IAoB9E,YAAY,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,eAAe,GAAG,EAAE,CAAC,QAAQ;IAsBlE,YAAY,CAAC,KAAK,EAAE,QAAQ,EAAE,SAAS,EAAE,MAAM,GAAG,EAAE,CAAC,QAAQ;IAuBvD,OAAO,CAAC,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,MAAM,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAIlE,MAAM,CAAC,KAAK,EAAE,SAAS,EAAE,aAAa,GAAE,kBAAuB,GAAG,OAAO,CAAC,eAAe,EAAE,CAAC;IA0CzG,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;cAU7B,YAAY,CAC1B,YAAY,EAAE,EAAE,CAAC,QAAQ,EACzB,mBAAmB,EAAE,UAAU,EAC/B,cAAc,CAAC,EAAE,MAAM;YAgEX,qBAAqB;CAWpC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2Base.js b/build/src/tinyYolov2/TinyYolov2Base.js
index b7bd23c..e720dcf 100644
--- a/build/src/tinyYolov2/TinyYolov2Base.js
+++ b/build/src/tinyYolov2/TinyYolov2Base.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { BoundingBox } from '../classes/BoundingBox';
 import { ObjectDetection } from '../classes/ObjectDetection';
 import { convLayer } from '../common';
diff --git a/build/src/tinyYolov2/TinyYolov2Base.js.map b/build/src/tinyYolov2/TinyYolov2Base.js.map
index 2329465..275b150 100644
--- a/build/src/tinyYolov2/TinyYolov2Base.js.map
+++ b/build/src/tinyYolov2/TinyYolov2Base.js.map
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/build/src/tinyYolov2/convWithBatchNorm.d.ts b/build/src/tinyYolov2/convWithBatchNorm.d.ts
index 02b0352..0f06488 100644
--- a/build/src/tinyYolov2/convWithBatchNorm.d.ts
+++ b/build/src/tinyYolov2/convWithBatchNorm.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvWithBatchNorm } from './types';
 export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D;
 //# sourceMappingURL=convWithBatchNorm.d.ts.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/convWithBatchNorm.d.ts.map b/build/src/tinyYolov2/convWithBatchNorm.d.ts.map
index 93c843f..e6b6da6 100644
--- a/build/src/tinyYolov2/convWithBatchNorm.d.ts.map
+++ b/build/src/tinyYolov2/convWithBatchNorm.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"convWithBatchNorm.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE5C,wBAAgB,iBAAiB,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAWxF"}
\ No newline at end of file
+{"version":3,"file":"convWithBatchNorm.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,iBAAiB,EAAE,MAAM,SAAS,CAAC;AAE5C,wBAAgB,iBAAiB,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAWxF"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/convWithBatchNorm.js b/build/src/tinyYolov2/convWithBatchNorm.js
index 3f8e503..0cbc0e7 100644
--- a/build/src/tinyYolov2/convWithBatchNorm.js
+++ b/build/src/tinyYolov2/convWithBatchNorm.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { leaky } from './leaky';
 export function convWithBatchNorm(x, params) {
     return tf.tidy(() => {
diff --git a/build/src/tinyYolov2/convWithBatchNorm.js.map b/build/src/tinyYolov2/convWithBatchNorm.js.map
index 1d12831..e2efcbe 100644
--- a/build/src/tinyYolov2/convWithBatchNorm.js.map
+++ b/build/src/tinyYolov2/convWithBatchNorm.js.map
@@ -1 +1 @@
-{"version":3,"file":"convWithBatchNorm.js","sourceRoot":"","sources":["../../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAGhC,MAAM,UAAU,iBAAiB,CAAC,CAAc,EAAE,MAAyB;IACzE,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAA;QAChC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA;QACpC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEnC,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"convWithBatchNorm.js","sourceRoot":"","sources":["../../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAGhC,MAAM,UAAU,iBAAiB,CAAC,CAAc,EAAE,MAAyB;IACzE,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAA;QAChC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA;QACpC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEnC,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts b/build/src/tinyYolov2/depthwiseSeparableConv.d.ts
index cffd719..b479997 100644
--- a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts
+++ b/build/src/tinyYolov2/depthwiseSeparableConv.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { SeparableConvParams } from '../common/types';
 export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D;
 //# sourceMappingURL=depthwiseSeparableConv.d.ts.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts.map b/build/src/tinyYolov2/depthwiseSeparableConv.d.ts.map
index d7f67e3..d3b7dc9 100644
--- a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts.map
+++ b/build/src/tinyYolov2/depthwiseSeparableConv.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"depthwiseSeparableConv.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,iBAAiB,CAAC;AAGtD,wBAAgB,sBAAsB,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAS/F"}
\ No newline at end of file
+{"version":3,"file":"depthwiseSeparableConv.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,mBAAmB,EAAE,MAAM,iBAAiB,CAAC;AAGtD,wBAAgB,sBAAsB,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,EAAE,MAAM,EAAE,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAS/F"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.js b/build/src/tinyYolov2/depthwiseSeparableConv.js
index a8e8fd4..b5e20bf 100644
--- a/build/src/tinyYolov2/depthwiseSeparableConv.js
+++ b/build/src/tinyYolov2/depthwiseSeparableConv.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { leaky } from './leaky';
 export function depthwiseSeparableConv(x, params) {
     return tf.tidy(() => {
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.js.map b/build/src/tinyYolov2/depthwiseSeparableConv.js.map
index f3c53f9..f33a61c 100644
--- a/build/src/tinyYolov2/depthwiseSeparableConv.js.map
+++ b/build/src/tinyYolov2/depthwiseSeparableConv.js.map
@@ -1 +1 @@
-{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/tinyYolov2/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAEhC,MAAM,UAAU,sBAAsB,CAAC,CAAc,EAAE,MAA2B;IAChF,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAChG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAE9B,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/tinyYolov2/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAEhC,MAAM,UAAU,sBAAsB,CAAC,CAAc,EAAE,MAA2B;IAChF,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAChG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAE9B,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParams.js b/build/src/tinyYolov2/extractParams.js
index e56fcbd..206c926 100644
--- a/build/src/tinyYolov2/extractParams.js
+++ b/build/src/tinyYolov2/extractParams.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { extractConvParamsFactory } from '../common';
 import { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
 import { extractWeightsFactory } from '../common/extractWeightsFactory';
diff --git a/build/src/tinyYolov2/extractParams.js.map b/build/src/tinyYolov2/extractParams.js.map
index 971e75c..79b370c 100644
--- a/build/src/tinyYolov2/extractParams.js.map
+++ b/build/src/tinyYolov2/extractParams.js.map
@@ -1 +1 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,wBAAwB,EAAE,MAAM,WAAW,CAAC;AACrD,OAAO,EAAE,iCAAiC,EAAE,MAAM,6CAA6C,CAAC;AAChG,OAAO,EAAE,qBAAqB,EAAE,MAAM,iCAAiC,CAAC;AAKxE,SAAS,iBAAiB,CAAC,cAAsC,EAAE,aAA6B;IAE9F,MAAM,iBAAiB,GAAG,wBAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,SAAS,sBAAsB,CAAC,IAAY,EAAE,YAAoB;QAEhE,MAAM,GAAG,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC7C,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAEjD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,MAAM,EAAE,EACpC,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,CACzC,CAAA;QAED,OAAO,EAAE,GAAG,EAAE,OAAO,EAAE,CAAA;IACzB,CAAC;IAED,SAAS,8BAA8B,CAAC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEnG,MAAM,IAAI,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAE,GAAG,YAAY,OAAO,CAAC,CAAA;QAClF,MAAM,EAAE,GAAG,sBAAsB,CAAC,WAAW,EAAE,GAAG,YAAY,KAAK,CAAC,CAAA;QAEpE,OAAO,EAAE,IAAI,EAAE,EAAE,EAAE,CAAA;IACrB,CAAC;IACD,MAAM,0BAA0B,GAAG,iCAAiC,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEnG,OAAO;QACL,iBAAiB;QACjB,8BAA8B;QAC9B,0BAA0B;KAC3B,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,aAAa,CAC3B,OAAqB,EACrB,MAAwB,EACxB,eAAuB,EACvB,WAAqB;IAGrB,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,iBAAiB,EACjB,8BAA8B,EAC9B,0BAA0B,EAC3B,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,IAAI,MAA2B,CAAA;IAE/B,IAAI,MAAM,CAAC,kBAAkB,EAAE;QAC7B,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QAExD,MAAM,KAAK,GAAG,MAAM,CAAC,kBAAkB;YACrC,CAAC,CAAC,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC;YACvC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC/C,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,IAAI,EAAE,IAAI,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QAChF,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;SAAM;QACL,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QACxD,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAE,CAAA;QAC9D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QACpE,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;IAED,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAGD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,wBAAwB,EAAE,MAAM,WAAW,CAAC;AACrD,OAAO,EAAE,iCAAiC,EAAE,MAAM,6CAA6C,CAAC;AAChG,OAAO,EAAE,qBAAqB,EAAE,MAAM,iCAAiC,CAAC;AAKxE,SAAS,iBAAiB,CAAC,cAAsC,EAAE,aAA6B;IAE9F,MAAM,iBAAiB,GAAG,wBAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,SAAS,sBAAsB,CAAC,IAAY,EAAE,YAAoB;QAEhE,MAAM,GAAG,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC7C,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAEjD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,MAAM,EAAE,EACpC,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,CACzC,CAAA;QAED,OAAO,EAAE,GAAG,EAAE,OAAO,EAAE,CAAA;IACzB,CAAC;IAED,SAAS,8BAA8B,CAAC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEnG,MAAM,IAAI,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAE,GAAG,YAAY,OAAO,CAAC,CAAA;QAClF,MAAM,EAAE,GAAG,sBAAsB,CAAC,WAAW,EAAE,GAAG,YAAY,KAAK,CAAC,CAAA;QAEpE,OAAO,EAAE,IAAI,EAAE,EAAE,EAAE,CAAA;IACrB,CAAC;IACD,MAAM,0BAA0B,GAAG,iCAAiC,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEnG,OAAO;QACL,iBAAiB;QACjB,8BAA8B;QAC9B,0BAA0B;KAC3B,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,aAAa,CAC3B,OAAqB,EACrB,MAAwB,EACxB,eAAuB,EACvB,WAAqB;IAGrB,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,iBAAiB,EACjB,8BAA8B,EAC9B,0BAA0B,EAC3B,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,IAAI,MAA2B,CAAA;IAE/B,IAAI,MAAM,CAAC,kBAAkB,EAAE;QAC7B,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QAExD,MAAM,KAAK,GAAG,MAAM,CAAC,kBAAkB;YACrC,CAAC,CAAC,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC;YACvC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC/C,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,IAAI,EAAE,IAAI,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QAChF,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;SAAM;QACL,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QACxD,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAE,CAAA;QAC9D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QACpE,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;IAED,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAGD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts b/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts
index e0747f6..a7798cf 100644
--- a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts
+++ b/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common/types';
 import { TinyYolov2Config } from './config';
 import { TinyYolov2NetParams } from './types';
diff --git a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts.map b/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts.map
index 939461d..e2e3796 100644
--- a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAM5C,OAAO,EAAE,YAAY,EAAE,MAAM,iBAAiB,CAAC;AAC/C,OAAO,EAAE,gBAAgB,EAAE,MAAM,UAAU,CAAC;AAC5C,OAAO,EAAgC,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAkC5E,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,EAC5B,MAAM,EAAE,gBAAgB,GACvB;IAAE,MAAM,EAAE,mBAAmB,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CA0ChE"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAMvC,OAAO,EAAE,YAAY,EAAE,MAAM,iBAAiB,CAAC;AAC/C,OAAO,EAAE,gBAAgB,EAAE,MAAM,UAAU,CAAC;AAC5C,OAAO,EAAgC,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAkC5E,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,EAC5B,MAAM,EAAE,gBAAgB,GACvB;IAAE,MAAM,EAAE,mBAAmB,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CA0ChE"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/leaky.d.ts b/build/src/tinyYolov2/leaky.d.ts
index dc35656..4f266d3 100644
--- a/build/src/tinyYolov2/leaky.d.ts
+++ b/build/src/tinyYolov2/leaky.d.ts
@@ -1,3 +1,3 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export declare function leaky(x: tf.Tensor4D): tf.Tensor4D;
 //# sourceMappingURL=leaky.d.ts.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/leaky.d.ts.map b/build/src/tinyYolov2/leaky.d.ts.map
index 246ab2a..a09d3d7 100644
--- a/build/src/tinyYolov2/leaky.d.ts.map
+++ b/build/src/tinyYolov2/leaky.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"leaky.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,wBAAgB,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAMjD"}
\ No newline at end of file
+{"version":3,"file":"leaky.d.ts","sourceRoot":"","sources":["../../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,wBAAgB,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAMjD"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/leaky.js b/build/src/tinyYolov2/leaky.js
index 37b63f6..18781ae 100644
--- a/build/src/tinyYolov2/leaky.js
+++ b/build/src/tinyYolov2/leaky.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 export function leaky(x) {
     return tf.tidy(() => {
         const min = tf.mul(x, tf.scalar(0.10000000149011612));
diff --git a/build/src/tinyYolov2/leaky.js.map b/build/src/tinyYolov2/leaky.js.map
index cbaebff..0b74265 100644
--- a/build/src/tinyYolov2/leaky.js.map
+++ b/build/src/tinyYolov2/leaky.js.map
@@ -1 +1 @@
-{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,MAAM,UAAU,KAAK,CAAC,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAA;QACrD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAA;QAC3C,2BAA2B;IAC7B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,MAAM,UAAU,KAAK,CAAC,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAA;QACrD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAA;QAC3C,2BAA2B;IAC7B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/types.d.ts b/build/src/tinyYolov2/types.d.ts
index fabac66..f7792d2 100644
--- a/build/src/tinyYolov2/types.d.ts
+++ b/build/src/tinyYolov2/types.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ConvParams } from '../common';
 import { SeparableConvParams } from '../common/types';
 export declare type BatchNorm = {
diff --git a/build/src/tinyYolov2/types.d.ts.map b/build/src/tinyYolov2/types.d.ts.map
index 9587562..0f631d9 100644
--- a/build/src/tinyYolov2/types.d.ts.map
+++ b/build/src/tinyYolov2/types.d.ts.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/utils/index.d.ts b/build/src/utils/index.d.ts
index 1fee00b..696b796 100644
--- a/build/src/utils/index.d.ts
+++ b/build/src/utils/index.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Point } from '../classes';
 import { Dimensions, IDimensions } from '../classes/Dimensions';
 export declare function isTensor(tensor: any, dim: number): boolean;
diff --git a/build/src/utils/index.d.ts.map b/build/src/utils/index.d.ts.map
index f7c3a0f..a4c3dbc 100644
--- a/build/src/utils/index.d.ts.map
+++ b/build/src/utils/index.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../../src/utils/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AACnC,OAAO,EAAE,UAAU,EAAE,WAAW,EAAE,MAAM,uBAAuB,CAAC;AAEhE,wBAAgB,QAAQ,CAAC,MAAM,EAAE,GAAG,EAAE,GAAG,EAAE,MAAM,WAEhD;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,OAAO,CAAC,GAAG,EAAE,MAAM,WAElC;AAED,wBAAgB,MAAM,CAAC,GAAG,EAAE,MAAM,WAEjC;AAED,wBAAgB,KAAK,CAAC,GAAG,EAAE,MAAM,EAAE,IAAI,GAAE,MAAU,UAGlD;AAED,wBAAgB,YAAY,CAAC,GAAG,EAAE,GAAG,GAAG,OAAO,CAE9C;AAED,wBAAgB,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,WAAW,EAAE,SAAS,EAAE,MAAM,cAG1F;AAED,wBAAgB,cAAc,CAAC,GAAG,EAAE,KAAK,EAAE,GAAG,KAAK,CAGlD;AAED,wBAAgB,KAAK,CAAC,GAAG,EAAE,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,IAAI,EAAE,MAAM,GAAG,MAAM,EAAE,CAExE;AAED,wBAAgB,aAAa,CAAC,GAAG,EAAE,GAAG,WAErC;AAED,wBAAgB,kBAAkB,CAAC,GAAG,EAAE,GAAG,WAE1C"}
\ No newline at end of file
+{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../../src/utils/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AACnC,OAAO,EAAE,UAAU,EAAE,WAAW,EAAE,MAAM,uBAAuB,CAAC;AAEhE,wBAAgB,QAAQ,CAAC,MAAM,EAAE,GAAG,EAAE,GAAG,EAAE,MAAM,WAEhD;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,UAAU,CAAC,MAAM,EAAE,GAAG,GAAG,MAAM,IAAI,EAAE,CAAC,QAAQ,CAE7D;AAED,wBAAgB,OAAO,CAAC,GAAG,EAAE,MAAM,WAElC;AAED,wBAAgB,MAAM,CAAC,GAAG,EAAE,MAAM,WAEjC;AAED,wBAAgB,KAAK,CAAC,GAAG,EAAE,MAAM,EAAE,IAAI,GAAE,MAAU,UAGlD;AAED,wBAAgB,YAAY,CAAC,GAAG,EAAE,GAAG,GAAG,OAAO,CAE9C;AAED,wBAAgB,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,WAAW,EAAE,SAAS,EAAE,MAAM,cAG1F;AAED,wBAAgB,cAAc,CAAC,GAAG,EAAE,KAAK,EAAE,GAAG,KAAK,CAGlD;AAED,wBAAgB,KAAK,CAAC,GAAG,EAAE,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,IAAI,EAAE,MAAM,GAAG,MAAM,EAAE,CAExE;AAED,wBAAgB,aAAa,CAAC,GAAG,EAAE,GAAG,WAErC;AAED,wBAAgB,kBAAkB,CAAC,GAAG,EAAE,GAAG,WAE1C"}
\ No newline at end of file
diff --git a/build/src/utils/index.js b/build/src/utils/index.js
index f591532..2448464 100644
--- a/build/src/utils/index.js
+++ b/build/src/utils/index.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { Point } from '../classes';
 import { Dimensions } from '../classes/Dimensions';
 export function isTensor(tensor, dim) {
diff --git a/build/src/utils/index.js.map b/build/src/utils/index.js.map
index 3e51308..65fb76a 100644
--- a/build/src/utils/index.js.map
+++ b/build/src/utils/index.js.map
@@ -1 +1 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/utils/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AACnC,OAAO,EAAE,UAAU,EAAe,MAAM,uBAAuB,CAAC;AAEhE,MAAM,UAAU,QAAQ,CAAC,MAAW,EAAE,GAAW;IAC/C,OAAO,MAAM,YAAY,EAAE,CAAC,MAAM,IAAI,MAAM,CAAC,KAAK,CAAC,MAAM,KAAK,GAAG,CAAA;AACnE,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,OAAO,CAAC,GAAW;IACjC,OAAO,GAAG,GAAG,CAAC,KAAK,CAAC,CAAA;AACtB,CAAC;AAED,MAAM,UAAU,MAAM,CAAC,GAAW;IAChC,OAAO,GAAG,GAAG,CAAC,KAAK,CAAC,CAAA;AACtB,CAAC;AAED,MAAM,UAAU,KAAK,CAAC,GAAW,EAAE,OAAe,CAAC;IACjD,MAAM,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,EAAE,EAAE,IAAI,CAAC,CAAA;IAC5B,OAAO,IAAI,CAAC,KAAK,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,CAAC,CAAA;AAChC,CAAC;AAED,MAAM,UAAU,YAAY,CAAC,GAAQ;IACnC,OAAO,GAAG,IAAI,GAAG,CAAC,KAAK,IAAI,GAAG,CAAC,MAAM,CAAA;AACvC,CAAC;AAED,MAAM,UAAU,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAe,EAAE,SAAiB;IACzF,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;IACjD,OAAO,IAAI,UAAU,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,GAAG,KAAK,CAAC,EAAE,IAAI,CAAC,KAAK,CAAC,MAAM,GAAG,KAAK,CAAC,CAAC,CAAA;AAC9E,CAAC;AAED,MAAM,UAAU,cAAc,CAAC,GAAY;IACzC,OAAO,GAAG,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,IAAI,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;SACzD,GAAG,CAAC,IAAI,KAAK,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,MAAM,CAAC,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,UAAU,KAAK,CAAC,GAAW,EAAE,KAAa,EAAE,IAAY;IAC5D,OAAO,KAAK,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,KAAK,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAA;AAC7D,CAAC;AAED,MAAM,UAAU,aAAa,CAAC,GAAQ;IACpC,OAAO,CAAC,CAAC,GAAG,IAAI,GAAG,KAAK,QAAQ,IAAI,GAAG,KAAK,CAAC,QAAQ,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,IAAI,GAAG,KAAK,CAAC,CAAA;AACnF,CAAC;AAED,MAAM,UAAU,kBAAkB,CAAC,GAAQ;IACzC,OAAO,aAAa,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,GAAG,IAAI,GAAG,IAAI,GAAG,CAAA;AACrD,CAAC"}
\ No newline at end of file
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\ No newline at end of file
diff --git a/build/src/xception/TinyXception.d.ts b/build/src/xception/TinyXception.d.ts
index 705992a..0ff386d 100644
--- a/build/src/xception/TinyXception.d.ts
+++ b/build/src/xception/TinyXception.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { NetInput, TNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { TinyXceptionParams } from './types';
diff --git a/build/src/xception/TinyXception.d.ts.map b/build/src/xception/TinyXception.d.ts.map
index 79266e2..cc1f672 100644
--- a/build/src/xception/TinyXception.d.ts.map
+++ b/build/src/xception/TinyXception.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"TinyXception.d.ts","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAG5C,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAyC,kBAAkB,EAAE,MAAM,SAAS,CAAC;AAuBpF,qBAAa,YAAa,SAAQ,aAAa,CAAC,kBAAkB,CAAC;IAEjE,OAAO,CAAC,cAAc,CAAQ;gBAElB,aAAa,EAAE,MAAM;IAK1B,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IA2BpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
+{"version":3,"file":"TinyXception.d.ts","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,QAAQ,EAAE,SAAS,EAAc,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AAKjD,OAAO,EAAyC,kBAAkB,EAAE,MAAM,SAAS,CAAC;AAuBpF,qBAAa,YAAa,SAAQ,aAAa,CAAC,kBAAkB,CAAC;IAEjE,OAAO,CAAC,cAAc,CAAQ;gBAElB,aAAa,EAAE,MAAM;IAK1B,YAAY,CAAC,KAAK,EAAE,QAAQ,GAAG,EAAE,CAAC,QAAQ;IA2BpC,OAAO,CAAC,KAAK,EAAE,SAAS,GAAG,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC;IAI5D,SAAS,CAAC,mBAAmB,IAAI,MAAM;IAIvC,SAAS,CAAC,0BAA0B,CAAC,SAAS,EAAE,EAAE,CAAC,cAAc;;;;IAIjE,SAAS,CAAC,aAAa,CAAC,OAAO,EAAE,YAAY;;;;CAG9C"}
\ No newline at end of file
diff --git a/build/src/xception/TinyXception.js b/build/src/xception/TinyXception.js
index d53a5ce..e8e834e 100644
--- a/build/src/xception/TinyXception.js
+++ b/build/src/xception/TinyXception.js
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { depthwiseSeparableConv } from '../common';
 import { toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/build/src/xception/TinyXception.js.map b/build/src/xception/TinyXception.js.map
index f9e3f75..da111d4 100644
--- a/build/src/xception/TinyXception.js.map
+++ b/build/src/xception/TinyXception.js.map
@@ -1 +1 @@
-{"version":3,"file":"TinyXception.js","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAc,sBAAsB,EAAE,MAAM,WAAW,CAAC;AAC/D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,SAAS,IAAI,CAAC,CAAc,EAAE,MAAkB,EAAE,MAAwB;IACxE,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,MAAM,EAAE,MAAM,CAAC,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;AAC1E,CAAC;AAED,SAAS,cAAc,CAAC,CAAc,EAAE,MAA4B,EAAE,kBAA2B,IAAI;IACnG,IAAI,GAAG,GAAG,eAAe,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1C,GAAG,GAAG,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACjE,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAG,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC3E,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;IAC7C,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,EAAG,MAAM,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1D,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,SAAS,SAAS,CAAC,CAAc,EAAE,MAAuB;IACxD,IAAI,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC5E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,OAAO,YAAa,SAAQ,aAAiC;IAIjE,YAAY,aAAqB;QAC/B,KAAK,CAAC,cAAc,CAAC,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;SAC9D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,EAAE,MAAM,CAAC,UAAU,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACtE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,EAAE,KAAK,CAAC,CAAA;YACrE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,CAAC,CAAA;YAE9D,KAAK,CAAC,IAAI,CAAC,cAAc,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;gBAC/C,GAAG,GAAG,SAAS,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAC,CAAA;YAEF,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,eAAe,CAAC,CAAA;YAC3D,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACnF,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,qBAAqB,CAAA;IAC9B,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACnE,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACpD,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"TinyXception.js","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAAc,sBAAsB,EAAE,MAAM,WAAW,CAAC;AAC/D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,SAAS,IAAI,CAAC,CAAc,EAAE,MAAkB,EAAE,MAAwB;IACxE,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,MAAM,EAAE,MAAM,CAAC,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;AAC1E,CAAC;AAED,SAAS,cAAc,CAAC,CAAc,EAAE,MAA4B,EAAE,kBAA2B,IAAI;IACnG,IAAI,GAAG,GAAG,eAAe,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1C,GAAG,GAAG,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACjE,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAG,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC3E,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;IAC7C,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,EAAG,MAAM,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1D,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,SAAS,SAAS,CAAC,CAAc,EAAE,MAAuB;IACxD,IAAI,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC5E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,OAAO,YAAa,SAAQ,aAAiC;IAIjE,YAAY,aAAqB;QAC/B,KAAK,CAAC,cAAc,CAAC,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;SAC9D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,EAAE,MAAM,CAAC,UAAU,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACtE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,EAAE,KAAK,CAAC,CAAA;YACrE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,CAAC,CAAA;YAE9D,KAAK,CAAC,IAAI,CAAC,cAAc,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;gBAC/C,GAAG,GAAG,SAAS,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAC,CAAA;YAEF,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,eAAe,CAAC,CAAA;YAC3D,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACnF,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,qBAAqB,CAAA;IAC9B,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACnE,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACpD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/xception/extractParamsFromWeigthMap.d.ts b/build/src/xception/extractParamsFromWeigthMap.d.ts
index 58524bc..b9c2335 100644
--- a/build/src/xception/extractParamsFromWeigthMap.d.ts
+++ b/build/src/xception/extractParamsFromWeigthMap.d.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import { ParamMapping } from '../common';
 import { TinyXceptionParams } from './types';
 export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap, numMainBlocks: number): {
diff --git a/build/src/xception/extractParamsFromWeigthMap.d.ts.map b/build/src/xception/extractParamsFromWeigthMap.d.ts.map
index 0945103..d90060c 100644
--- a/build/src/xception/extractParamsFromWeigthMap.d.ts.map
+++ b/build/src/xception/extractParamsFromWeigthMap.d.ts.map
@@ -1 +1 @@
-{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/xception/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAIL,YAAY,EACb,MAAM,WAAW,CAAC;AAGnB,OAAO,EAAyC,kBAAkB,EAAE,MAAM,SAAS,CAAC;AAmCpF,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,EAC5B,aAAa,EAAE,MAAM,GACpB;IAAE,MAAM,EAAE,kBAAkB,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAqC/D"}
\ No newline at end of file
+{"version":3,"file":"extractParamsFromWeigthMap.d.ts","sourceRoot":"","sources":["../../../src/xception/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAEvC,OAAO,EAIL,YAAY,EACb,MAAM,WAAW,CAAC;AAGnB,OAAO,EAAyC,kBAAkB,EAAE,MAAM,SAAS,CAAC;AAmCpF,wBAAgB,0BAA0B,CACxC,SAAS,EAAE,EAAE,CAAC,cAAc,EAC5B,aAAa,EAAE,MAAM,GACpB;IAAE,MAAM,EAAE,kBAAkB,CAAC;IAAC,aAAa,EAAE,YAAY,EAAE,CAAA;CAAE,CAqC/D"}
\ No newline at end of file
diff --git a/dist/face-api.cjs.js b/dist/face-api.cjs.js
index 06a3480..ed48944 100644
--- a/dist/face-api.cjs.js
+++ b/dist/face-api.cjs.js
@@ -1,6 +1,5 @@
 var __defineProperty = Object.defineProperty;
 var __hasOwnProperty = Object.prototype.hasOwnProperty;
-var __assign = Object.assign;
 var __commonJS = (callback, module2) => () => {
   if (!module2) {
     module2 = {exports: {}};
@@ -11,10 +10,10 @@ var __commonJS = (callback, module2) => () => {
 var __markAsModule = (target) => {
   return __defineProperty(target, "__esModule", {value: true});
 };
-var __export = (target, all3) => {
+var __export = (target, all) => {
   __markAsModule(target);
-  for (var name in all3)
-    __defineProperty(target, name, {get: all3[name], enumerable: true});
+  for (var name in all)
+    __defineProperty(target, name, {get: all[name], enumerable: true});
 };
 var __exportStar = (target, module2) => {
   __markAsModule(target);
@@ -31,646 +30,6 @@ var __toModule = (module2) => {
   return __exportStar(__defineProperty({}, "default", {value: module2, enumerable: true}), module2);
 };
 
-// node_modules/seedrandom/lib/alea.js
-var require_alea = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function Alea(seed) {
-      var me = this, mash = Mash();
-      me.next = function() {
-        var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
-        me.s0 = me.s1;
-        me.s1 = me.s2;
-        return me.s2 = t - (me.c = t | 0);
-      };
-      me.c = 1;
-      me.s0 = mash(" ");
-      me.s1 = mash(" ");
-      me.s2 = mash(" ");
-      me.s0 -= mash(seed);
-      if (me.s0 < 0) {
-        me.s0 += 1;
-      }
-      me.s1 -= mash(seed);
-      if (me.s1 < 0) {
-        me.s1 += 1;
-      }
-      me.s2 -= mash(seed);
-      if (me.s2 < 0) {
-        me.s2 += 1;
-      }
-      mash = null;
-    }
-    function copy(f, t) {
-      t.c = f.c;
-      t.s0 = f.s0;
-      t.s1 = f.s1;
-      t.s2 = f.s2;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
-      prng.int32 = function() {
-        return xg.next() * 4294967296 | 0;
-      };
-      prng.double = function() {
-        return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
-      };
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    function Mash() {
-      var n = 4022871197;
-      var mash = function(data) {
-        data = data.toString();
-        for (var i = 0; i < data.length; i++) {
-          n += data.charCodeAt(i);
-          var h = 0.02519603282416938 * n;
-          n = h >>> 0;
-          h -= n;
-          h *= n;
-          n = h >>> 0;
-          h -= n;
-          n += h * 4294967296;
-        }
-        return (n >>> 0) * 23283064365386963e-26;
-      };
-      return mash;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.alea = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor128.js
-var require_xor128 = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.next = function() {
-        var t = me.x ^ me.x << 11;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
-      };
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor128 = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorwow.js
-var require_xorwow = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var t = me.x ^ me.x >>> 2;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        me.w = me.v;
-        return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
-      };
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.v = 0;
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        if (k == strseed.length) {
-          me.d = me.x << 10 ^ me.x >>> 4;
-        }
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      t.v = f.v;
-      t.d = f.d;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorwow = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorshift7.js
-var require_xorshift7 = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var X = me.x, i = me.i, t, v, w;
-        t = X[i];
-        t ^= t >>> 7;
-        v = t ^ t << 24;
-        t = X[i + 1 & 7];
-        v ^= t ^ t >>> 10;
-        t = X[i + 3 & 7];
-        v ^= t ^ t >>> 3;
-        t = X[i + 4 & 7];
-        v ^= t ^ t << 7;
-        t = X[i + 7 & 7];
-        t = t ^ t << 13;
-        v ^= t ^ t << 9;
-        X[i] = v;
-        me.i = i + 1 & 7;
-        return v;
-      };
-      function init(me2, seed2) {
-        var j, w, X = [];
-        if (seed2 === (seed2 | 0)) {
-          w = X[0] = seed2;
-        } else {
-          seed2 = "" + seed2;
-          for (j = 0; j < seed2.length; ++j) {
-            X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
-          }
-        }
-        while (X.length < 8)
-          X.push(0);
-        for (j = 0; j < 8 && X[j] === 0; ++j)
-          ;
-        if (j == 8)
-          w = X[7] = -1;
-        else
-          w = X[j];
-        me2.x = X;
-        me2.i = 0;
-        for (j = 256; j > 0; --j) {
-          me2.next();
-        }
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.x = f.x.slice();
-      t.i = f.i;
-      return t;
-    }
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.x)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorshift7 = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor4096.js
-var require_xor4096 = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var w = me.w, X = me.X, i = me.i, t, v;
-        me.w = w = w + 1640531527 | 0;
-        v = X[i + 34 & 127];
-        t = X[i = i + 1 & 127];
-        v ^= v << 13;
-        t ^= t << 17;
-        v ^= v >>> 15;
-        t ^= t >>> 12;
-        v = X[i] = v ^ t;
-        me.i = i;
-        return v + (w ^ w >>> 16) | 0;
-      };
-      function init(me2, seed2) {
-        var t, v, i, j, w, X = [], limit = 128;
-        if (seed2 === (seed2 | 0)) {
-          v = seed2;
-          seed2 = null;
-        } else {
-          seed2 = seed2 + "\0";
-          v = 0;
-          limit = Math.max(limit, seed2.length);
-        }
-        for (i = 0, j = -32; j < limit; ++j) {
-          if (seed2)
-            v ^= seed2.charCodeAt((j + 32) % seed2.length);
-          if (j === 0)
-            w = v;
-          v ^= v << 10;
-          v ^= v >>> 15;
-          v ^= v << 4;
-          v ^= v >>> 13;
-          if (j >= 0) {
-            w = w + 1640531527 | 0;
-            t = X[j & 127] ^= v + w;
-            i = t == 0 ? i + 1 : 0;
-          }
-        }
-        if (i >= 128) {
-          X[(seed2 && seed2.length || 0) & 127] = -1;
-        }
-        i = 127;
-        for (j = 4 * 128; j > 0; --j) {
-          v = X[i + 34 & 127];
-          t = X[i = i + 1 & 127];
-          v ^= v << 13;
-          t ^= t << 17;
-          v ^= v >>> 15;
-          t ^= t >>> 12;
-          X[i] = v ^ t;
-        }
-        me2.w = w;
-        me2.X = X;
-        me2.i = i;
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.w = f.w;
-      t.X = f.X.slice();
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.X)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor4096 = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/tychei.js
-var require_tychei = __commonJS((exports2, module2) => {
-  (function(global2, module3, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var b = me.b, c = me.c, d = me.d, a = me.a;
-        b = b << 25 ^ b >>> 7 ^ c;
-        c = c - d | 0;
-        d = d << 24 ^ d >>> 8 ^ a;
-        a = a - b | 0;
-        me.b = b = b << 20 ^ b >>> 12 ^ c;
-        me.c = c = c - d | 0;
-        me.d = d << 16 ^ c >>> 16 ^ a;
-        return me.a = a - b | 0;
-      };
-      me.a = 0;
-      me.b = 0;
-      me.c = 2654435769 | 0;
-      me.d = 1367130551;
-      if (seed === Math.floor(seed)) {
-        me.a = seed / 4294967296 | 0;
-        me.b = seed | 0;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 20; k++) {
-        me.b ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.a = f.a;
-      t.b = f.b;
-      t.c = f.c;
-      t.d = f.d;
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module3 && module3.exports) {
-      module3.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.tychei = impl;
-    }
-  })(exports2, typeof module2 == "object" && module2, typeof define == "function" && define);
-});
-
-// disabled:crypto
-var require_disabled_crypto = __commonJS(() => {
-});
-
-// node_modules/seedrandom/seedrandom.js
-var require_seedrandom = __commonJS((exports2, module2) => {
-  (function(pool3, math) {
-    var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
-    function seedrandom2(seed, options, callback) {
-      var key = [];
-      options = options == true ? {entropy: true} : options || {};
-      var shortseed = mixkey(flatten2(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
-      var arc4 = new ARC4(key);
-      var prng = function() {
-        var n = arc4.g(chunks), d = startdenom, x = 0;
-        while (n < significance) {
-          n = (n + x) * width;
-          d *= width;
-          x = arc4.g(1);
-        }
-        while (n >= overflow) {
-          n /= 2;
-          d /= 2;
-          x >>>= 1;
-        }
-        return (n + x) / d;
-      };
-      prng.int32 = function() {
-        return arc4.g(4) | 0;
-      };
-      prng.quick = function() {
-        return arc4.g(4) / 4294967296;
-      };
-      prng.double = prng;
-      mixkey(tostring(arc4.S), pool3);
-      return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
-        if (state) {
-          if (state.S) {
-            copy(state, arc4);
-          }
-          prng2.state = function() {
-            return copy(arc4, {});
-          };
-        }
-        if (is_math_call) {
-          math[rngname] = prng2;
-          return seed2;
-        } else
-          return prng2;
-      })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
-    }
-    math["seed" + rngname] = seedrandom2;
-    function ARC4(key) {
-      var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
-      if (!keylen) {
-        key = [keylen++];
-      }
-      while (i < width) {
-        s[i] = i++;
-      }
-      for (i = 0; i < width; i++) {
-        s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
-        s[j] = t;
-      }
-      (me.g = function(count) {
-        var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
-        while (count--) {
-          t2 = s2[i2 = mask & i2 + 1];
-          r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
-        }
-        me.i = i2;
-        me.j = j2;
-        return r;
-      })(width);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.j = f.j;
-      t.S = f.S.slice();
-      return t;
-    }
-    ;
-    function flatten2(obj, depth) {
-      var result = [], typ = typeof obj, prop;
-      if (depth && typ == "object") {
-        for (prop in obj) {
-          try {
-            result.push(flatten2(obj[prop], depth - 1));
-          } catch (e) {
-          }
-        }
-      }
-      return result.length ? result : typ == "string" ? obj : obj + "\0";
-    }
-    function mixkey(seed, key) {
-      var stringseed = seed + "", smear, j = 0;
-      while (j < stringseed.length) {
-        key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
-      }
-      return tostring(key);
-    }
-    function autoseed() {
-      try {
-        var out;
-        if (nodecrypto && (out = nodecrypto.randomBytes)) {
-          out = out(width);
-        } else {
-          out = new Uint8Array(width);
-          (global2.crypto || global2.msCrypto).getRandomValues(out);
-        }
-        return tostring(out);
-      } catch (e) {
-        var browser2 = global2.navigator, plugins = browser2 && browser2.plugins;
-        return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
-      }
-    }
-    function tostring(a) {
-      return String.fromCharCode.apply(0, a);
-    }
-    mixkey(math.random(), pool3);
-    if (typeof module2 == "object" && module2.exports) {
-      module2.exports = seedrandom2;
-      try {
-        nodecrypto = require_disabled_crypto();
-      } catch (ex) {
-      }
-    } else if (typeof define == "function" && define.amd) {
-      define(function() {
-        return seedrandom2;
-      });
-    }
-  })([], Math);
-});
-
-// node_modules/seedrandom/index.js
-var require_seedrandom2 = __commonJS((exports2, module2) => {
-  var alea2 = require_alea();
-  var xor128 = require_xor128();
-  var xorwow = require_xorwow();
-  var xorshift7 = require_xorshift7();
-  var xor4096 = require_xor4096();
-  var tychei = require_tychei();
-  var sr = require_seedrandom();
-  sr.alea = alea2;
-  sr.xor128 = xor128;
-  sr.xorwow = xorwow;
-  sr.xorshift7 = xorshift7;
-  sr.xor4096 = xor4096;
-  sr.tychei = tychei;
-  module2.exports = sr;
-});
-
 // build/src/env/isNodejs.js
 var require_isNodejs = __commonJS((exports2, module2) => {
   __export(exports2, {
@@ -681,19257 +40,6 @@ var require_isNodejs = __commonJS((exports2, module2) => {
   }
 });
 
-// node_modules/@tensorflow/tfjs-core/dist/environment.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
-class Environment {
-  constructor(global2) {
-    this.global = global2;
-    this.flags = {};
-    this.flagRegistry = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  setPlatform(platformName, platform) {
-    if (this.platform != null) {
-      console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
-    }
-    this.platformName = platformName;
-    this.platform = platform;
-  }
-  registerFlag(flagName, evaluationFn, setHook) {
-    this.flagRegistry[flagName] = {evaluationFn, setHook};
-    if (this.urlFlags[flagName] != null) {
-      const flagValue = this.urlFlags[flagName];
-      console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
-      this.set(flagName, flagValue);
-    }
-  }
-  async getAsync(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    this.flags[flagName] = await this.evaluateFlag(flagName);
-    return this.flags[flagName];
-  }
-  get(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    const flagValue = this.evaluateFlag(flagName);
-    if (flagValue instanceof Promise) {
-      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
-    }
-    this.flags[flagName] = flagValue;
-    return this.flags[flagName];
-  }
-  getNumber(flagName) {
-    return this.get(flagName);
-  }
-  getBool(flagName) {
-    return this.get(flagName);
-  }
-  getFlags() {
-    return this.flags;
-  }
-  get features() {
-    return this.flags;
-  }
-  set(flagName, value) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
-    }
-    this.flags[flagName] = value;
-    if (this.flagRegistry[flagName].setHook != null) {
-      this.flagRegistry[flagName].setHook(value);
-    }
-  }
-  evaluateFlag(flagName) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
-    }
-    return this.flagRegistry[flagName].evaluationFn();
-  }
-  setFlags(flags3) {
-    this.flags = Object.assign({}, flags3);
-  }
-  reset() {
-    this.flags = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  populateURLFlags() {
-    if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
-      return;
-    }
-    const urlParams = getQueryParams(this.global.location.search);
-    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
-      keyValues.forEach((keyValue) => {
-        const [key, value] = keyValue.split(":");
-        this.urlFlags[key] = parseValue(key, value);
-      });
-    }
-  }
-}
-function getQueryParams(queryString) {
-  const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
-    decodeParam(params, t[0], t[1]);
-    return t.join("=");
-  });
-  return params;
-}
-function decodeParam(params, name, value) {
-  params[decodeURIComponent(name)] = decodeURIComponent(value || "");
-}
-function parseValue(flagName, value) {
-  value = value.toLowerCase();
-  if (value === "true" || value === "false") {
-    return value === "true";
-  } else if (`${+value}` === value) {
-    return +value;
-  }
-  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
-}
-function env() {
-  return ENV;
-}
-let ENV = null;
-function setEnvironmentGlobal(environment12) {
-  ENV = environment12;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/global_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let globalNameSpace;
-function getGlobalNamespace() {
-  if (globalNameSpace == null) {
-    let ns;
-    if (typeof window !== "undefined") {
-      ns = window;
-    } else if (typeof global !== "undefined") {
-      ns = global;
-    } else if (typeof process !== "undefined") {
-      ns = process;
-    } else if (typeof self !== "undefined") {
-      ns = self;
-    } else {
-      throw new Error("Could not find a global object");
-    }
-    globalNameSpace = ns;
-  }
-  return globalNameSpace;
-}
-function getGlobalMap() {
-  const ns = getGlobalNamespace();
-  if (ns._tfGlobals == null) {
-    ns._tfGlobals = new Map();
-  }
-  return ns._tfGlobals;
-}
-function getGlobal(key, init) {
-  const globalMap = getGlobalMap();
-  if (globalMap.has(key)) {
-    return globalMap.get(key);
-  } else {
-    const singleton = init();
-    globalMap.set(key, singleton);
-    return globalMap.get(key);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
-const Abs = "Abs";
-const Acos = "Acos";
-const Acosh = "Acosh";
-const Add = "Add";
-const AddN = "AddN";
-const All = "All";
-const Any = "Any";
-const ArgMax = "ArgMax";
-const ArgMin = "ArgMin";
-const Asin = "Asin";
-const Asinh = "Asinh";
-const Atan = "Atan";
-const Atanh = "Atanh";
-const Atan2 = "Atan2";
-const AvgPool = "AvgPool";
-const AvgPoolBackprop = "AvgPoolBackprop";
-const AvgPool3D = "AvgPool3D";
-const AvgPool3DBackprop = "AvgPool3DBackprop";
-const BatchMatMul = "BatchMatMul";
-const BatchToSpaceND = "BatchToSpaceND";
-const BroadcastTo = "BroadcastTo";
-const Cast = "Cast";
-const Ceil = "Ceil";
-const ClipByValue = "ClipByValue";
-const Complex = "Complex";
-const Concat = "Concat";
-const Conv2D = "Conv2D";
-const Conv2DBackpropFilter = "Conv2DBackpropFilter";
-const Conv2DBackpropInput = "Conv2DBackpropInput";
-const Conv3D = "Conv3D";
-const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
-const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
-const Cos = "Cos";
-const Cosh = "Cosh";
-const Cumsum = "Cumsum";
-const CropAndResize = "CropAndResize";
-const DepthToSpace = "DepthToSpace";
-const DepthwiseConv2dNative = "DepthwiseConv2dNative";
-const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
-const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
-const Diag = "Diag";
-const Dilation2D = "Dilation2D";
-const Dilation2DBackpropInput = "Dilation2DBackpropInput";
-const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
-const Div = "Div";
-const Elu = "Elu";
-const EluGrad = "EluGrad";
-const Erf = "Erf";
-const Equal = "Equal";
-const Exp = "Exp";
-const Expm1 = "Expm1";
-const FFT = "FFT";
-const Fill = "Fill";
-const FlipLeftRight = "FlipLeftRight";
-const Floor = "Floor";
-const FloorDiv = "FloorDiv";
-const FusedBatchNorm = "FusedBatchNorm";
-const GatherV2 = "GatherV2";
-const GatherNd = "GatherNd";
-const Greater = "Greater";
-const GreaterEqual = "GreaterEqual";
-const Identity = "Identity";
-const IFFT = "IFFT";
-const Imag = "Imag";
-const IsFinite = "IsFinite";
-const IsInf = "IsInf";
-const IsNan = "IsNan";
-const Less = "Less";
-const LessEqual = "LessEqual";
-const LinSpace = "LinSpace";
-const Log = "Log";
-const Log1p = "Log1p";
-const LogicalAnd = "LogicalAnd";
-const LogicalNot = "LogicalNot";
-const LogicalOr = "LogicalOr";
-const LogSoftmax = "LogSoftmax";
-const LRN = "LRN";
-const LRNBackprop = "LRNBackprop";
-const Max = "Max";
-const Maximum = "Maximum";
-const MaxPool = "MaxPool";
-const MaxPoolBackprop = "MaxPoolBackprop";
-const MaxPool3D = "MaxPool3D";
-const MaxPool3DBackprop = "MaxPool3DBackprop";
-const MaxPoolWithArgmax = "MaxPoolWithArgmax";
-const Mean = "Mean";
-const Min = "Min";
-const Minimum = "Minimum";
-const Mod = "Mod";
-const Multiply = "Multiply";
-const Negate = "Negate";
-const NotEqual = "NotEqual";
-const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
-const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
-const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
-const OnesLike = "OnesLike";
-const OneHot = "OneHot";
-const PadV2 = "PadV2";
-const Pool = "Pool";
-const Pow = "Pow";
-const Prelu = "Prelu";
-const Prod = "Prod";
-const Range = "Range";
-const Real = "Real";
-const Reciprocal = "Reciprocal";
-const Relu = "Relu";
-const Reshape = "Reshape";
-const ResizeNearestNeighbor = "ResizeNearestNeighbor";
-const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
-const ResizeBilinear = "ResizeBilinear";
-const ResizeBilinearGrad = "ResizeBilinearGrad";
-const Relu6 = "Relu6";
-const Reverse = "Reverse";
-const Round = "Round";
-const Rsqrt = "Rsqrt";
-const ScatterNd = "ScatterNd";
-const SelectV2 = "SelectV2";
-const Selu = "Selu";
-const Slice = "Slice";
-const Sin = "Sin";
-const Sinh = "Sinh";
-const Sign = "Sign";
-const Sigmoid = "Sigmoid";
-const Softplus = "Softplus";
-const Sqrt = "Sqrt";
-const Sum = "Sum";
-const SpaceToBatchND = "SpaceToBatchND";
-const SplitV = "SplitV";
-const Softmax = "Softmax";
-const SquaredDifference = "SquaredDifference";
-const Square = "Square";
-const Sub = "Sub";
-const SparseToDense = "SparseToDense";
-const StridedSlice = "StridedSlice";
-const Tan = "Tan";
-const Tanh = "Tanh";
-const Tile = "Tile";
-const TopK = "TopK";
-const Transpose = "Transpose";
-const Unpack = "Unpack";
-const UnsortedSegmentSum = "UnsortedSegmentSum";
-const ZerosLike = "ZerosLike";
-const Step = "Step";
-const FromPixels = "FromPixels";
-const RotateWithOffset = "RotateWithOffset";
-const _FusedMatMul = "_FusedMatMul";
-const FusedConv2D = "FusedConv2D";
-const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const kernelRegistry = getGlobal("kernelRegistry", () => new Map());
-const gradRegistry = getGlobal("gradRegistry", () => new Map());
-function getKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  return kernelRegistry.get(key);
-}
-function getGradient(kernelName) {
-  return gradRegistry.get(kernelName);
-}
-function getKernelsForBackend(backendName) {
-  const it = kernelRegistry.entries();
-  const result = [];
-  while (true) {
-    const {done, value} = it.next();
-    if (done) {
-      break;
-    }
-    const [key, config2] = value;
-    const [backend2] = key.split("_");
-    if (backend2 === backendName) {
-      result.push(config2);
-    }
-  }
-  return result;
-}
-function registerKernel(config2) {
-  const {kernelName, backendName} = config2;
-  const key = makeKey(kernelName, backendName);
-  if (kernelRegistry.has(key)) {
-    console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
-  }
-  kernelRegistry.set(key, config2);
-}
-function registerGradient(config2) {
-  const {kernelName} = config2;
-  if (gradRegistry.has(kernelName)) {
-    if (env().getBool("DEBUG")) {
-      console.warn(`Overriding the gradient for '${kernelName}'`);
-    }
-  }
-  gradRegistry.set(kernelName, config2);
-}
-function unregisterKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  if (!kernelRegistry.has(key)) {
-    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
-  }
-  kernelRegistry.delete(key);
-}
-function unregisterGradient(kernelName) {
-  if (!gradRegistry.has(kernelName)) {
-    throw new Error(`The gradient '${kernelName}' for backend is not registered`);
-  }
-  gradRegistry.delete(kernelName);
-}
-function makeKey(kernelName, backendName) {
-  return `${backendName}_${kernelName}`;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/util.js
-const util_exports = {};
-__export(util_exports, {
-  arraysEqual: () => arraysEqual,
-  assert: () => assert,
-  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
-  assertNonNull: () => assertNonNull,
-  assertShapesMatch: () => assertShapesMatch,
-  bytesFromStringArray: () => bytesFromStringArray,
-  bytesPerElement: () => bytesPerElement,
-  checkConversionForErrors: () => checkConversionForErrors,
-  clamp: () => clamp,
-  computeStrides: () => computeStrides,
-  createShuffledIndices: () => createShuffledIndices,
-  decodeString: () => decodeString,
-  distSquared: () => distSquared,
-  encodeString: () => encodeString,
-  fetch: () => fetch2,
-  flatten: () => flatten,
-  getArrayFromDType: () => getArrayFromDType,
-  getTypedArrayFromDType: () => getTypedArrayFromDType,
-  hasEncodingLoss: () => hasEncodingLoss,
-  indexToLoc: () => indexToLoc,
-  inferDtype: () => inferDtype,
-  inferFromImplicitShape: () => inferFromImplicitShape,
-  isBoolean: () => isBoolean,
-  isFunction: () => isFunction,
-  isInt: () => isInt,
-  isNumber: () => isNumber,
-  isScalarShape: () => isScalarShape,
-  isString: () => isString,
-  isTypedArray: () => isTypedArray,
-  isValidDtype: () => isValidDtype,
-  locToIndex: () => locToIndex,
-  makeOnesTypedArray: () => makeOnesTypedArray,
-  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
-  makeZerosTypedArray: () => makeZerosTypedArray,
-  nearestDivisor: () => nearestDivisor,
-  nearestLargerEven: () => nearestLargerEven,
-  now: () => now,
-  parseAxisParam: () => parseAxisParam,
-  randUniform: () => randUniform,
-  repeatedTry: () => repeatedTry,
-  rightPad: () => rightPad,
-  shuffle: () => shuffle,
-  sizeFromShape: () => sizeFromShape,
-  sizeToSquarishShape: () => sizeToSquarishShape,
-  squeezeShape: () => squeezeShape,
-  sum: () => sum,
-  tanh: () => tanh,
-  toNestedArray: () => toNestedArray,
-  toTypedArray: () => toTypedArray
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function shuffle(array) {
-  let counter = array.length;
-  let temp = 0;
-  let index = 0;
-  while (counter > 0) {
-    index = Math.random() * counter | 0;
-    counter--;
-    temp = array[counter];
-    array[counter] = array[index];
-    array[index] = temp;
-  }
-}
-function clamp(min4, x, max6) {
-  return Math.max(min4, Math.min(x, max6));
-}
-function nearestLargerEven(val) {
-  return val % 2 === 0 ? val : val + 1;
-}
-function sum(arr) {
-  let sum26 = 0;
-  for (let i = 0; i < arr.length; i++) {
-    sum26 += arr[i];
-  }
-  return sum26;
-}
-function randUniform(a, b) {
-  const r = Math.random();
-  return b * r + (1 - r) * a;
-}
-function distSquared(a, b) {
-  let result = 0;
-  for (let i = 0; i < a.length; i++) {
-    const diff = Number(a[i]) - Number(b[i]);
-    result += diff * diff;
-  }
-  return result;
-}
-function assert(expr, msg) {
-  if (!expr) {
-    throw new Error(typeof msg === "string" ? msg : msg());
-  }
-}
-function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
-  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-}
-function assertNonNull(a) {
-  assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
-}
-function flatten(arr, result = [], skipTypedArray = false) {
-  if (result == null) {
-    result = [];
-  }
-  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-    for (let i = 0; i < arr.length; ++i) {
-      flatten(arr[i], result, skipTypedArray);
-    }
-  } else {
-    result.push(arr);
-  }
-  return result;
-}
-function sizeFromShape(shape) {
-  if (shape.length === 0) {
-    return 1;
-  }
-  let size = shape[0];
-  for (let i = 1; i < shape.length; i++) {
-    size *= shape[i];
-  }
-  return size;
-}
-function isScalarShape(shape) {
-  return shape.length === 0;
-}
-function arraysEqual(n1, n2) {
-  if (n1 === n2) {
-    return true;
-  }
-  if (n1 == null || n2 == null) {
-    return false;
-  }
-  if (n1.length !== n2.length) {
-    return false;
-  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function isInt(a) {
-  return a % 1 === 0;
-}
-function tanh(x) {
-  if (Math.tanh != null) {
-    return Math.tanh(x);
-  }
-  if (x === Infinity) {
-    return 1;
-  } else if (x === -Infinity) {
-    return -1;
-  } else {
-    const e2x = Math.exp(2 * x);
-    return (e2x - 1) / (e2x + 1);
-  }
-}
-function sizeToSquarishShape(size) {
-  const width = Math.ceil(Math.sqrt(size));
-  return [width, Math.ceil(size / width)];
-}
-function createShuffledIndices(n) {
-  const shuffledIndices = new Uint32Array(n);
-  for (let i = 0; i < n; ++i) {
-    shuffledIndices[i] = i;
-  }
-  shuffle(shuffledIndices);
-  return shuffledIndices;
-}
-function rightPad(a, size) {
-  if (size <= a.length) {
-    return a;
-  }
-  return a + " ".repeat(size - a.length);
-}
-function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
-  return new Promise((resolve, reject) => {
-    let tryCount = 0;
-    const tryFn = () => {
-      if (checkFn()) {
-        resolve();
-        return;
-      }
-      tryCount++;
-      const nextBackoff = delayFn(tryCount);
-      if (maxCounter != null && tryCount >= maxCounter) {
-        reject();
-        return;
-      }
-      setTimeout(tryFn, nextBackoff);
-    };
-    tryFn();
-  });
-}
-function inferFromImplicitShape(shape, size) {
-  let shapeProd = 1;
-  let implicitIdx = -1;
-  for (let i = 0; i < shape.length; ++i) {
-    if (shape[i] >= 0) {
-      shapeProd *= shape[i];
-    } else if (shape[i] === -1) {
-      if (implicitIdx !== -1) {
-        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
-      }
-      implicitIdx = i;
-    } else if (shape[i] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
-    }
-  }
-  if (implicitIdx === -1) {
-    if (size > 0 && size !== shapeProd) {
-      throw Error(`Size(${size}) must match the product of shape ${shape}`);
-    }
-    return shape;
-  }
-  if (shapeProd === 0) {
-    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
-  }
-  if (size % shapeProd !== 0) {
-    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
-  }
-  const newShape = shape.slice();
-  newShape[implicitIdx] = size / shapeProd;
-  return newShape;
-}
-function parseAxisParam(axis, shape) {
-  const rank = shape.length;
-  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
-  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
-  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
-  return axis.map((a) => a < 0 ? rank + a : a);
-}
-function squeezeShape(shape, axis) {
-  const newShape = [];
-  const keptDims = [];
-  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
-  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
-  let j = 0;
-  for (let i = 0; i < shape.length; ++i) {
-    if (axes != null) {
-      if (axes[j] === i && shape[i] !== 1) {
-        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
-      }
-      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
-        newShape.push(shape[i]);
-        keptDims.push(i);
-      }
-      if (axes[j] <= i) {
-        j++;
-      }
-    }
-    if (shape[i] !== 1) {
-      newShape.push(shape[i]);
-      keptDims.push(i);
-    }
-  }
-  return {newShape, keptDims};
-}
-function getTypedArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function getArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else if (dtype === "string") {
-    values = new Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function checkConversionForErrors(vals, dtype) {
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
-    }
-  }
-}
-function isValidDtype(dtype) {
-  return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
-}
-function hasEncodingLoss(oldType, newType) {
-  if (newType === "complex64") {
-    return false;
-  }
-  if (newType === "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "bool" && oldType === "bool") {
-    return false;
-  }
-  return true;
-}
-function isTypedArray(a) {
-  return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
-}
-function bytesPerElement(dtype) {
-  if (dtype === "float32" || dtype === "int32") {
-    return 4;
-  } else if (dtype === "complex64") {
-    return 8;
-  } else if (dtype === "bool") {
-    return 1;
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-function bytesFromStringArray(arr) {
-  if (arr == null) {
-    return 0;
-  }
-  let bytes = 0;
-  arr.forEach((x) => bytes += x.length);
-  return bytes;
-}
-function isString(value) {
-  return typeof value === "string" || value instanceof String;
-}
-function isBoolean(value) {
-  return typeof value === "boolean";
-}
-function isNumber(value) {
-  return typeof value === "number";
-}
-function inferDtype(values) {
-  if (Array.isArray(values)) {
-    return inferDtype(values[0]);
-  }
-  if (values instanceof Float32Array) {
-    return "float32";
-  } else if (values instanceof Int32Array || values instanceof Uint8Array) {
-    return "int32";
-  } else if (isNumber(values)) {
-    return "float32";
-  } else if (isString(values)) {
-    return "string";
-  } else if (isBoolean(values)) {
-    return "bool";
-  }
-  return "float32";
-}
-function isFunction(f) {
-  return !!(f && f.constructor && f.call && f.apply);
-}
-function nearestDivisor(size, start) {
-  for (let i = start; i < size; ++i) {
-    if (size % i === 0) {
-      return i;
-    }
-  }
-  return size;
-}
-function computeStrides(shape) {
-  const rank = shape.length;
-  if (rank < 2) {
-    return [];
-  }
-  const strides = new Array(rank - 1);
-  strides[rank - 2] = shape[rank - 1];
-  for (let i = rank - 3; i >= 0; --i) {
-    strides[i] = strides[i + 1] * shape[i + 1];
-  }
-  return strides;
-}
-function toTypedArray(a, dtype) {
-  if (dtype === "string") {
-    throw new Error("Cannot convert a string[] to a TypedArray");
-  }
-  if (Array.isArray(a)) {
-    a = flatten(a);
-  }
-  if (env().getBool("DEBUG")) {
-    checkConversionForErrors(a, dtype);
-  }
-  if (noConversionNeeded(a, dtype)) {
-    return a;
-  }
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(a);
-  } else if (dtype === "int32") {
-    return new Int32Array(a);
-  } else if (dtype === "bool") {
-    const bool = new Uint8Array(a.length);
-    for (let i = 0; i < bool.length; ++i) {
-      if (Math.round(a[i]) !== 0) {
-        bool[i] = 1;
-      }
-    }
-    return bool;
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function createNestedArray(offset, shape, a) {
-  const ret = new Array();
-  if (shape.length === 1) {
-    const d = shape[0];
-    for (let i = 0; i < d; i++) {
-      ret[i] = a[offset + i];
-    }
-  } else {
-    const d = shape[0];
-    const rest = shape.slice(1);
-    const len = rest.reduce((acc, c) => acc * c);
-    for (let i = 0; i < d; i++) {
-      ret[i] = createNestedArray(offset + i * len, rest, a);
-    }
-  }
-  return ret;
-}
-function toNestedArray(shape, a) {
-  if (shape.length === 0) {
-    return a[0];
-  }
-  const size = shape.reduce((acc, c) => acc * c);
-  if (size === 0) {
-    return [];
-  }
-  if (size !== a.length) {
-    throw new Error(`[${shape}] does not match the input size ${a.length}.`);
-  }
-  return createNestedArray(0, shape, a);
-}
-function noConversionNeeded(a, dtype) {
-  return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
-}
-function makeOnesTypedArray(size, dtype) {
-  const array = makeZerosTypedArray(size, dtype);
-  for (let i = 0; i < array.length; i++) {
-    array[i] = 1;
-  }
-  return array;
-}
-function makeZerosTypedArray(size, dtype) {
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(size);
-  } else if (dtype === "int32") {
-    return new Int32Array(size);
-  } else if (dtype === "bool") {
-    return new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function makeZerosNestedTypedArray(shape, dtype) {
-  const size = shape.reduce((prev, curr) => prev * curr, 1);
-  if (dtype == null || dtype === "float32") {
-    return toNestedArray(shape, new Float32Array(size));
-  } else if (dtype === "int32") {
-    return toNestedArray(shape, new Int32Array(size));
-  } else if (dtype === "bool") {
-    return toNestedArray(shape, new Uint8Array(size));
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function now() {
-  return env().platform.now();
-}
-function assertNonNegativeIntegerDimensions(shape) {
-  shape.forEach((dimSize) => {
-    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
-  });
-}
-function fetch2(path, requestInits) {
-  return env().platform.fetch(path, requestInits);
-}
-function encodeString(s, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.encode(s, encoding);
-}
-function decodeString(bytes, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.decode(bytes, encoding);
-}
-function locToIndex(locs, rank, strides) {
-  if (rank === 0) {
-    return 0;
-  } else if (rank === 1) {
-    return locs[0];
-  }
-  let index = locs[locs.length - 1];
-  for (let i = 0; i < locs.length - 1; ++i) {
-    index += strides[i] * locs[i];
-  }
-  return index;
-}
-function indexToLoc(index, rank, strides) {
-  if (rank === 0) {
-    return [];
-  } else if (rank === 1) {
-    return [index];
-  }
-  const locs = new Array(rank);
-  for (let i = 0; i < locs.length - 1; ++i) {
-    locs[i] = Math.floor(index / strides[i]);
-    index -= locs[i] * strides[i];
-  }
-  locs[locs.length - 1] = index;
-  return locs;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/profiler.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Profiler {
-  constructor(backendTimer, logger) {
-    this.backendTimer = backendTimer;
-    this.logger = logger;
-    if (logger == null) {
-      this.logger = new Logger();
-    }
-  }
-  profileKernel(kernelName, inputs, f) {
-    let outputs;
-    const holdResultWrapperFn = () => {
-      outputs = f();
-    };
-    const timer = this.backendTimer.time(holdResultWrapperFn);
-    for (let i = 0; i < outputs.length; i++) {
-      const output = outputs[i];
-      output.data().then((tensorVals) => {
-        checkComputationForErrors(tensorVals, output.dtype, kernelName);
-      });
-    }
-    const kernelProfile = {
-      kernelName,
-      outputs,
-      inputs,
-      timeMs: timer.then((timing) => timing.kernelMs),
-      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
-    };
-    return kernelProfile;
-  }
-  logKernelProfile(kernelProfile) {
-    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
-    outputs.forEach((result) => {
-      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
-        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
-      });
-    });
-  }
-}
-function checkComputationForErrors(vals, dtype, kernelName) {
-  if (dtype !== "float32") {
-    return false;
-  }
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      console.warn(`Found ${num} in the result of '${kernelName}'`);
-      return true;
-    }
-  }
-  return false;
-}
-class Logger {
-  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
-    const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
-    const paddedName = rightPad(name, 25);
-    const rank = result.rank;
-    const size = result.size;
-    const shape = rightPad(result.shape.toString(), 14);
-    let inputShapesDescription = "";
-    for (const name2 in inputs) {
-      const input = inputs[name2];
-      if (input != null) {
-        const inputShape = input.shape || result.shape;
-        const inputRank = inputShape.length;
-        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
-      }
-    }
-    console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tape.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFilteredNodesXToY(tape2, xs, y) {
-  const tensorsFromX = {};
-  const nodesFromX = {};
-  for (let i = 0; i < xs.length; i++) {
-    tensorsFromX[xs[i].id] = true;
-  }
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (const inputName in nodeInputs) {
-      const input = nodeInputs[inputName];
-      let anyInputFromX = false;
-      for (let j = 0; j < xs.length; j++) {
-        if (tensorsFromX[input.id]) {
-          node2.outputs.forEach((output) => tensorsFromX[output.id] = true);
-          anyInputFromX = true;
-          nodesFromX[node2.id] = true;
-          break;
-        }
-      }
-      if (anyInputFromX) {
-        break;
-      }
-    }
-  }
-  const tensorsLeadToY = {};
-  tensorsLeadToY[y.id] = true;
-  const nodesToY = {};
-  for (let i = tape2.length - 1; i >= 0; i--) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (let j = 0; j < node2.outputs.length; j++) {
-      if (tensorsLeadToY[node2.outputs[j].id]) {
-        for (const inputName in nodeInputs) {
-          tensorsLeadToY[nodeInputs[inputName].id] = true;
-          nodesToY[node2.id] = true;
-        }
-        break;
-      }
-    }
-  }
-  const filteredTape = [];
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    if (nodesFromX[node2.id] && nodesToY[node2.id]) {
-      const prunedInputs = {};
-      for (const inputName in node2.inputs) {
-        const nodeInput = node2.inputs[inputName];
-        if (tensorsFromX[nodeInput.id]) {
-          prunedInputs[inputName] = nodeInput;
-        }
-      }
-      const prunedNode = Object.assign({}, node2);
-      prunedNode.inputs = prunedInputs;
-      prunedNode.outputs = node2.outputs;
-      filteredTape.push(prunedNode);
-    }
-  }
-  return filteredTape;
-}
-function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add29) {
-  for (let i = filteredTape.length - 1; i >= 0; i--) {
-    const node2 = filteredTape[i];
-    const dys = [];
-    node2.outputs.forEach((o) => {
-      const gradTensor = tensorAccumulatedGradientMap[o.id];
-      if (gradTensor != null) {
-        dys.push(gradTensor);
-      } else {
-        dys.push(null);
-      }
-    });
-    if (node2.gradient == null) {
-      throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);
-    }
-    const inputGradients = node2.gradient(dys);
-    for (const inputName in node2.inputs) {
-      if (!(inputName in inputGradients)) {
-        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
-      }
-      const dx = tidy2(() => inputGradients[inputName]());
-      if (dx.dtype !== "float32") {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
-      }
-      const x = node2.inputs[inputName];
-      if (!arraysEqual(dx.shape, x.shape)) {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
-      }
-      if (tensorAccumulatedGradientMap[x.id] == null) {
-        tensorAccumulatedGradientMap[x.id] = dx;
-      } else {
-        const curGradient = tensorAccumulatedGradientMap[x.id];
-        tensorAccumulatedGradientMap[x.id] = add29(curGradient, dx);
-        curGradient.dispose();
-      }
-    }
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const FORMAT_LIMIT_NUM_VALS = 20;
-const FORMAT_NUM_FIRST_LAST_VALS = 3;
-const FORMAT_NUM_SIG_DIGITS = 7;
-function tensorToString(vals, shape, dtype, verbose) {
-  const strides = computeStrides(shape);
-  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
-  const rank = shape.length;
-  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
-  const lines = ["Tensor"];
-  if (verbose) {
-    lines.push(`  dtype: ${dtype}`);
-    lines.push(`  rank: ${rank}`);
-    lines.push(`  shape: [${shape}]`);
-    lines.push(`  values:`);
-  }
-  lines.push(valsLines.map((l) => "    " + l).join("\n"));
-  return lines.join("\n");
-}
-function computeMaxSizePerColumn(vals, shape, dtype, strides) {
-  const n = sizeFromShape(shape);
-  const numCols = strides[strides.length - 1];
-  const padPerCol = new Array(numCols).fill(0);
-  const rank = shape.length;
-  const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
-  if (rank > 1) {
-    for (let row = 0; row < n / numCols; row++) {
-      const offset = row * numCols;
-      for (let j = 0; j < numCols; j++) {
-        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
-      }
-    }
-  }
-  return padPerCol;
-}
-function valToString(val, pad8, dtype) {
-  let valStr;
-  if (Array.isArray(val)) {
-    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
-  } else if (isString(val)) {
-    valStr = `'${val}'`;
-  } else if (dtype === "bool") {
-    valStr = boolNumToString(val);
-  } else {
-    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
-  }
-  return rightPad(valStr, pad8);
-}
-function boolNumToString(v) {
-  return v === 0 ? "false" : "true";
-}
-function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
-  const storagePerElement = dtype === "complex64" ? 2 : 1;
-  const size = shape[0];
-  const rank = shape.length;
-  if (rank === 0) {
-    if (dtype === "complex64") {
-      const complexTuple = createComplexTuples(vals);
-      return [valToString(complexTuple[0], 0, dtype)];
-    }
-    if (dtype === "bool") {
-      return [boolNumToString(vals[0])];
-    }
-    return [vals[0].toString()];
-  }
-  if (rank === 1) {
-    if (size > FORMAT_LIMIT_NUM_VALS) {
-      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
-      let firstVals = Array.from(vals.slice(0, firstValsSize));
-      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
-      if (dtype === "complex64") {
-        firstVals = createComplexTuples(firstVals);
-        lastVals = createComplexTuples(lastVals);
-      }
-      return [
-        "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
-      ];
-    }
-    const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
-    return [
-      "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
-    ];
-  }
-  const subshape = shape.slice(1);
-  const substrides = strides.slice(1);
-  const stride = strides[0] * storagePerElement;
-  const lines = [];
-  if (size > FORMAT_LIMIT_NUM_VALS) {
-    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
-    }
-    lines.push("...");
-    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  } else {
-    for (let i = 0; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  }
-  const sep = rank === 2 ? "," : "";
-  lines[0] = "[" + lines[0] + sep;
-  for (let i = 1; i < lines.length - 1; i++) {
-    lines[i] = " " + lines[i] + sep;
-  }
-  let newLineSep = ",\n";
-  for (let i = 2; i < rank; i++) {
-    newLineSep += "\n";
-  }
-  lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
-  return lines;
-}
-function createComplexTuples(vals) {
-  const complexTuples = [];
-  for (let i = 0; i < vals.length; i += 2) {
-    complexTuples.push([vals[i], vals[i + 1]]);
-  }
-  return complexTuples;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class TensorBuffer {
-  constructor(shape, dtype, values) {
-    this.dtype = dtype;
-    this.shape = shape.slice();
-    this.size = sizeFromShape(shape);
-    if (values != null) {
-      const n = values.length;
-      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
-    }
-    if (dtype === "complex64") {
-      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
-    }
-    this.values = values || getArrayFromDType(dtype, this.size);
-    this.strides = computeStrides(shape);
-  }
-  set(value, ...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
-    const index = this.locToIndex(locs);
-    this.values[index] = value;
-  }
-  get(...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    let i = 0;
-    for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i]) {
-        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
-        throw new Error(msg);
-      }
-      i++;
-    }
-    let index = locs[locs.length - 1];
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      index += this.strides[i2] * locs[i2];
-    }
-    return this.values[index];
-  }
-  locToIndex(locs) {
-    if (this.rank === 0) {
-      return 0;
-    } else if (this.rank === 1) {
-      return locs[0];
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += this.strides[i] * locs[i];
-    }
-    return index;
-  }
-  indexToLoc(index) {
-    if (this.rank === 0) {
-      return [];
-    } else if (this.rank === 1) {
-      return [index];
-    }
-    const locs = new Array(this.shape.length);
-    for (let i = 0; i < locs.length - 1; ++i) {
-      locs[i] = Math.floor(index / this.strides[i]);
-      index -= locs[i] * this.strides[i];
-    }
-    locs[locs.length - 1] = index;
-    return locs;
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  toTensor() {
-    return trackerFn().makeTensor(this.values, this.shape, this.dtype);
-  }
-}
-let trackerFn = null;
-let opHandler = null;
-let deprecationWarningFn = null;
-function setTensorTracker(fn) {
-  trackerFn = fn;
-}
-function setOpHandler(handler) {
-  opHandler = handler;
-}
-function setDeprecationWarningFn(fn) {
-  deprecationWarningFn = fn;
-}
-class Tensor {
-  constructor(shape, dtype, dataId, id) {
-    this.kept = false;
-    this.isDisposedInternal = false;
-    this.shape = shape.slice();
-    this.dtype = dtype || "float32";
-    this.size = sizeFromShape(shape);
-    this.strides = computeStrides(shape);
-    this.dataId = dataId;
-    this.id = id;
-    this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  async buffer() {
-    const vals = await this.data();
-    return opHandler.buffer(this.shape, this.dtype, vals);
-  }
-  bufferSync() {
-    return opHandler.buffer(this.shape, this.dtype, this.dataSync());
-  }
-  async array() {
-    const vals = await this.data();
-    return toNestedArray(this.shape, vals);
-  }
-  arraySync() {
-    return toNestedArray(this.shape, this.dataSync());
-  }
-  async data() {
-    this.throwIfDisposed();
-    const data = trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      const bytes = await data;
-      try {
-        return bytes.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  dataSync() {
-    this.throwIfDisposed();
-    const data = trackerFn().readSync(this.dataId);
-    if (this.dtype === "string") {
-      try {
-        return data.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  async bytes() {
-    this.throwIfDisposed();
-    const data = await trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      return data;
-    } else {
-      return new Uint8Array(data.buffer);
-    }
-  }
-  dispose() {
-    if (this.isDisposed) {
-      return;
-    }
-    trackerFn().disposeTensor(this);
-    this.isDisposedInternal = true;
-  }
-  get isDisposed() {
-    return this.isDisposedInternal;
-  }
-  throwIfDisposed() {
-    if (this.isDisposed) {
-      throw new Error(`Tensor is disposed.`);
-    }
-  }
-  print(verbose = false) {
-    return opHandler.print(this, verbose);
-  }
-  clone() {
-    this.throwIfDisposed();
-    return opHandler.clone(this);
-  }
-  toString(verbose = false) {
-    const vals = this.dataSync();
-    return tensorToString(vals, this.shape, this.dtype, verbose);
-  }
-  cast(dtype) {
-    this.throwIfDisposed();
-    return opHandler.cast(this, dtype);
-  }
-  variable(trainable = true, name, dtype) {
-    this.throwIfDisposed();
-    return trackerFn().makeVariable(this, trainable, name, dtype);
-  }
-}
-Object.defineProperty(Tensor, Symbol.hasInstance, {
-  value: (instance) => {
-    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
-  }
-});
-class Variable extends Tensor {
-  constructor(initialValue, trainable, name, tensorId) {
-    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
-    this.trainable = trainable;
-    this.name = name;
-  }
-  assign(newValue) {
-    if (newValue.dtype !== this.dtype) {
-      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
-    }
-    if (!arraysEqual(newValue.shape, this.shape)) {
-      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
-    }
-    trackerFn().disposeTensor(this);
-    this.dataId = newValue.dataId;
-    trackerFn().incRef(this, null);
-  }
-  dispose() {
-    trackerFn().disposeVariable(this);
-    this.isDisposedInternal = true;
-  }
-}
-Object.defineProperty(Variable, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/types.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Rank;
-(function(Rank2) {
-  Rank2["R0"] = "R0";
-  Rank2["R1"] = "R1";
-  Rank2["R2"] = "R2";
-  Rank2["R3"] = "R3";
-  Rank2["R4"] = "R4";
-  Rank2["R5"] = "R5";
-  Rank2["R6"] = "R6";
-})(Rank || (Rank = {}));
-var UpcastInt32AndMap;
-(function(UpcastInt32AndMap2) {
-  UpcastInt32AndMap2["float32"] = "float32";
-  UpcastInt32AndMap2["int32"] = "int32";
-  UpcastInt32AndMap2["bool"] = "int32";
-  UpcastInt32AndMap2["complex64"] = "complex64";
-})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
-var UpcastBoolAndMap;
-(function(UpcastBoolAndMap2) {
-  UpcastBoolAndMap2["float32"] = "float32";
-  UpcastBoolAndMap2["int32"] = "int32";
-  UpcastBoolAndMap2["bool"] = "bool";
-  UpcastBoolAndMap2["complex64"] = "complex64";
-})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
-var UpcastFloat32AndMap;
-(function(UpcastFloat32AndMap2) {
-  UpcastFloat32AndMap2["float32"] = "float32";
-  UpcastFloat32AndMap2["int32"] = "float32";
-  UpcastFloat32AndMap2["bool"] = "float32";
-  UpcastFloat32AndMap2["complex64"] = "complex64";
-})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
-var UpcastComplex64AndMap;
-(function(UpcastComplex64AndMap2) {
-  UpcastComplex64AndMap2["float32"] = "complex64";
-  UpcastComplex64AndMap2["int32"] = "complex64";
-  UpcastComplex64AndMap2["bool"] = "complex64";
-  UpcastComplex64AndMap2["complex64"] = "complex64";
-})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
-const upcastTypeMap = {
-  float32: UpcastFloat32AndMap,
-  int32: UpcastInt32AndMap,
-  bool: UpcastBoolAndMap,
-  complex64: UpcastComplex64AndMap
-};
-function upcastType(typeA, typeB) {
-  if (typeA === "string" || typeB === "string") {
-    if (typeA === "string" && typeB === "string") {
-      return "string";
-    }
-    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
-  }
-  return upcastTypeMap[typeA][typeB];
-}
-function sumOutType(type) {
-  return upcastType(type, "int32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
-const tensor_util_exports = {};
-__export(tensor_util_exports, {
-  assertTypesMatch: () => assertTypesMatch,
-  getTensorsInContainer: () => getTensorsInContainer,
-  isTensorInList: () => isTensorInList,
-  makeTypesMatch: () => makeTypesMatch
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTypesMatch(a, b) {
-  if (a.dtype === b.dtype) {
-    return [a, b];
-  }
-  const dtype = upcastType(a.dtype, b.dtype);
-  return [a.cast(dtype), b.cast(dtype)];
-}
-function assertTypesMatch(a, b) {
-  assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
-}
-function isTensorInList(tensor17, tensorList) {
-  return tensorList.some((x) => x.id === tensor17.id);
-}
-function getTensorsInContainer(result) {
-  const list = [];
-  const seen = new Set();
-  walkTensorContainer(result, list, seen);
-  return list;
-}
-function walkTensorContainer(container, list, seen) {
-  if (container == null) {
-    return;
-  }
-  if (container instanceof Tensor) {
-    list.push(container);
-    return;
-  }
-  if (!isIterable(container)) {
-    return;
-  }
-  const iterable = container;
-  for (const k in iterable) {
-    const val = iterable[k];
-    if (!seen.has(val)) {
-      seen.add(val);
-      walkTensorContainer(val, list, seen);
-    }
-  }
-}
-function isIterable(obj) {
-  return Array.isArray(obj) || typeof obj === "object";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/engine.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class EngineState {
-  constructor() {
-    this.registeredVariables = {};
-    this.nextTapeNodeId = 0;
-    this.numBytes = 0;
-    this.numTensors = 0;
-    this.numStringTensors = 0;
-    this.numDataBuffers = 0;
-    this.gradientDepth = 0;
-    this.kernelDepth = 0;
-    this.scopeStack = [];
-    this.numDataMovesStack = [];
-    this.nextScopeId = 0;
-    this.tensorInfo = new WeakMap();
-    this.profiling = false;
-    this.activeProfile = {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};
-  }
-  dispose() {
-    for (const variableName in this.registeredVariables) {
-      this.registeredVariables[variableName].dispose();
-    }
-  }
-}
-class Engine {
-  constructor(ENV3) {
-    this.ENV = ENV3;
-    this.registry = {};
-    this.registryFactory = {};
-    this.pendingBackendInitId = 0;
-    this.state = new EngineState();
-  }
-  async ready() {
-    if (this.pendingBackendInit != null) {
-      return this.pendingBackendInit.then(() => {
-      });
-    }
-    if (this.backendInstance != null) {
-      return;
-    }
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const success = await this.initializeBackend(backendName).success;
-      if (success) {
-        await this.setBackend(backendName);
-        return;
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  get backend() {
-    if (this.pendingBackendInit != null) {
-      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-    }
-    if (this.backendInstance == null) {
-      const {name, asyncInit} = this.initializeBackendsAndReturnBest();
-      if (asyncInit) {
-        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-      }
-      this.setBackend(name);
-    }
-    return this.backendInstance;
-  }
-  backendNames() {
-    return Object.keys(this.registryFactory);
-  }
-  findBackend(backendName) {
-    if (!(backendName in this.registry)) {
-      if (backendName in this.registryFactory) {
-        const {asyncInit} = this.initializeBackend(backendName);
-        if (asyncInit) {
-          return null;
-        }
-      } else {
-        return null;
-      }
-    }
-    return this.registry[backendName];
-  }
-  findBackendFactory(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      return null;
-    }
-    return this.registryFactory[backendName].factory;
-  }
-  registerBackend(backendName, factory, priority = 1) {
-    if (backendName in this.registryFactory) {
-      console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
-      return false;
-    }
-    this.registryFactory[backendName] = {factory, priority};
-    return true;
-  }
-  async setBackend(backendName) {
-    if (this.registryFactory[backendName] == null) {
-      throw new Error(`Backend name '${backendName}' not found in registry`);
-    }
-    this.backendName = backendName;
-    if (this.registry[backendName] == null) {
-      this.backendInstance = null;
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      const result = asyncInit ? await success : success;
-      if (!result) {
-        return false;
-      }
-    }
-    this.backendInstance = this.registry[backendName];
-    this.setupRegisteredKernels();
-    this.profiler = new Profiler(this.backendInstance);
-    return true;
-  }
-  setupRegisteredKernels() {
-    const kernels = getKernelsForBackend(this.backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.setupFunc != null) {
-        kernel.setupFunc(this.backendInstance);
-      }
-    });
-  }
-  disposeRegisteredKernels(backendName) {
-    const kernels = getKernelsForBackend(backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.disposeFunc != null) {
-        kernel.disposeFunc(this.registry[backendName]);
-      }
-    });
-  }
-  initializeBackend(backendName) {
-    const registryFactoryEntry = this.registryFactory[backendName];
-    if (registryFactoryEntry == null) {
-      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
-    }
-    try {
-      const backend2 = registryFactoryEntry.factory();
-      if (Promise.resolve(backend2) === backend2) {
-        const promiseId = ++this.pendingBackendInitId;
-        const success = backend2.then((backendInstance) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.registry[backendName] = backendInstance;
-          this.pendingBackendInit = null;
-          return true;
-        }).catch((err) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.pendingBackendInit = null;
-          console.warn(`Initialization of backend ${backendName} failed`);
-          console.warn(err.stack || err.message);
-          return false;
-        });
-        this.pendingBackendInit = success;
-        return {success, asyncInit: true};
-      } else {
-        this.registry[backendName] = backend2;
-        return {success: true, asyncInit: false};
-      }
-    } catch (err) {
-      console.warn(`Initialization of backend ${backendName} failed`);
-      console.warn(err.stack || err.message);
-      return {success: false, asyncInit: false};
-    }
-  }
-  removeBackend(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      throw new Error(`${backendName} backend not found in registry`);
-    }
-    if (this.backendName === backendName && this.pendingBackendInit != null) {
-      this.pendingBackendInitId++;
-    }
-    if (backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    delete this.registryFactory[backendName];
-    if (this.backendName === backendName) {
-      this.pendingBackendInit = null;
-      this.backendName = null;
-      this.backendInstance = null;
-    }
-  }
-  getSortedBackends() {
-    if (Object.keys(this.registryFactory).length === 0) {
-      throw new Error("No backend found in registry.");
-    }
-    return Object.keys(this.registryFactory).sort((a, b) => {
-      return this.registryFactory[b].priority - this.registryFactory[a].priority;
-    });
-  }
-  initializeBackendsAndReturnBest() {
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      if (asyncInit || success) {
-        return {name: backendName, asyncInit};
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  moveData(backend2, dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    const srcBackend = info.backend;
-    const values = this.readSync(dataId);
-    srcBackend.disposeData(dataId);
-    info.backend = backend2;
-    backend2.move(dataId, values, info.shape, info.dtype);
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
-    }
-  }
-  tidy(nameOrFn, fn) {
-    let name = null;
-    if (fn == null) {
-      if (typeof nameOrFn !== "function") {
-        throw new Error("Please provide a function to tidy()");
-      }
-      fn = nameOrFn;
-    } else {
-      if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
-        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
-      }
-      if (typeof fn !== "function") {
-        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
-      }
-      name = nameOrFn;
-    }
-    let result;
-    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
-      result = fn();
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      return result;
-    });
-  }
-  scopedRun(start, end, f) {
-    start();
-    try {
-      const res = f();
-      end();
-      return res;
-    } catch (ex) {
-      end();
-      throw ex;
-    }
-  }
-  nextTensorId() {
-    return Engine.nextTensorId++;
-  }
-  nextVariableId() {
-    return Engine.nextVariableId++;
-  }
-  clone(x) {
-    const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
-    const inputs = {x};
-    const grad2 = (dy) => ({
-      x: () => {
-        const dtype = "float32";
-        const gradInputs = {x: dy};
-        const attrs = {dtype};
-        return ENGINE.runKernelFunc((backend2) => backend2.cast(dy, dtype), gradInputs, null, Cast, attrs);
-      }
-    });
-    const saved = [];
-    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
-    return y;
-  }
-  runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {
-    const forwardFunc = null;
-    const backwardsFunc = null;
-    return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
-  }
-  shouldCheckForMemLeaks() {
-    return this.ENV.getBool("IS_TEST");
-  }
-  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
-    const numDataIdsAfter = this.backend.numDataIds();
-    let numOutputDataIds = 0;
-    outInfos.forEach((info) => {
-      numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
-    });
-    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
-    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
-    if (dataIdsLeaked > 0) {
-      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
-    }
-  }
-  runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
-    let outputs;
-    let saved = [];
-    const isTapeOn = this.isTapeOn();
-    if (kernelName == null) {
-      kernelName = this.state.activeScope != null ? this.state.activeScope.name : "";
-    }
-    const startingBytecount = this.state.numBytes;
-    const startingNumTensors = this.state.numTensors;
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack.push(0);
-    }
-    let kernelFunc;
-    const kernel = getKernel(kernelName, this.backendName);
-    let out;
-    if (kernel != null) {
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
-        const outInfos = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
-        }
-        const outTensors = outInfos.map(({dataId, shape, dtype}) => this.makeTensorFromDataId(dataId, shape, dtype));
-        if (isTapeOn) {
-          let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
-          if (tensorsToSave == null) {
-            if (outputsToSave == null) {
-              outputsToSave = [];
-            }
-            const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);
-            tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);
-          }
-          saved = this.saveTensorsForBackwardMode(tensorsToSave);
-        }
-        return outTensors;
-      };
-    } else {
-      const saveFunc = (tensors) => {
-        if (!isTapeOn) {
-          return;
-        }
-        saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-      };
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
-        const outs = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
-        }
-        return outs;
-      };
-    }
-    let kernelProfile;
-    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-      if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
-        outputs = kernelFunc();
-      } else {
-        kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());
-        if (this.ENV.getBool("DEBUG")) {
-          this.profiler.logKernelProfile(kernelProfile);
-        }
-        outputs = kernelProfile.outputs;
-      }
-    });
-    if (isTapeOn) {
-      this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);
-    }
-    if (this.state.profiling) {
-      this.state.activeProfile.kernels.push({
-        name: kernelName,
-        bytesAdded: this.state.numBytes - startingBytecount,
-        totalBytesSnapshot: this.state.numBytes,
-        tensorsAdded: this.state.numTensors - startingNumTensors,
-        totalTensorsSnapshot: this.state.numTensors,
-        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
-        outputShapes: outputs.map((item) => item.shape),
-        kernelTimeMs: kernelProfile.timeMs,
-        extraInfo: kernelProfile.extraInfo
-      });
-    }
-    return Array.isArray(out) ? outputs : outputs[0];
-  }
-  saveTensorsForBackwardMode(tensors) {
-    const saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-    return saved;
-  }
-  getTensorsForGradient(kernelName, inputs, outputs) {
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      const inputsToSave = gradConfig.inputsToSave || [];
-      const outputsToSave = gradConfig.outputsToSave || [];
-      let inputTensorsToSave;
-      if (gradConfig.saveAllInputs) {
-        assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
-        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
-      } else {
-        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
-      }
-      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
-      return inputTensorsToSave.concat(outputTensorsToSave);
-    }
-    return null;
-  }
-  makeTensor(values, shape, dtype, backend2) {
-    if (values == null) {
-      throw new Error("Values passed to engine.makeTensor() are null");
-    }
-    dtype = dtype || "float32";
-    backend2 = backend2 || this.backend;
-    let backendVals = values;
-    if (dtype === "string" && isString(values[0])) {
-      backendVals = values.map((d) => encodeString(d));
-    }
-    const dataId = backend2.write(backendVals, shape, dtype);
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    if (dtype === "string") {
-      const info = this.state.tensorInfo.get(dataId);
-      const newBytes = bytesFromStringArray(backendVals);
-      this.state.numBytes += newBytes - info.bytes;
-      info.bytes = newBytes;
-    }
-    return t;
-  }
-  makeTensorFromDataId(dataId, shape, dtype, backend2) {
-    dtype = dtype || "float32";
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    return t;
-  }
-  makeVariable(initialValue, trainable = true, name, dtype) {
-    name = name || this.nextVariableId().toString();
-    if (dtype != null && dtype !== initialValue.dtype) {
-      initialValue = initialValue.cast(dtype);
-    }
-    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
-    if (this.state.registeredVariables[v.name] != null) {
-      throw new Error(`Variable with name ${v.name} was already registered`);
-    }
-    this.state.registeredVariables[v.name] = v;
-    this.incRef(v, this.backend);
-    return v;
-  }
-  incRef(a, backend2) {
-    const refCount = this.state.tensorInfo.has(a.dataId) ? this.state.tensorInfo.get(a.dataId).refCount : 0;
-    this.state.numTensors++;
-    if (a.dtype === "string") {
-      this.state.numStringTensors++;
-    }
-    if (refCount === 0) {
-      this.state.numDataBuffers++;
-      let bytes = 0;
-      if (a.dtype !== "complex64" && a.dtype !== "string") {
-        bytes = a.size * bytesPerElement(a.dtype);
-      }
-      this.state.tensorInfo.set(a.dataId, {
-        backend: backend2 || this.backend,
-        dtype: a.dtype,
-        shape: a.shape,
-        bytes,
-        refCount: 0
-      });
-      this.state.numBytes += bytes;
-    }
-    this.state.tensorInfo.get(a.dataId).refCount++;
-    if (!(a instanceof Variable)) {
-      this.track(a);
-    }
-  }
-  disposeTensor(a) {
-    if (!this.state.tensorInfo.has(a.dataId)) {
-      return;
-    }
-    this.state.numTensors--;
-    if (a.dtype === "string") {
-      this.state.numStringTensors--;
-    }
-    const info = this.state.tensorInfo.get(a.dataId);
-    const refCount = info.refCount;
-    if (refCount <= 1) {
-      if (a.dtype !== "complex64") {
-        this.state.numBytes -= info.bytes;
-      }
-      this.state.numDataBuffers--;
-      info.backend.disposeData(a.dataId);
-      this.state.tensorInfo.delete(a.dataId);
-    } else {
-      this.state.tensorInfo.get(a.dataId).refCount--;
-    }
-  }
-  disposeVariables() {
-    for (const varName in this.state.registeredVariables) {
-      const v = this.state.registeredVariables[varName];
-      this.disposeVariable(v);
-    }
-  }
-  disposeVariable(v) {
-    this.disposeTensor(v);
-    if (this.state.registeredVariables[v.name] != null) {
-      delete this.state.registeredVariables[v.name];
-    }
-  }
-  memory() {
-    const info = this.backend.memory();
-    info.numTensors = this.state.numTensors;
-    info.numDataBuffers = this.state.numDataBuffers;
-    info.numBytes = this.state.numBytes;
-    if (this.state.numStringTensors > 0) {
-      info.unreliable = true;
-      if (info.reasons == null) {
-        info.reasons = [];
-      }
-      info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
-    }
-    return info;
-  }
-  async profile(query) {
-    this.state.profiling = true;
-    const startBytes = this.state.numBytes;
-    const startNumTensors = this.state.numTensors;
-    this.state.activeProfile.kernels = [];
-    this.state.activeProfile.result = await query();
-    this.state.profiling = false;
-    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
-    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
-    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
-    for (const kernel of this.state.activeProfile.kernels) {
-      kernel.kernelTimeMs = await kernel.kernelTimeMs;
-      kernel.extraInfo = await kernel.extraInfo;
-    }
-    return this.state.activeProfile;
-  }
-  isTapeOn() {
-    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
-  }
-  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
-    const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      gradientsFunc = gradConfig.gradFunc;
-    }
-    if (gradientsFunc != null) {
-      tapeNode.gradient = (dys) => {
-        dys = dys.map((dy, i) => {
-          if (dy == null) {
-            const output = outputs[i];
-            const vals = makeZerosTypedArray(output.size, output.dtype);
-            return this.makeTensor(vals, output.shape, output.dtype);
-          }
-          return dy;
-        });
-        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
-      };
-    }
-    this.state.activeTape.push(tapeNode);
-  }
-  keep(result) {
-    result.kept = true;
-    return result;
-  }
-  startTape() {
-    if (this.state.gradientDepth === 0) {
-      this.state.activeTape = [];
-    }
-    this.state.gradientDepth++;
-  }
-  endTape() {
-    this.state.gradientDepth--;
-  }
-  startScope(name) {
-    const scopeInfo = {
-      track: [],
-      name: "unnamed scope",
-      id: this.state.nextScopeId++
-    };
-    if (name) {
-      scopeInfo.name = name;
-    }
-    this.state.scopeStack.push(scopeInfo);
-    this.state.activeScope = scopeInfo;
-  }
-  endScope(result) {
-    const tensorsToTrackInParent = getTensorsInContainer(result);
-    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
-    for (let i = 0; i < this.state.activeScope.track.length; i++) {
-      const tensor17 = this.state.activeScope.track[i];
-      if (!tensor17.kept && !tensorsToTrackInParentSet.has(tensor17.id)) {
-        tensor17.dispose();
-      }
-    }
-    const oldScope = this.state.scopeStack.pop();
-    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
-    tensorsToTrackInParent.forEach((tensor17) => {
-      if (!tensor17.kept && tensor17.scopeId === oldScope.id) {
-        this.track(tensor17);
-      }
-    });
-  }
-  gradients(f, xs, dy, allowNoGradients = false) {
-    assert(xs.length > 0, () => "gradients() received an empty list of xs.");
-    if (dy != null && dy.dtype !== "float32") {
-      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
-    }
-    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
-    assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
-    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
-    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
-      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
-    }
-    return this.tidy("backward", () => {
-      const accumulatedGradientMap = {};
-      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
-      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
-      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
-      if (this.state.gradientDepth === 0) {
-        this.state.activeTape.forEach((node2) => {
-          for (const tensor17 of node2.saved) {
-            tensor17.dispose();
-          }
-        });
-        this.state.activeTape = null;
-      }
-      return {value: y, grads: grads2};
-    });
-  }
-  customGrad(f) {
-    assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
-    return (...inputs) => {
-      assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
-      let res;
-      const inputMap = {};
-      inputs.forEach((input, i) => {
-        inputMap[i] = input;
-      });
-      return this.runKernelFunc((_, save) => {
-        res = f(...[...inputs, save]);
-        assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
-        assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
-        return res.value;
-      }, inputMap, (dy, saved) => {
-        const gradRes = res.gradFunc(dy, saved);
-        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
-        assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
-        assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
-        const gradMap = {};
-        grads2.forEach((grad2, i) => {
-          gradMap[i] = () => grad2;
-        });
-        return gradMap;
-      });
-    };
-  }
-  readSync(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.readSync(dataId);
-  }
-  read(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.read(dataId);
-  }
-  async time(query) {
-    const start = now();
-    const timingInfo = await this.backend.time(query);
-    timingInfo.wallMs = now() - start;
-    return timingInfo;
-  }
-  track(result) {
-    if (this.state.activeScope != null) {
-      result.scopeId = this.state.activeScope.id;
-      this.state.activeScope.track.push(result);
-    }
-    return result;
-  }
-  get registeredVariables() {
-    return this.state.registeredVariables;
-  }
-  reset() {
-    this.pendingBackendInitId++;
-    this.state.dispose();
-    this.ENV.reset();
-    this.state = new EngineState();
-    for (const backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    this.backendName = null;
-    this.backendInstance = null;
-    this.pendingBackendInit = null;
-  }
-}
-Engine.nextTensorId = 0;
-Engine.nextVariableId = 0;
-function ones(shape) {
-  const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
-  return ENGINE.makeTensor(values, shape, "float32");
-}
-function getOrMakeEngine() {
-  const ns = getGlobalNamespace();
-  if (ns._tfengine == null) {
-    const environment12 = new Environment(ns);
-    ns._tfengine = new Engine(environment12);
-  }
-  setEnvironmentGlobal(ns._tfengine.ENV);
-  setTensorTracker(() => ns._tfengine);
-  return ns._tfengine;
-}
-const ENGINE = getOrMakeEngine();
-function add(a, b) {
-  const inputs = {a, b};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.add(a, b);
-    save([a, b]);
-    return res;
-  }, inputs, null, Add);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/device_util.js
-const device_util_exports = {};
-__export(device_util_exports, {
-  isBrowser: () => isBrowser,
-  isMobile: () => isMobile
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function _isNavigatorDefined() {
-  return typeof navigator !== "undefined" && navigator != null;
-}
-function isMobile() {
-  if (_isNavigatorDefined()) {
-    const a = navigator.userAgent || navigator.vendor || window.opera;
-    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
-  }
-  return false;
-}
-function isBrowser() {
-  return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/flags.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ENV2 = env();
-ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
-  if (debugValue) {
-    console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
-  }
-});
-ENV2.registerFlag("IS_BROWSER", () => isBrowser());
-ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
-ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-ENV2.registerFlag("PROD", () => false);
-ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
-ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-ENV2.registerFlag("IS_TEST", () => false);
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function inferShape(val, dtype) {
-  let firstElem = val;
-  if (isTypedArray(val)) {
-    return dtype === "string" ? [] : [val.length];
-  }
-  if (!Array.isArray(val)) {
-    return [];
-  }
-  const shape = [];
-  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
-    shape.push(firstElem.length);
-    firstElem = firstElem[0];
-  }
-  if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
-    deepAssertShapeConsistency(val, shape, []);
-  }
-  return shape;
-}
-function deepAssertShapeConsistency(val, shape, indices) {
-  indices = indices || [];
-  if (!Array.isArray(val) && !isTypedArray(val)) {
-    assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
-    return;
-  }
-  assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
-  assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
-  const subShape = shape.slice(1);
-  for (let i = 0; i < val.length; ++i) {
-    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
-  }
-}
-function assertDtype(expectedDtype, actualDType, argName, functionName) {
-  if (expectedDtype == null) {
-    return;
-  }
-  if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
-  }
-}
-function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
-  if (x instanceof Tensor) {
-    assertDtype(parseAsDtype, x.dtype, argName, functionName);
-    return x;
-  }
-  let inferredDtype = inferDtype(x);
-  if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
-    inferredDtype = parseAsDtype;
-  }
-  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
-  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
-    const type = x == null ? "null" : x.constructor.name;
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
-  }
-  const inferredShape = inferShape(x, inferredDtype);
-  if (!isTypedArray(x) && !Array.isArray(x)) {
-    x = [x];
-  }
-  const skipTypedArray = true;
-  const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
-  return ENGINE.makeTensor(values, inferredShape, inferredDtype);
-}
-function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
-  if (!Array.isArray(arg)) {
-    throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
-  }
-  const tensors = arg;
-  return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OP_SCOPE_SUFFIX = "__op";
-function op(f) {
-  const keys = Object.keys(f);
-  if (keys.length !== 1) {
-    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
-  }
-  let opName = keys[0];
-  const fn = f[opName];
-  if (opName.endsWith("_")) {
-    opName = opName.substring(0, opName.length - 1);
-  }
-  opName = opName + OP_SCOPE_SUFFIX;
-  const f2 = (...args) => {
-    ENGINE.startScope(opName);
-    try {
-      const result = fn(...args);
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      ENGINE.endScope(result);
-      return result;
-    } catch (ex) {
-      ENGINE.endScope(null);
-      throw ex;
-    }
-  };
-  Object.defineProperty(f2, "name", {value: opName, configurable: true});
-  return f2;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function complex_(real6, imag6) {
-  const $real = convertToTensor(real6, "real", "complex");
-  const $imag = convertToTensor(imag6, "imag", "complex");
-  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
-  const forward = (backend2) => {
-    return backend2.complex($real, $imag);
-  };
-  const inputs = {real: $real, imag: $imag};
-  return ENGINE.runKernelFunc(forward, inputs, null, Complex);
-}
-const complex = op({complex_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTensor(values, shape, inferredShape, dtype) {
-  if (dtype == null) {
-    dtype = inferDtype(values);
-  }
-  if (dtype === "complex64") {
-    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
-  }
-  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
-    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
-  }
-  if (shape != null) {
-    assertNonNegativeIntegerDimensions(shape);
-    const providedSize = sizeFromShape(shape);
-    const inferredSize = sizeFromShape(inferredShape);
-    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-    for (let i = 0; i < inferredShape.length; ++i) {
-      const inferred = inferredShape[i];
-      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
-      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
-    }
-  }
-  if (!isTypedArray(values) && !Array.isArray(values)) {
-    values = [values];
-  }
-  shape = shape || inferredShape;
-  values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4(values, shape, dtype) {
-  const inferredShape = inferShape(values, dtype);
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/types.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DTYPE_VALUE_SIZE_MAP = {
-  float32: 4,
-  float16: 2,
-  int32: 4,
-  uint16: 2,
-  uint8: 1,
-  bool: 1,
-  complex64: 8
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const NUM_BYTES_STRING_LENGTH = 4;
-async function encodeWeights(tensors, group) {
-  const specs = [];
-  const dataPromises = [];
-  const names = Array.isArray(tensors) ? tensors.map((tensor17) => tensor17.name) : Object.keys(tensors);
-  for (let i = 0; i < names.length; ++i) {
-    const name = names[i];
-    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
-    if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
-      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
-    }
-    const spec = {name, shape: t.shape, dtype: t.dtype};
-    if (t.dtype === "string") {
-      const utf8bytes = new Promise(async (resolve) => {
-        const vals = await t.bytes();
-        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
-        const bytes = new Uint8Array(totalNumBytes);
-        let offset = 0;
-        for (let i2 = 0; i2 < vals.length; i2++) {
-          const val = vals[i2];
-          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
-          bytes.set(bytesOfLength, offset);
-          offset += NUM_BYTES_STRING_LENGTH;
-          bytes.set(val, offset);
-          offset += val.length;
-        }
-        resolve(bytes);
-      });
-      dataPromises.push(utf8bytes);
-    } else {
-      dataPromises.push(t.data());
-    }
-    if (group != null) {
-      spec.group = group;
-    }
-    specs.push(spec);
-  }
-  const tensorValues = await Promise.all(dataPromises);
-  return {data: concatenateTypedArrays(tensorValues), specs};
-}
-function decodeWeights(buffer11, specs) {
-  const out = {};
-  let float16Decode;
-  let offset = 0;
-  for (const spec of specs) {
-    const name = spec.name;
-    const dtype = spec.dtype;
-    const shape = spec.shape;
-    const size = sizeFromShape(shape);
-    let values;
-    if ("quantization" in spec) {
-      const quantization = spec.quantization;
-      if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-        if (!("min" in quantization && "scale" in quantization)) {
-          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
-        }
-      } else if (quantization.dtype === "float16") {
-        if (dtype !== "float32") {
-          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
-        }
-      } else {
-        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      }
-      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * quantizationSizeFactor);
-      const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
-      if (dtype === "float32") {
-        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-          values = new Float32Array(quantizedArray.length);
-          for (let i = 0; i < quantizedArray.length; i++) {
-            const v = quantizedArray[i];
-            values[i] = v * quantization.scale + quantization.min;
-          }
-        } else if (quantization.dtype === "float16") {
-          if (float16Decode === void 0) {
-            float16Decode = getFloat16Decoder();
-          }
-          values = float16Decode(quantizedArray);
-        } else {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
-        }
-      } else if (dtype === "int32") {
-        if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
-        }
-        values = new Int32Array(quantizedArray.length);
-        for (let i = 0; i < quantizedArray.length; i++) {
-          const v = quantizedArray[i];
-          values[i] = Math.round(v * quantization.scale + quantization.min);
-        }
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * quantizationSizeFactor;
-    } else if (dtype === "string") {
-      const size2 = sizeFromShape(spec.shape);
-      values = [];
-      for (let i = 0; i < size2; i++) {
-        const byteLength = new Uint32Array(buffer11.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
-        offset += NUM_BYTES_STRING_LENGTH;
-        const bytes = new Uint8Array(buffer11.slice(offset, offset + byteLength));
-        values.push(bytes);
-        offset += byteLength;
-      }
-    } else {
-      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * dtypeFactor);
-      if (dtype === "float32") {
-        values = new Float32Array(byteBuffer);
-      } else if (dtype === "int32") {
-        values = new Int32Array(byteBuffer);
-      } else if (dtype === "bool") {
-        values = new Uint8Array(byteBuffer);
-      } else if (dtype === "complex64") {
-        values = new Float32Array(byteBuffer);
-        const real6 = new Float32Array(values.length / 2);
-        const image2 = new Float32Array(values.length / 2);
-        for (let i = 0; i < real6.length; i++) {
-          real6[i] = values[i * 2];
-          image2[i] = values[i * 2 + 1];
-        }
-        const realTensor = tensor4(real6, shape, "float32");
-        const imageTensor = tensor4(image2, shape, "float32");
-        out[name] = complex(realTensor, imageTensor);
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * dtypeFactor;
-    }
-    if (dtype !== "complex64") {
-      out[name] = tensor4(values, shape, dtype);
-    }
-  }
-  return out;
-}
-function concatenateTypedArrays(xs) {
-  if (xs === null) {
-    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
-  }
-  let totalByteLength = 0;
-  const normalizedXs = [];
-  xs.forEach((x) => {
-    totalByteLength += x.byteLength;
-    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
-    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
-      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
-    }
-  });
-  const y = new Uint8Array(totalByteLength);
-  let offset = 0;
-  normalizedXs.forEach((x) => {
-    y.set(new Uint8Array(x.buffer), offset);
-    offset += x.byteLength;
-  });
-  return y.buffer;
-}
-const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
-function stringByteLength(str) {
-  if (useNodeBuffer) {
-    return Buffer.byteLength(str);
-  }
-  return new Blob([str]).size;
-}
-function concatenateArrayBuffers(buffers) {
-  if (buffers.length === 1) {
-    return buffers[0];
-  }
-  let totalByteLength = 0;
-  buffers.forEach((buffer11) => {
-    totalByteLength += buffer11.byteLength;
-  });
-  const temp = new Uint8Array(totalByteLength);
-  let offset = 0;
-  buffers.forEach((buffer11) => {
-    temp.set(new Uint8Array(buffer11), offset);
-    offset += buffer11.byteLength;
-  });
-  return temp.buffer;
-}
-function basename(path) {
-  const SEPARATOR = "/";
-  path = path.trim();
-  while (path.endsWith(SEPARATOR)) {
-    path = path.slice(0, path.length - 1);
-  }
-  const items = path.split(SEPARATOR);
-  return items[items.length - 1];
-}
-function getModelArtifactsInfoForJSON(modelArtifacts) {
-  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-    throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  }
-  return {
-    dateSaved: new Date(),
-    modelTopologyType: "JSON",
-    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
-    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
-    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
-  };
-}
-function computeFloat16MantisaTable() {
-  const convertMantissa = (i) => {
-    let m = i << 13;
-    let e = 0;
-    while ((m & 8388608) === 0) {
-      e -= 8388608;
-      m <<= 1;
-    }
-    m &= ~8388608;
-    e += 947912704;
-    return m | e;
-  };
-  const mantisaTable = new Uint32Array(2048);
-  mantisaTable[0] = 0;
-  for (let i = 1; i < 1024; i++) {
-    mantisaTable[i] = convertMantissa(i);
-  }
-  for (let i = 1024; i < 2048; i++) {
-    mantisaTable[i] = 939524096 + (i - 1024 << 13);
-  }
-  return mantisaTable;
-}
-function computeFloat16ExponentTable() {
-  const exponentTable = new Uint32Array(64);
-  exponentTable[0] = 0;
-  exponentTable[31] = 1199570944;
-  exponentTable[32] = 2147483648;
-  exponentTable[63] = 3347054592;
-  for (let i = 1; i < 31; i++) {
-    exponentTable[i] = i << 23;
-  }
-  for (let i = 33; i < 63; i++) {
-    exponentTable[i] = 2147483648 + (i - 32 << 23);
-  }
-  return exponentTable;
-}
-function computeFloat16OffsetTable() {
-  const offsetTable = new Uint32Array(64);
-  for (let i = 0; i < 64; i++) {
-    offsetTable[i] = 1024;
-  }
-  offsetTable[0] = offsetTable[32] = 0;
-  return offsetTable;
-}
-function getFloat16Decoder() {
-  const mantisaTable = computeFloat16MantisaTable();
-  const exponentTable = computeFloat16ExponentTable();
-  const offsetTable = computeFloat16OffsetTable();
-  return (quantizedArray) => {
-    const buffer11 = new ArrayBuffer(4 * quantizedArray.length);
-    const bufferUint32View = new Uint32Array(buffer11);
-    for (let index = 0; index < quantizedArray.length; index++) {
-      const float16Bits = quantizedArray[index];
-      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
-      bufferUint32View[index] = float32Bits;
-    }
-    return new Float32Array(buffer11);
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class IORouterRegistry {
-  constructor() {
-    this.saveRouters = [];
-    this.loadRouters = [];
-  }
-  static getInstance() {
-    if (IORouterRegistry.instance == null) {
-      IORouterRegistry.instance = new IORouterRegistry();
-    }
-    return IORouterRegistry.instance;
-  }
-  static registerSaveRouter(saveRouter) {
-    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
-  }
-  static registerLoadRouter(loadRouter) {
-    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
-  }
-  static getSaveHandlers(url) {
-    return IORouterRegistry.getHandlers(url, "save");
-  }
-  static getLoadHandlers(url, loadOptions) {
-    return IORouterRegistry.getHandlers(url, "load", loadOptions);
-  }
-  static getHandlers(url, handlerType, loadOptions) {
-    const validHandlers = [];
-    const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
-    routers.forEach((router) => {
-      const handler = router(url, loadOptions);
-      if (handler !== null) {
-        validHandlers.push(handler);
-      }
-    });
-    return validHandlers;
-  }
-}
-const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
-const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
-const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
-const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const URL_SCHEME_SUFFIX = "://";
-class ModelStoreManagerRegistry {
-  constructor() {
-    this.managers = {};
-  }
-  static getInstance() {
-    if (ModelStoreManagerRegistry.instance == null) {
-      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
-    }
-    return ModelStoreManagerRegistry.instance;
-  }
-  static registerManager(scheme, manager) {
-    assert(scheme != null, () => "scheme must not be undefined or null.");
-    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
-      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
-    }
-    assert(scheme.length > 0, () => "scheme must not be an empty string.");
-    const registry = ModelStoreManagerRegistry.getInstance();
-    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
-    registry.managers[scheme] = manager;
-  }
-  static getManager(scheme) {
-    const manager = this.getInstance().managers[scheme];
-    if (manager == null) {
-      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
-    }
-    return manager;
-  }
-  static getSchemes() {
-    return Object.keys(this.getInstance().managers);
-  }
-}
-function parseURL(url) {
-  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
-    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
-  }
-  return {
-    scheme: url.split(URL_SCHEME_SUFFIX)[0],
-    path: url.split(URL_SCHEME_SUFFIX)[1]
-  };
-}
-async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
-  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
-  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
-  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
-  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
-  const loadHandler = loadHandlers[0];
-  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
-  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
-  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
-  const saveHandler = saveHandlers[0];
-  const sourceScheme = parseURL(sourceURL).scheme;
-  const sourcePath = parseURL(sourceURL).path;
-  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
-  const modelArtifacts = await loadHandler.load();
-  if (deleteSource && sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  const saveResult = await saveHandler.save(modelArtifacts);
-  if (deleteSource && !sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  return saveResult.modelArtifactsInfo;
-}
-async function listModels() {
-  const schemes = ModelStoreManagerRegistry.getSchemes();
-  const out = {};
-  for (const scheme of schemes) {
-    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
-    for (const path in schemeOut) {
-      const url = scheme + URL_SCHEME_SUFFIX + path;
-      out[url] = schemeOut[path];
-    }
-  }
-  return out;
-}
-async function removeModel(url) {
-  const schemeAndPath = parseURL(url);
-  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
-  return manager.removeModel(schemeAndPath.path);
-}
-async function copyModel(sourceURL, destURL) {
-  const deleteSource = false;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-async function moveModel(sourceURL, destURL) {
-  const deleteSource = true;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function buffer(shape, dtype = "float32", values) {
-  dtype = dtype || "float32";
-  assertNonNegativeIntegerDimensions(shape);
-  return new TensorBuffer(shape, dtype, values);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cast_(x, dtype) {
-  const $x = convertToTensor(x, "x", "cast");
-  if (!isValidDtype(dtype)) {
-    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
-  }
-  if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
-    throw new Error("Only strings can be casted to strings");
-  }
-  const inputs = {x: $x};
-  const attrs = {dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.cast($x, dtype), inputs, null, Cast, attrs);
-}
-const cast = op({cast_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clone_(x) {
-  const $x = convertToTensor(x, "x", "clone", null);
-  const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Identity);
-}
-const clone = op({clone_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/print.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function print(x, verbose = false) {
-  console.log(x.toString(verbose));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-getOrMakeEngine();
-const opHandler2 = {
-  buffer,
-  cast,
-  clone,
-  print
-};
-setOpHandler(opHandler2);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DEFAULT_FILE_NAME_PREFIX = "model";
-const DEFAULT_JSON_EXTENSION_NAME = ".json";
-const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
-function defer(f) {
-  return new Promise((resolve) => setTimeout(resolve)).then(f);
-}
-class BrowserDownloads {
-  constructor(fileNamePrefix) {
-    if (!env().getBool("IS_BROWSER")) {
-      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    }
-    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
-      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
-    }
-    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
-      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
-    }
-    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
-    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
-  }
-  async save(modelArtifacts) {
-    if (typeof document === "undefined") {
-      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
-    }
-    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
-    } else {
-      const weightsManifest = [{
-        paths: ["./" + this.weightDataFileName],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelTopologyAndWeightManifest = {
-        modelTopology: modelArtifacts.modelTopology,
-        format: modelArtifacts.format,
-        generatedBy: modelArtifacts.generatedBy,
-        convertedBy: modelArtifacts.convertedBy,
-        weightsManifest
-      };
-      const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
-      const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
-      jsonAnchor.download = this.modelTopologyFileName;
-      jsonAnchor.href = modelTopologyAndWeightManifestURL;
-      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
-      if (modelArtifacts.weightData != null) {
-        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        weightDataAnchor.download = this.weightDataFileName;
-        weightDataAnchor.href = weightsURL;
-        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
-      }
-      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
-    }
-  }
-}
-BrowserDownloads.URL_SCHEME = "downloads://";
-class BrowserFiles {
-  constructor(files) {
-    if (files == null || files.length < 1) {
-      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
-    }
-    this.files = files;
-  }
-  async load() {
-    const jsonFile = this.files[0];
-    const weightFiles = this.files.slice(1);
-    return new Promise((resolve, reject) => {
-      const jsonReader = new FileReader();
-      jsonReader.onload = (event) => {
-        const modelJSON = JSON.parse(event.target.result);
-        const modelTopology = modelJSON.modelTopology;
-        if (modelTopology == null) {
-          reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        if (weightFiles.length === 0) {
-          resolve({modelTopology});
-        }
-        const weightsManifest = modelJSON.weightsManifest;
-        if (weightsManifest == null) {
-          reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        let pathToFile;
-        try {
-          pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
-        } catch (err) {
-          reject(err);
-          return;
-        }
-        const weightSpecs = [];
-        const paths = [];
-        const perFileBuffers = [];
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            paths.push(path);
-            perFileBuffers.push(null);
-          });
-          weightSpecs.push(...weightsGroup.weights);
-        });
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            const weightFileReader = new FileReader();
-            weightFileReader.onload = (event2) => {
-              const weightData = event2.target.result;
-              const index = paths.indexOf(path);
-              perFileBuffers[index] = weightData;
-              if (perFileBuffers.indexOf(null) === -1) {
-                resolve({
-                  modelTopology,
-                  weightSpecs,
-                  weightData: concatenateArrayBuffers(perFileBuffers),
-                  format: modelJSON.format,
-                  generatedBy: modelJSON.generatedBy,
-                  convertedBy: modelJSON.convertedBy,
-                  userDefinedMetadata: modelJSON.userDefinedMetadata
-                });
-              }
-            };
-            weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
-            weightFileReader.readAsArrayBuffer(pathToFile[path]);
-          });
-        });
-      };
-      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
-      jsonReader.readAsText(jsonFile);
-    });
-  }
-  checkManifestAndWeightFiles(manifest, files) {
-    const basenames = [];
-    const fileNames = files.map((file) => basename(file.name));
-    const pathToFile = {};
-    for (const group of manifest) {
-      group.paths.forEach((path) => {
-        const pathBasename = basename(path);
-        if (basenames.indexOf(pathBasename) !== -1) {
-          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
-        }
-        basenames.push(pathBasename);
-        if (fileNames.indexOf(pathBasename) === -1) {
-          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
-        } else {
-          pathToFile[path] = files[fileNames.indexOf(pathBasename)];
-        }
-      });
-    }
-    if (basenames.length !== files.length) {
-      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
-    }
-    return pathToFile;
-  }
-}
-const browserDownloadsRouter = (url) => {
-  if (!env().getBool("IS_BROWSER")) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
-      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
-function browserDownloads(fileNamePrefix = "model") {
-  return new BrowserDownloads(fileNamePrefix);
-}
-function browserFiles(files) {
-  return new BrowserFiles(files);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/progress.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
-  checkPromises(promises);
-  startFraction = startFraction == null ? 0 : startFraction;
-  endFraction = endFraction == null ? 1 : endFraction;
-  checkFraction(startFraction, endFraction);
-  let resolvedPromise = 0;
-  const registerMonitor = (promise) => {
-    promise.then((value) => {
-      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
-      onProgress(fraction);
-      return value;
-    });
-    return promise;
-  };
-  function checkPromises(promises2) {
-    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
-  }
-  function checkFraction(startFraction2, endFraction2) {
-    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
-    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
-    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
-  }
-  return Promise.all(promises.map(registerMonitor));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
-  if (loadOptions == null) {
-    loadOptions = {};
-  }
-  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
-  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
-  const fetchStartFraction = 0;
-  const fetchEndFraction = 0.5;
-  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
-  const bufferPromises = responses.map((response) => response.arrayBuffer());
-  const bufferStartFraction = 0.5;
-  const bufferEndFraction = 1;
-  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
-  return buffers;
-}
-async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
-  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
-  const loadWeights2 = weightsLoaderFactory(fetchWeights);
-  return loadWeights2(manifest, filePathPrefix, weightNames);
-}
-function weightsLoaderFactory(fetchWeightsFunction) {
-  return async (manifest, filePathPrefix = "", weightNames) => {
-    const groupIndicesToFetchMap = manifest.map(() => false);
-    const groupWeightsToFetch = {};
-    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
-    const allManifestWeightNames = [];
-    manifest.forEach((manifestGroupConfig, groupIndex) => {
-      let groupOffset = 0;
-      manifestGroupConfig.weights.forEach((weightsEntry) => {
-        const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
-        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
-        const enqueueWeightsForFetchingFn = () => {
-          groupIndicesToFetchMap[groupIndex] = true;
-          if (groupWeightsToFetch[groupIndex] == null) {
-            groupWeightsToFetch[groupIndex] = [];
-          }
-          groupWeightsToFetch[groupIndex].push({
-            manifestEntry: weightsEntry,
-            groupOffset,
-            sizeBytes: weightsBytes
-          });
-        };
-        if (weightNames != null) {
-          weightNames.forEach((weightName, weightIndex) => {
-            if (weightName === weightsEntry.name) {
-              enqueueWeightsForFetchingFn();
-              weightsFound[weightIndex] = true;
-            }
-          });
-        } else {
-          enqueueWeightsForFetchingFn();
-        }
-        allManifestWeightNames.push(weightsEntry.name);
-        groupOffset += weightsBytes;
-      });
-    });
-    if (!weightsFound.every((found) => found)) {
-      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
-      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
-    }
-    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
-      if (shouldFetch) {
-        accumulator.push(i);
-      }
-      return accumulator;
-    }, []);
-    const fetchUrls = [];
-    groupIndicesToFetch.forEach((i) => {
-      manifest[i].paths.forEach((filepath) => {
-        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
-        fetchUrls.push(fetchUrl);
-      });
-    });
-    const buffers = await fetchWeightsFunction(fetchUrls);
-    const weightsTensorMap = {};
-    let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach((i) => {
-      const numBuffers = manifest[i].paths.length;
-      let groupBytes = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        groupBytes += buffers[bufferIndexOffset + i2].byteLength;
-      }
-      const groupBuffer = new ArrayBuffer(groupBytes);
-      const groupByteBuffer = new Uint8Array(groupBuffer);
-      let groupBufferOffset = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        const buffer11 = new Uint8Array(buffers[bufferIndexOffset + i2]);
-        groupByteBuffer.set(buffer11, groupBufferOffset);
-        groupBufferOffset += buffer11.byteLength;
-      }
-      const weightsEntries = groupWeightsToFetch[i];
-      weightsEntries.forEach((weightsEntry) => {
-        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
-        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
-        for (const name in nameToTensorMap) {
-          weightsTensorMap[name] = nameToTensorMap[name];
-        }
-      });
-      bufferIndexOffset += numBuffers;
-    });
-    return weightsTensorMap;
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/http.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
-const JSON_TYPE = "application/json";
-class HTTPRequest {
-  constructor(path, loadOptions) {
-    this.DEFAULT_METHOD = "POST";
-    if (loadOptions == null) {
-      loadOptions = {};
-    }
-    this.weightPathPrefix = loadOptions.weightPathPrefix;
-    this.onProgress = loadOptions.onProgress;
-    this.weightUrlConverter = loadOptions.weightUrlConverter;
-    if (loadOptions.fetchFunc != null) {
-      assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
-      this.fetch = loadOptions.fetchFunc;
-    } else {
-      this.fetch = env().platform.fetch;
-    }
-    assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
-    if (Array.isArray(path)) {
-      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
-    }
-    this.path = path;
-    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
-      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
-    }
-    this.requestInit = loadOptions.requestInit || {};
-  }
-  async save(modelArtifacts) {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
-    }
-    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
-    init.body = new FormData();
-    const weightsManifest = [{
-      paths: ["./model.weights.bin"],
-      weights: modelArtifacts.weightSpecs
-    }];
-    const modelTopologyAndWeightManifest = {
-      modelTopology: modelArtifacts.modelTopology,
-      format: modelArtifacts.format,
-      generatedBy: modelArtifacts.generatedBy,
-      convertedBy: modelArtifacts.convertedBy,
-      userDefinedMetadata: modelArtifacts.userDefinedMetadata,
-      weightsManifest
-    };
-    init.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
-    if (modelArtifacts.weightData != null) {
-      init.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
-    }
-    const response = await this.fetch(this.path, init);
-    if (response.ok) {
-      return {
-        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
-        responses: [response]
-      };
-    } else {
-      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
-    }
-  }
-  async load() {
-    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
-    if (!modelConfigRequest.ok) {
-      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
-    }
-    let modelConfig;
-    try {
-      modelConfig = await modelConfigRequest.json();
-    } catch (e) {
-      let message = `Failed to parse model JSON of response from ${this.path}.`;
-      if (this.path.endsWith(".pb")) {
-        message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
-      } else {
-        message += " Please make sure the server is serving valid JSON for this request.";
-      }
-      throw new Error(message);
-    }
-    const modelTopology = modelConfig.modelTopology;
-    const weightsManifest = modelConfig.weightsManifest;
-    const generatedBy = modelConfig.generatedBy;
-    const convertedBy = modelConfig.convertedBy;
-    const format = modelConfig.format;
-    const userDefinedMetadata = modelConfig.userDefinedMetadata;
-    if (modelTopology == null && weightsManifest == null) {
-      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    }
-    let weightSpecs;
-    let weightData;
-    if (weightsManifest != null) {
-      const results = await this.loadWeights(weightsManifest);
-      [weightSpecs, weightData] = results;
-    }
-    return {
-      modelTopology,
-      weightSpecs,
-      weightData,
-      userDefinedMetadata,
-      generatedBy,
-      convertedBy,
-      format
-    };
-  }
-  async loadWeights(weightsManifest) {
-    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
-    const [prefix, suffix] = parseUrl(weightPath);
-    const pathPrefix = this.weightPathPrefix || prefix;
-    const weightSpecs = [];
-    for (const entry of weightsManifest) {
-      weightSpecs.push(...entry.weights);
-    }
-    const fetchURLs = [];
-    const urlPromises = [];
-    for (const weightsGroup of weightsManifest) {
-      for (const path of weightsGroup.paths) {
-        if (this.weightUrlConverter != null) {
-          urlPromises.push(this.weightUrlConverter(path));
-        } else {
-          fetchURLs.push(pathPrefix + path + suffix);
-        }
-      }
-    }
-    if (this.weightUrlConverter) {
-      fetchURLs.push(...await Promise.all(urlPromises));
-    }
-    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
-      requestInit: this.requestInit,
-      fetchFunc: this.fetch,
-      onProgress: this.onProgress
-    });
-    return [weightSpecs, concatenateArrayBuffers(buffers)];
-  }
-}
-HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
-function parseUrl(url) {
-  const lastSlash = url.lastIndexOf("/");
-  const lastSearchParam = url.lastIndexOf("?");
-  const prefix = url.substring(0, lastSlash);
-  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
-  return [prefix + "/", suffix];
-}
-function isHTTPScheme(url) {
-  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
-}
-const httpRouter = (url, loadOptions) => {
-  if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
-    return null;
-  } else {
-    let isHTTP = true;
-    if (Array.isArray(url)) {
-      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
-    } else {
-      isHTTP = isHTTPScheme(url);
-    }
-    if (isHTTP) {
-      return http(url, loadOptions);
-    }
-  }
-  return null;
-};
-IORouterRegistry.registerSaveRouter(httpRouter);
-IORouterRegistry.registerLoadRouter(httpRouter);
-function http(path, loadOptions) {
-  return new HTTPRequest(path, loadOptions);
-}
-function browserHTTPRequest(path, loadOptions) {
-  return http(path, loadOptions);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class PassthroughLoader {
-  constructor(modelArtifacts) {
-    this.modelArtifacts = modelArtifacts;
-  }
-  async load() {
-    return this.modelArtifacts;
-  }
-}
-class PassthroughSaver {
-  constructor(saveHandler) {
-    this.saveHandler = saveHandler;
-  }
-  async save(modelArtifacts) {
-    return this.saveHandler(modelArtifacts);
-  }
-}
-function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
-  if (arguments.length === 1) {
-    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
-    if (isModelArtifacts) {
-      return new PassthroughLoader(modelArtifacts);
-    } else {
-      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-      return new PassthroughLoader({modelTopology: modelArtifacts});
-    }
-  } else {
-    console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-    return new PassthroughLoader({
-      modelTopology: modelArtifacts,
-      weightSpecs,
-      weightData,
-      trainingConfig
-    });
-  }
-}
-function withSaveHandler(saveHandler) {
-  return new PassthroughSaver(saveHandler);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io.js
-const io_exports = {};
-__export(io_exports, {
-  browserFiles: () => browserFiles,
-  browserHTTPRequest: () => browserHTTPRequest,
-  concatenateArrayBuffers: () => concatenateArrayBuffers,
-  copyModel: () => copyModel,
-  decodeWeights: () => decodeWeights,
-  encodeWeights: () => encodeWeights,
-  fromMemory: () => fromMemory,
-  getLoadHandlers: () => getLoadHandlers,
-  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
-  getSaveHandlers: () => getSaveHandlers,
-  http: () => http,
-  isHTTPScheme: () => isHTTPScheme,
-  listModels: () => listModels,
-  loadWeights: () => loadWeights,
-  moveModel: () => moveModel,
-  registerLoadRouter: () => registerLoadRouter,
-  registerSaveRouter: () => registerSaveRouter,
-  removeModel: () => removeModel,
-  weightsLoaderFactory: () => weightsLoaderFactory,
-  withSaveHandler: () => withSaveHandler
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reshape_(x, shape) {
-  const $x = convertToTensor(x, "x", "reshape", null);
-  shape = inferFromImplicitShape(shape, $x.size);
-  assert($x.size === sizeFromShape(shape), () => "new shape and old shape must have the same number of elements.");
-  const inputs = {x: $x};
-  const attrs = {shape};
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.reshape($x, shape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Reshape, attrs);
-}
-const reshape = op({reshape_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function matMul_(a, b, transposeA = false, transposeB = false) {
-  let $a = convertToTensor(a, "a", "matMul");
-  let $b = convertToTensor(b, "b", "matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  const forward = (backend2, save) => {
-    save([a3D, b3D]);
-    return backend2.batchMatMul(a3D, b3D, transposeA, transposeB);
-  };
-  const inputs = {a: a3D, b: b3D};
-  const attrs = {transposeA, transposeB};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, BatchMatMul, attrs);
-  return reshape(res, outShape);
-}
-const matMul = op({matMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function oneHot_(indices, depth, onValue = 1, offValue = 0) {
-  if (depth < 2) {
-    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
-  }
-  const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
-  const outShape = [...$indices.shape, depth];
-  const forward = (backend2, save) => {
-    save([$indices]);
-    return reshape(backend2.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);
-  };
-  const inputs = {indices: $indices};
-  const attrs = {depth, onValue, offValue};
-  return ENGINE.runKernelFunc(forward, inputs, null, OneHot, attrs);
-}
-const oneHot = op({oneHot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function transpose_(x, perm) {
-  const $x = convertToTensor(x, "x", "transpose");
-  if (perm == null) {
-    perm = $x.shape.map((s, i) => i).reverse();
-  }
-  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
-  perm.forEach((axis) => {
-    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
-  });
-  if ($x.rank <= 1) {
-    return $x.clone();
-  }
-  const inputs = {x: $x};
-  const attrs = {perm};
-  return ENGINE.runKernelFunc((backend2) => backend2.transpose($x, perm), inputs, null, Transpose, attrs);
-}
-const transpose = op({transpose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function confusionMatrix_(labels, predictions, numClasses) {
-  const $labels = convertToTensor(labels, "labels", "confusionMatrix");
-  const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
-  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
-  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
-  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
-  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
-  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
-  const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
-  const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
-  const oneHotLabelsT = transpose(oneHotLabels);
-  return cast(matMul(oneHotLabelsT, oneHotPredictions), "int32");
-}
-const confusionMatrix = op({confusionMatrix_});
-
-// node_modules/@tensorflow/tfjs-core/dist/math.js
-const math_exports = {};
-__export(math_exports, {
-  confusionMatrix: () => confusionMatrix
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor3d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 3) {
-    throw new Error("tensor3d() requires shape to have three numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
-    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
-const browser_exports = {};
-__export(browser_exports, {
-  fromPixels: () => fromPixels,
-  toPixels: () => toPixels
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let fromPixels2DContext;
-function fromPixels_(pixels, numChannels = 3) {
-  if (numChannels > 4) {
-    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
-  }
-  if (pixels == null) {
-    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-  }
-  let isPixelData = false;
-  let isImageData = false;
-  let isVideo = false;
-  let isImage = false;
-  let isCanvasLike = false;
-  if (pixels.data instanceof Uint8Array) {
-    isPixelData = true;
-  } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
-    isImageData = true;
-  } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
-    isVideo = true;
-  } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
-    isImage = true;
-  } else if (pixels.getContext != null) {
-    isCanvasLike = true;
-  } else {
-    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
-  }
-  if (isVideo) {
-    const HAVE_CURRENT_DATA_READY_STATE = 2;
-    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
-      throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-    }
-  }
-  const kernel = getKernel(FromPixels, ENGINE.backendName);
-  if (kernel != null) {
-    const inputs = {pixels};
-    const attrs = {numChannels};
-    return ENGINE.runKernel(FromPixels, inputs, attrs);
-  }
-  const [width, height] = isVideo ? [
-    pixels.videoWidth,
-    pixels.videoHeight
-  ] : [pixels.width, pixels.height];
-  let vals;
-  if (isCanvasLike) {
-    vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
-  } else if (isImageData || isPixelData) {
-    vals = pixels.data;
-  } else if (isImage || isVideo) {
-    if (fromPixels2DContext == null) {
-      fromPixels2DContext = document.createElement("canvas").getContext("2d");
-    }
-    fromPixels2DContext.canvas.width = width;
-    fromPixels2DContext.canvas.height = height;
-    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
-    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
-  }
-  let values;
-  if (numChannels === 4) {
-    values = new Int32Array(vals);
-  } else {
-    const numPixels = width * height;
-    values = new Int32Array(numPixels * numChannels);
-    for (let i = 0; i < numPixels; i++) {
-      for (let channel = 0; channel < numChannels; ++channel) {
-        values[i * numChannels + channel] = vals[i * 4 + channel];
-      }
-    }
-  }
-  const outShape = [height, width, numChannels];
-  return tensor3d(values, outShape, "int32");
-}
-async function toPixels(img, canvas) {
-  let $img = convertToTensor(img, "img", "toPixels");
-  if (!(img instanceof Tensor)) {
-    const originalImgTensor = $img;
-    $img = cast(originalImgTensor, "int32");
-    originalImgTensor.dispose();
-  }
-  if ($img.rank !== 2 && $img.rank !== 3) {
-    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
-  }
-  const [height, width] = $img.shape.slice(0, 2);
-  const depth = $img.rank === 2 ? 1 : $img.shape[2];
-  if (depth > 4 || depth === 2) {
-    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
-  }
-  if ($img.dtype !== "float32" && $img.dtype !== "int32") {
-    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
-  }
-  const data = await $img.data();
-  const multiplier = $img.dtype === "float32" ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255];
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-      if ($img.dtype === "float32") {
-        if (value < 0 || value > 1) {
-          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
-        }
-      } else if ($img.dtype === "int32") {
-        if (value < 0 || value > 255) {
-          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
-        }
-      }
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-  if (canvas != null) {
-    canvas.width = width;
-    canvas.height = height;
-    const ctx = canvas.getContext("2d");
-    const imageData = new ImageData(bytes, width, height);
-    ctx.putImageData(imageData, 0, 0);
-  }
-  if ($img !== img) {
-    $img.dispose();
-  }
-  return bytes;
-}
-const fromPixels = op({fromPixels_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
-const gather_nd_util_exports = {};
-__export(gather_nd_util_exports, {
-  prepareAndValidate: () => prepareAndValidate
-});
-function prepareAndValidate(tensor17, indices) {
-  if (tensor17.rank < 1) {
-    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor17.rank}.`);
-  }
-  if (indices.rank < 1) {
-    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
-  }
-  if (indices.shape[indices.rank - 1] > tensor17.rank) {
-    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank - 1]} vs. ${tensor17.rank}`);
-  }
-  if (tensor17.size === 0) {
-    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor17.shape}.`);
-  }
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-  let nResult = 1;
-  for (let i = 0; i < indicesShape.length - 1; ++i) {
-    nResult *= indicesShape[i];
-  }
-  const inputShape = tensor17.shape;
-  const resultShape = indicesShape.slice();
-  resultShape.pop();
-  let sliceSize = 1;
-  for (let i = sliceRank; i < tensor17.rank; ++i) {
-    sliceSize *= inputShape[i];
-    resultShape.push(inputShape[i]);
-  }
-  const strides = [
-    ...computeStrides(tensor17.shape).map((stride) => stride / sliceSize),
-    1
-  ].slice(0, sliceRank);
-  return [resultShape, nResult, sliceSize, strides];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
-const scatter_nd_util_exports = {};
-__export(scatter_nd_util_exports, {
-  calculateShapes: () => calculateShapes,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape
-});
-function validateUpdateShape(shape, indices, updates) {
-  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
-  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
-  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
-  if (updates.rank < batchDim) {
-    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
-  }
-  if (shape.length < sliceDim + (updates.rank - batchDim)) {
-    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
-  }
-  if (updates.rank !== batchDim + shape.length - sliceDim) {
-    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
-  }
-  for (let d = 0; d < batchDim; ++d) {
-    if (updates.shape[d] !== indices.shape[d]) {
-      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
-    }
-  }
-  for (let d = 0; d < updates.rank - batchDim; ++d) {
-    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
-      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
-    }
-  }
-}
-function validateInput(updates, indices, shape) {
-  if (indices.rank < 1) {
-    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (updates.rank < 1) {
-    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
-  }
-  if (shape.length < 1) {
-    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
-  }
-  if (shape.length === 0) {
-    if (indices.size === 0) {
-      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
-    }
-    if (updates.size === 0) {
-      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
-    }
-  }
-  validateUpdateShape(shape, indices, updates);
-}
-function calculateShapes(updates, indices, shape) {
-  const indicesRank = indices.shape.length;
-  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
-  const totalNd = shape.length;
-  let sliceSize = 1;
-  for (let i = sliceRank; i < totalNd; ++i) {
-    sliceSize *= shape[i];
-  }
-  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
-  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
-  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
-  const outputSize = sizeFromShape(shape);
-  return {sliceRank, numUpdates, sliceSize, strides, outputSize};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
-const slice_util_exports = {};
-__export(slice_util_exports, {
-  assertParamsValid: () => assertParamsValid,
-  computeFlatOffset: () => computeFlatOffset,
-  computeOutShape: () => computeOutShape,
-  getNormalizedAxes: () => getNormalizedAxes,
-  isSliceContinous: () => isSliceContinous,
-  maskToAxes: () => maskToAxes,
-  parseSliceParams: () => parseSliceParams,
-  startForAxis: () => startForAxis,
-  startIndicesWithElidedDims: () => startIndicesWithElidedDims,
-  stopForAxis: () => stopForAxis,
-  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
-  stridesForAxis: () => stridesForAxis,
-  stridesWithElidedDims: () => stridesWithElidedDims
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsValid(input, begin, size) {
-  assert(input.rank === begin.length, () => `Error in slice${input.rank}D: Length of begin ${begin} must match the rank of the array (${input.rank}).`);
-  assert(input.rank === size.length, () => `Error in slice${input.rank}D: Length of size ${size} must match the rank of the array (${input.rank}).`);
-  for (let i = 0; i < input.rank; ++i) {
-    assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${input.rank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);
-  }
-}
-function maskToAxes(mask) {
-  const axes = [];
-  let axis = 0;
-  while (mask > 0) {
-    if (mask & 1) {
-      axes.push(axis);
-    }
-    mask /= 2;
-    axis++;
-  }
-  return axes;
-}
-function computeOutShape(begin, end, strides) {
-  const size = [];
-  for (let axis = 0; axis < begin.length; axis++) {
-    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
-  }
-  return size;
-}
-function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
-  const newStrides = [...strides];
-  for (let i = newStrides.length; i < inputShape.length; i++) {
-    newStrides.push(1);
-  }
-  for (let i = 0; i < numElidedAxes; i++) {
-    if (i === 0) {
-      newStrides[ellipsisInsertionIndex] = 1;
-    } else {
-      newStrides.splice(ellipsisInsertionIndex, 0, 1);
-      newStrides.pop();
-    }
-  }
-  return newStrides;
-}
-function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
-  if (normalizedAxis <= ellipsisInsertionIndex) {
-    return normalizedAxis;
-  }
-  return normalizedAxis - (numElidedAxes - 1);
-}
-function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
-  const elidedAxes = [];
-  for (let i = 0; i < numElidedAxes; i++) {
-    elidedAxes.push(ellipsisInsertionIndex + i);
-  }
-  return elidedAxes;
-}
-function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
-  const inputRank = inputShape.length;
-  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
-  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
-    const fullIndex = ellipsisAxes[0];
-    const numElidedAxes = numInterpolatedAxes + 1;
-    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
-    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
-    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
-  } else {
-    for (let axis = 0; axis < inputRank; axis++) {
-      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
-      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
-      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
-    }
-  }
-  return {
-    begin: normalizedBegin,
-    end: normalizedEnd,
-    strides: normalizedStrides
-  };
-}
-function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = 0;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalBegin[originalAxis];
-      if (beginMask & 1 << originalAxis) {
-        originalValue = 0;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  return newIndices;
-}
-function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = Number.MAX_SAFE_INTEGER;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalEnd[originalAxis];
-      if (endMask & 1 << originalAxis) {
-        originalValue = Number.MAX_SAFE_INTEGER;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  for (let i = 0; i < newIndices.length; i++) {
-    const axisSize = inputShape[i];
-    if (newIndices[i] < 0) {
-      newIndices[i] += axisSize;
-    }
-    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
-  }
-  return newIndices;
-}
-function stridesForAxis(strides, axis, ellipsisMask) {
-  let stride = strides[axis];
-  if (ellipsisMask & 1 << axis || stride == null) {
-    stride = 1;
-  }
-  return stride;
-}
-function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
-  let start = startIndices[axis];
-  const stride = strides[axis] || 1;
-  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
-    if (stride > 0) {
-      start = Number.MIN_SAFE_INTEGER;
-    } else {
-      start = Number.MAX_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (start < 0) {
-    start += axisSize;
-  }
-  start = clamp(0, start, axisSize - 1);
-  return start;
-}
-function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
-  let stop = stopIndices[axis];
-  const stride = strides[axis] || 1;
-  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
-    if (stride > 0) {
-      stop = Number.MAX_SAFE_INTEGER;
-    } else {
-      stop = Number.MIN_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (stop < 0) {
-    stop += axisSize;
-  }
-  if (stride > 0) {
-    stop = clamp(0, stop, axisSize);
-  } else {
-    stop = clamp(-1, stop, axisSize - 1);
-  }
-  return stop;
-}
-function isSliceContinous(shape, begin, size) {
-  let firstNonOneAxis = size.length;
-  for (let i = 0; i < size.length; i++) {
-    if (size[i] > 1) {
-      firstNonOneAxis = i;
-      break;
-    }
-  }
-  for (let i = firstNonOneAxis + 1; i < size.length; i++) {
-    if (begin[i] > 0 || size[i] !== shape[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function computeFlatOffset(begin, strides) {
-  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i = 0; i < begin.length - 1; i++) {
-    flatOffset += begin[i] * strides[i];
-  }
-  return flatOffset;
-}
-function parseSliceParams(x, begin, size) {
-  let begin_;
-  if (typeof begin === "number") {
-    begin_ = [begin, ...new Array(x.rank - 1).fill(0)];
-  } else if (begin.length < x.rank) {
-    begin_ = begin.concat(new Array(x.rank - begin.length).fill(0));
-  } else {
-    begin_ = begin.slice();
-  }
-  begin_.forEach((d) => {
-    assert(d !== -1, () => "slice() does not support negative begin indexing.");
-  });
-  let size_;
-  if (size == null) {
-    size_ = new Array(x.rank).fill(-1);
-  } else if (typeof size === "number") {
-    size_ = [size, ...new Array(x.rank - 1).fill(-1)];
-  } else if (size.length < x.rank) {
-    size_ = size.concat(new Array(x.rank - size.length).fill(-1));
-  } else {
-    size_ = size;
-  }
-  size_ = size_.map((d, i) => {
-    if (d >= 0) {
-      return d;
-    } else {
-      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
-      return x.shape[i] - begin_[i];
-    }
-  });
-  return [begin_, size_];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/serialization.js
-const serialization_exports = {};
-__export(serialization_exports, {
-  Serializable: () => Serializable,
-  SerializationMap: () => SerializationMap,
-  registerClass: () => registerClass
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Serializable {
-  getClassName() {
-    return this.constructor.className;
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2);
-  }
-}
-class SerializationMap {
-  constructor() {
-    this.classNameMap = {};
-  }
-  static getMap() {
-    if (SerializationMap.instance == null) {
-      SerializationMap.instance = new SerializationMap();
-    }
-    return SerializationMap.instance;
-  }
-  static register(cls) {
-    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
-  }
-}
-function registerClass(cls) {
-  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
-  assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
-  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
-  SerializationMap.register(cls);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/test_util.js
-const test_util_exports = {};
-__export(test_util_exports, {
-  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
-  expectArrayBuffersEqual: () => expectArrayBuffersEqual,
-  expectArraysClose: () => expectArraysClose,
-  expectArraysEqual: () => expectArraysEqual,
-  expectNumbersClose: () => expectNumbersClose,
-  expectPromiseToFail: () => expectPromiseToFail,
-  expectValuesInRange: () => expectValuesInRange,
-  testEpsilon: () => testEpsilon
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TEST_EPSILON_FLOAT32 = 1e-3;
-const TEST_EPSILON_FLOAT16 = 0.1;
-function expectArraysClose(actual, expected, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon2));
-}
-function testEpsilon() {
-  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
-}
-function expectArraysPredicate(actual, expected, predicate) {
-  let checkClassType = true;
-  if (isTypedArray(actual) || isTypedArray(expected)) {
-    checkClassType = false;
-  }
-  if (isTypedArray(actual) && isTypedArray(expected)) {
-    checkClassType = true;
-  }
-  if (checkClassType) {
-    const aType = actual.constructor.name;
-    const bType = expected.constructor.name;
-    if (aType !== bType) {
-      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
-    }
-  }
-  if (Array.isArray(actual) && Array.isArray(expected)) {
-    const actualShape = inferShape(actual);
-    const expectedShape = inferShape(expected);
-    if (!arraysEqual(actualShape, expectedShape)) {
-      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
-    }
-  }
-  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
-  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
-  if (actualFlat.length !== expectedFlat.length) {
-    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-  }
-  for (let i = 0; i < expectedFlat.length; ++i) {
-    const a = actualFlat[i];
-    const e = expectedFlat[i];
-    if (!predicate(a, e)) {
-      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-    }
-  }
-}
-function expectPromiseToFail(fn, done) {
-  fn().then(() => done.fail(), () => done());
-}
-function expectArraysEqual(actual, expected) {
-  const exp11 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
-  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
-    return expectArraysPredicate(actual, exp11, (a, b) => a == b);
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
-}
-function expectNumbersClose(a, e, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  if (!areClose(a, e, epsilon2)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
-  }
-}
-function areClose(a, e, epsilon2) {
-  if (!isFinite(a) && !isFinite(e)) {
-    return true;
-  }
-  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon2) {
-    return false;
-  }
-  return true;
-}
-function expectValuesInRange(actual, low, high) {
-  for (let i = 0; i < actual.length; i++) {
-    if (actual[i] < low || actual[i] > high) {
-      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
-    }
-  }
-}
-function expectArrayBuffersEqual(actual, expected) {
-  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/version.js
-/** @license See the LICENSE file. */
-const version = "2.4.0";
-
-// node_modules/@tensorflow/tfjs-core/dist/globals.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enableProdMode() {
-  env().set("PROD", true);
-}
-function enableDebugMode() {
-  env().set("DEBUG", true);
-}
-function disableDeprecationWarnings() {
-  env().set("DEPRECATION_WARNINGS_ENABLED", false);
-  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
-}
-function deprecationWarn(msg) {
-  if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
-    console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
-  }
-}
-setDeprecationWarningFn(deprecationWarn);
-function disposeVariables() {
-  ENGINE.disposeVariables();
-}
-function engine15() {
-  return ENGINE;
-}
-function memory() {
-  return ENGINE.memory();
-}
-function profile(f) {
-  return ENGINE.profile(f);
-}
-function tidy(nameOrFn, fn) {
-  return ENGINE.tidy(nameOrFn, fn);
-}
-function dispose(container) {
-  const tensors = getTensorsInContainer(container);
-  tensors.forEach((tensor17) => tensor17.dispose());
-}
-function keep(result) {
-  return ENGINE.keep(result);
-}
-function time(f) {
-  return ENGINE.time(f);
-}
-function setBackend(backendName) {
-  return ENGINE.setBackend(backendName);
-}
-function ready() {
-  return ENGINE.ready();
-}
-function getBackend() {
-  return ENGINE.backendName;
-}
-function removeBackend(name) {
-  ENGINE.removeBackend(name);
-}
-function findBackend(name) {
-  return ENGINE.findBackend(name);
-}
-function findBackendFactory(name) {
-  return ENGINE.findBackendFactory(name);
-}
-function registerBackend(name, factory, priority = 1) {
-  return ENGINE.registerBackend(name, factory, priority);
-}
-function backend() {
-  return ENGINE.backend;
-}
-function setPlatform(platformName, platform) {
-  env().setPlatform(platformName, platform);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function add_(a, b) {
-  let $a = convertToTensor(a, "a", "add");
-  let $b = convertToTensor(b, "b", "add");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.add($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Add);
-}
-const add2 = op({add_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floorDiv_(a, b) {
-  let $a = convertToTensor(a, "a", "floorDiv");
-  let $b = convertToTensor(b, "b", "floorDiv");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.floorDiv($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, FloorDiv);
-}
-const floorDiv = op({floorDiv_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function div_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "int32" && $b.dtype === "int32") {
-    return floorDiv($a, $b);
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.realDivide($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, Div, attrs);
-}
-const div = op({div_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mul_(a, b) {
-  let $a = convertToTensor(a, "a", "mul");
-  let $b = convertToTensor(b, "b", "mul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.multiply($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Multiply);
-}
-const mul = op({mul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function abs_(x) {
-  const $x = convertToTensor(x, "x", "abs");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "complex64") {
-      return backend2.complexAbs($x);
-    }
-    return backend2.abs($x);
-  }, inputs, null, Abs);
-}
-const abs = op({abs_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acos_(x) {
-  const $x = convertToTensor(x, "x", "acos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acos);
-}
-const acos = op({acos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acosh_(x) {
-  const $x = convertToTensor(x, "x", "acosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acosh);
-}
-const acosh = op({acosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addN_(tensors) {
-  assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
-  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
-  const firstTensor = $tensors[0];
-  $tensors.forEach((t) => {
-    if (t.dtype !== firstTensor.dtype) {
-      throw new Error("All tensors passed to tf.addN() must have the same dtype");
-    }
-  });
-  $tensors.forEach((t) => {
-    if (!arraysEqual(t.shape, firstTensor.shape)) {
-      throw new Error("All tensors passed to tf.addN() must have the same shape");
-    }
-  });
-  const forward = (backend2, save) => {
-    const res = backend2.addN($tensors);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  return ENGINE.runKernelFunc(forward, inputs, null, AddN);
-}
-const addN = op({addN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function axesAreInnerMostDims(axes, rank) {
-  for (let i = 0; i < axes.length; ++i) {
-    if (axes[axes.length - i - 1] !== rank - 1 - i) {
-      return false;
-    }
-  }
-  return true;
-}
-function combineLocations(outputLoc, reduceLoc, axes) {
-  const rank = outputLoc.length + reduceLoc.length;
-  const loc = [];
-  let outIdx = 0;
-  let reduceIdx = 0;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      loc.push(outputLoc[outIdx++]);
-    } else {
-      loc.push(reduceLoc[reduceIdx++]);
-    }
-  }
-  return loc;
-}
-function computeOutAndReduceShapes(aShape, axes) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      outShape.push(aShape[dim]);
-    }
-  }
-  const reduceShape = axes.map((dim) => aShape[dim]);
-  return [outShape, reduceShape];
-}
-function expandShapeToKeepDim(shape, axes) {
-  const reduceSubShape = axes.map((x) => 1);
-  return combineLocations(shape, reduceSubShape, axes);
-}
-function assertAxesAreInnerMostDims(msg, axes, rank) {
-  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
-}
-function getAxesPermutation(axes, rank) {
-  if (axesAreInnerMostDims(axes, rank)) {
-    return null;
-  }
-  const result = [];
-  for (let i = 0; i < rank; ++i) {
-    if (axes.indexOf(i) === -1) {
-      result.push(i);
-    }
-  }
-  axes.forEach((axis) => result.push(axis));
-  return result;
-}
-function getUndoAxesPermutation(axes) {
-  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
-}
-function getInnerMostAxes(numAxes, rank) {
-  const res = [];
-  for (let i = rank - numAxes; i < rank; ++i) {
-    res.push(i);
-  }
-  return res;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/all.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function all_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "all", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.all($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, All, attrs);
-}
-const all = op({all_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/any.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function any_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "any", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.any($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Any, attrs);
-}
-const any = op({any_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMax_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMax");
-  const forward = (backend2, save) => {
-    save([$x]);
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMax($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMax, attrs);
-}
-const argMax = op({argMax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMin_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMin");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if (axis == null) {
-      axis = 0;
-    }
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMin($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMin, attrs);
-}
-const argMin = op({argMin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asin_(x) {
-  const $x = convertToTensor(x, "x", "asin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asin);
-}
-const asin = op({asin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asinh_(x) {
-  const $x = convertToTensor(x, "x", "asinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asinh);
-}
-const asinh = op({asinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan_(x) {
-  const $x = convertToTensor(x, "x", "atan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atan);
-}
-const atan = op({atan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan2_(a, b) {
-  let $a = convertToTensor(a, "a", "atan2");
-  let $b = convertToTensor(b, "b", "atan2");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.atan2($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Atan2);
-}
-const atan2 = op({atan2_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atanh_(x) {
-  const $x = convertToTensor(x, "x", "atanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atanh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atanh);
-}
-const atanh = op({atanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function computeDilation2DInfo(inputShape, filterShape, strides, pad8, dataFormat = "NHWC", dilations) {
-  const inputChannels = inputShape[3];
-  const $filterShape = [...filterShape, inputChannels];
-  const $dataFormat = convertConv2DDataFormat(dataFormat);
-  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad8, null, null, $dataFormat);
-}
-function computePool2DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "channelsLast") {
-  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
-  let filterShape;
-  if (dataFormat === "channelsLast") {
-    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
-  } else if (dataFormat === "channelsFirst") {
-    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, false, dataFormat);
-}
-function computePool3DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "NDHWC") {
-  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
-  let filterShape;
-  let $dataFormat;
-  if (dataFormat === "NDHWC") {
-    $dataFormat = "channelsLast";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
-  } else if (dataFormat === "NCDHW") {
-    $dataFormat = "channelsFirst";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, false, $dataFormat, roundingMode);
-}
-function computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, depthwise = false, dataFormat = "channelsLast") {
-  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideHeight, strideWidth] = parseTupleParam(strides);
-  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inHeight,
-    inWidth,
-    inChannels,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideHeight,
-    strideWidth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, depthwise = false, dataFormat = "channelsLast", roundingMode) {
-  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
-  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
-  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inDepth,
-    inHeight,
-    inWidth,
-    inChannels,
-    outDepth,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideDepth,
-    strideHeight,
-    strideWidth,
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterDepth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationDepth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputRows = inShape[0];
-  const inputCols = inShape[1];
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputRows, outputCols];
-}
-function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputDepth = inShape[0];
-  const inputRows = inShape[1];
-  const inputCols = inShape[2];
-  const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputDepths, outputRows, outputCols, outChannels];
-}
-function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
-  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
-  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
-}
-function parseTupleParam(param) {
-  if (typeof param === "number") {
-    return [param, param, param];
-  }
-  if (param.length === 2) {
-    return [param[0], param[1], 1];
-  }
-  return param;
-}
-function parse3TupleParam(param) {
-  return typeof param === "number" ? [param, param, param] : param;
-}
-function getEffectiveFilterSize(filterSize, dilation) {
-  if (dilation <= 1) {
-    return filterSize;
-  }
-  return filterSize + (filterSize - 1) * (dilation - 1);
-}
-function getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
-  let padInfo;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {top: pad8, bottom: pad8, left: pad8, right: pad8, type: padType};
-    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad8, roundingMode);
-    outHeight = outShape[0];
-    outWidth = outShape[1];
-  } else if (pad8 === "same") {
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
-    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else if (typeof pad8 === "object") {
-    const top = dataFormat === "channelsLast" ? pad8[1][0] : pad8[2][0];
-    const bottom = dataFormat === "channelsLast" ? pad8[1][1] : pad8[2][1];
-    const left = dataFormat === "channelsLast" ? pad8[2][0] : pad8[3][0];
-    const right = dataFormat === "channelsLast" ? pad8[2][1] : pad8[3][1];
-    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
-    padInfo = {top, bottom, left, right, type: padType};
-    outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
-    outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outHeight, outWidth};
-}
-function get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
-  let padInfo;
-  let outDepth;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {
-      top: pad8,
-      bottom: pad8,
-      left: pad8,
-      right: pad8,
-      front: pad8,
-      back: pad8,
-      type: padType
-    };
-    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad8, roundingMode);
-    outDepth = outShape[0];
-    outHeight = outShape[1];
-    outWidth = outShape[2];
-  } else if (pad8 === "same") {
-    outDepth = Math.ceil(inDepth / strideDepth);
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
-    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
-    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
-    const front = Math.floor(padAlongDepth / 2);
-    const back = padAlongDepth - front;
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, front, back, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {
-      top: 0,
-      bottom: 0,
-      left: 0,
-      right: 0,
-      front: 0,
-      back: 0,
-      type: "VALID"
-    };
-    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outDepth, outHeight, outWidth};
-}
-function conditionalRound(value, roundingMode) {
-  if (!roundingMode) {
-    return value;
-  }
-  switch (roundingMode) {
-    case "round":
-      return Math.round(value);
-    case "ceil":
-      return Math.ceil(value);
-    case "floor":
-      return Math.floor(value);
-    default:
-      throw new Error(`Unknown roundingMode ${roundingMode}`);
-  }
-}
-function tupleValuesAreOne(param) {
-  const [dimA, dimB, dimC] = parseTupleParam(param);
-  return dimA === 1 && dimB === 1 && dimC === 1;
-}
-function eitherStridesOrDilationsAreOne(strides, dilations) {
-  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
-}
-function convertConv2DDataFormat(dataFormat) {
-  if (dataFormat === "NHWC") {
-    return "channelsLast";
-  } else if (dataFormat === "NCHW") {
-    return "channelsFirst";
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "avgPool", "float32");
-  const dilations = 1;
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    save([x4D]);
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      return x4D.clone();
-    }
-    return backend2.avgPool(x4D, convInfo);
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool, attrs);
-  res = cast(res, $x.dtype);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPool = op({avgPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3d_(x, filterSize, strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "avgPool3d", "float32");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    save([x5D]);
-    return backend2.avgPool3d(x5D, convInfo);
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3D, attrs);
-  res = cast(res, x5D.dtype);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3d = op({avgPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsConsistent(shapes, axis) {
-  const rank = shapes[0].length;
-  shapes.forEach((shape, i) => {
-    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
-  });
-  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
-  const firstShape = shapes[0];
-  shapes.forEach((shape, i) => {
-    for (let r = 0; r < rank; r++) {
-      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
-    }
-  });
-}
-function computeOutShape2(shapes, axis) {
-  const outputShape = shapes[0].slice();
-  for (let i = 1; i < shapes.length; i++) {
-    outputShape[axis] += shapes[i][axis];
-  }
-  return outputShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function concat_(tensors, axis = 0) {
-  assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
-  let $tensors = convertToTensorArray(tensors, "tensors", "concat");
-  if ($tensors[0].dtype === "complex64") {
-    $tensors.forEach((tensor17) => {
-      if (tensor17.dtype !== "complex64") {
-        throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor17.dtype}. `);
-      }
-    });
-  }
-  const $axis = parseAxisParam(axis, $tensors[0].shape)[0];
-  const outShape = computeOutShape2($tensors.map((t) => t.shape), $axis);
-  if (sizeFromShape(outShape) === 0) {
-    return tensor4([], outShape);
-  }
-  $tensors = $tensors.filter((t) => t.size > 0);
-  if ($tensors.length === 1) {
-    return $tensors[0];
-  }
-  const shapes = $tensors.map((t) => t.shape);
-  assertParamsConsistent(shapes, $axis);
-  const forward = (backend2, save) => {
-    const res = backend2.concat($tensors, $axis);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  const attr = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Concat, attr);
-}
-const concat = op({concat_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoid_(x) {
-  const $x = convertToTensor(x, "x", "sigmoid");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sigmoid($x);
-    save([res]);
-    return res;
-  }, inputs, null, Sigmoid);
-}
-const sigmoid = op({sigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice");
-  if ($x.rank === 0) {
-    throw new Error("Slicing scalar is not possible");
-  }
-  const [begin_, size_] = parseSliceParams($x, begin, size);
-  assertParamsValid($x, begin_, size_);
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.slice($x, begin_, size_);
-  };
-  const inputs = {x: $x};
-  const attrs = {begin, size};
-  return ENGINE.runKernelFunc(forward, inputs, null, Slice, attrs);
-}
-const slice = op({slice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tanh_(x) {
-  const $x = convertToTensor(x, "x", "tanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.tanh($x);
-    save([y]);
-    return y;
-  }, inputs, null, Tanh);
-}
-const tanh2 = op({tanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
-  const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
-  const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
-  const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
-  const $data = convertToTensor(data, "data", "basicLSTMCell");
-  const $c = convertToTensor(c, "c", "basicLSTMCell");
-  const $h = convertToTensor(h, "h", "basicLSTMCell");
-  const combined = concat([$data, $h], 1);
-  const weighted = matMul(combined, $lstmKernel);
-  const res = add2(weighted, $lstmBias);
-  const batchSize = res.shape[0];
-  const sliceCols = res.shape[1] / 4;
-  const sliceSize = [batchSize, sliceCols];
-  const i = slice(res, [0, 0], sliceSize);
-  const j = slice(res, [0, sliceCols], sliceSize);
-  const f = slice(res, [0, sliceCols * 2], sliceSize);
-  const o = slice(res, [0, sliceCols * 3], sliceSize);
-  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
-  const newH = mul(tanh2(newC), sigmoid(o));
-  return [newC, newH];
-}
-const basicLSTMCell = op({basicLSTMCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchToSpaceND_(x, blockShape, crops) {
-  const $x = convertToTensor(x, "x", "batchToSpaceND");
-  const prod3 = blockShape.reduce((a, b) => a * b);
-  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
-  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
-  assert($x.shape[0] % prod3 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod3}`);
-  const forward = (backend2) => {
-    return backend2.batchToSpaceND($x, blockShape, crops);
-  };
-  const inputs = {x: $x};
-  const attrs = {blockShape, crops};
-  return ENGINE.runKernelFunc(forward, inputs, null, BatchToSpaceND, attrs);
-}
-const batchToSpaceND = op({batchToSpaceND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
-function xAs4D(x) {
-  let x4D;
-  if (x.rank === 0 || x.rank === 1) {
-    x4D = reshape(x, [1, 1, 1, x.size]);
-  } else if (x.rank === 2) {
-    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  } else {
-    x4D = x;
-  }
-  return x4D;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 1e-3;
-  }
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
-  assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
-  assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-  const x4D = xAs4D($x);
-  const forward = (backend2, save) => {
-    save([x4D, $mean, $variance, $scale]);
-    return backend2.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);
-  };
-  const inputs = {
-    x: x4D,
-    scale: $scale,
-    offset: $offset,
-    mean: $mean,
-    variance: $variance
-  };
-  const attrs = {varianceEpsilon};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, FusedBatchNorm, attrs);
-  return reshape(res, $x.shape);
-}
-function as1DOr4D(x) {
-  if (x == null) {
-    return null;
-  }
-  if (x.rank === 0) {
-    return reshape(x, [x.size]);
-  } else if (x.rank === 1) {
-    return x;
-  } else if (x.rank === 2) {
-    return reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  return x;
-}
-const batchNorm = op({batchNorm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
-function batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
-  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm2d = op({batchNorm2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
-function batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
-  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm3d = op({batchNorm3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
-function batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
-  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm4d = op({batchNorm4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function broadcastTo_(x, shape) {
-  let input = convertToTensor(x, "broadcastTo", "x");
-  const xShape = input.shape;
-  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
-    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
-  }
-  if (shape.length < input.rank) {
-    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);
-  }
-  if (shape.length > input.rank) {
-    const newShape = input.shape.slice();
-    while (newShape.length < shape.length) {
-      newShape.unshift(1);
-    }
-    input = reshape(input, newShape);
-  }
-  const inputShape = input.shape;
-  const reps = Array.from(shape);
-  for (let i = shape.length - 1; i >= 0; i--) {
-    if (inputShape[i] === shape[i]) {
-      reps[i] = 1;
-    } else if (input.shape[i] !== 1) {
-      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
-    }
-  }
-  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
-  if (axes.length === 0) {
-    return clone(input);
-  }
-  const forward = (backend2) => backend2.tile(input, reps);
-  const inputs = {x: input};
-  const attrs = {shape, inputShape};
-  return ENGINE.runKernelFunc(forward, inputs, null, BroadcastTo, attrs);
-}
-const broadcastTo = op({broadcastTo_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ceil_(x) {
-  const $x = convertToTensor(x, "x", "ceil");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.ceil($x), inputs, null, Ceil);
-}
-const ceil = op({ceil_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clipByValue_(x, clipValueMin, clipValueMax) {
-  const $x = convertToTensor(x, "x", "clipByValue");
-  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
-  const inputs = {x: $x};
-  const attrs = {clipValueMin, clipValueMax};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.clip($x, clipValueMin, clipValueMax);
-    save([$x]);
-    return res;
-  }, inputs, null, ClipByValue, attrs);
-}
-const clipByValue = op({clipByValue_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
-function concat1d_(tensors) {
-  return concat(tensors, 0);
-}
-const concat1d = op({concat1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
-function concat2d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat2d = op({concat2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
-function concat3d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat3d = op({concat3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
-function concat4d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat4d = op({concat4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const forward = (backend2, save) => {
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2d(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2D, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2d = op({conv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
-function conv1d_(x, filter, stride, pad8, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv1d");
-  const $filter = convertToTensor(filter, "filter", "conv1d");
-  let x3D = $x;
-  let reshapedTo3D = false;
-  if ($x.rank === 2) {
-    reshapedTo3D = true;
-    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
-  }
-  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
-  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
-  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
-  assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
-  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
-  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
-  const strides = [1, stride];
-  const dilations = [1, dilation];
-  const conv2dDataFormat = "NHWC";
-  const res = conv2d(input4D, filter4D, strides, pad8, conv2dDataFormat, dilations, dimRoundingMode);
-  if (reshapedTo3D) {
-    return reshape(res, [res.shape[2], res.shape[3]]);
-  }
-  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
-}
-const conv1d = op({conv1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropInput_(xShape, dy, filter, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape4D = xShape;
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
-  }
-  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
-  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
-  const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
-  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2dDerInput(dy4D, filter, convInfo);
-    save([dy4D, filter]);
-    return res2;
-  };
-  const inputs = {dy: dy4D, filter};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape: xShape4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropInput, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2DBackpropInput = op({conv2DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
-function conv2dTranspose_(x, filter, outputShape, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
-  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad8, "NHWC", dimRoundingMode);
-}
-const conv2dTranspose = op({conv2dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3d_(x, filter, strides, pad8, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
-  const $x = convertToTensor(x, "x", "conv3d");
-  const $filter = convertToTensor(filter, "filter", "conv3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
-  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
-  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
-  const forward = (backend2, save) => {
-    const convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad8);
-    const res2 = backend2.conv3d(x5D, $filter, convInfo);
-    save([x5D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x5D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3d = op({conv3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropInput_(xShape, dy, filter, strides, pad8) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape5D = xShape;
-  let dy5D = dy;
-  let reshapedTo5D = false;
-  if (dy.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
-  }
-  const inDepth = xShape5D[4];
-  const outDepth = dy5D.shape[4];
-  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
-  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
-  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
-  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad8);
-    return backend2.conv3dDerInput(dy5D, filter, convInfo);
-  };
-  const inputs = {dy: dy5D};
-  const attrs = {pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3DBackpropInput = op({conv3DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
-function conv3dTranspose_(x, filter, outputShape, strides, pad8) {
-  const $x = convertToTensor(x, "x", "conv3dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
-  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad8);
-}
-const conv3dTranspose = op({conv3dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cos_(x) {
-  const $x = convertToTensor(x, "x", "cos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cos);
-}
-const cos = op({cos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cosh_(x) {
-  const $x = convertToTensor(x, "x", "cosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cosh);
-}
-const cosh = op({cosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cumsum_(x, axis = 0, exclusive = false, reverse9 = false) {
-  const $x = convertToTensor(x, "x", "cumsum");
-  const forward = (backend2, save) => {
-    const permutation = getAxesPermutation([axis], $x.rank);
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-    }
-    const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
-    let value = backend2.cumsum(permutedX, permutedAxis, exclusive, reverse9);
-    save([$x]);
-    if (permutation != null) {
-      const reversePermutation = getUndoAxesPermutation(permutation);
-      value = transpose(value, reversePermutation);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, exclusive, reverse: reverse9};
-  return ENGINE.runKernelFunc(forward, inputs, null, Cumsum, attrs);
-}
-const cumsum = op({cumsum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "depthToSpace");
-  const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
-  const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
-  const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
-  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
-    ${$x.shape}`);
-  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`);
-  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
-  const forward = (backend2) => backend2.depthToSpace($x, blockSize, dataFormat);
-  const inputs = {x: $x};
-  const attrs = {blockSize, dataFormat};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthToSpace, attrs);
-}
-const depthToSpace = op({depthToSpace_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1];
-    }
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-    const res2 = backend2.depthwiseConv2D(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNative, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2d = op({depthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function diag_(x) {
-  const $x = convertToTensor(x, "x", "diag");
-  const forward = (backend2) => {
-    const flat = reshape($x, [$x.size]);
-    const result = backend2.diag(flat);
-    const outShape = [...x.shape, ...x.shape];
-    return reshape(result, outShape);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Diag);
-}
-const diag = op({diag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dilation2d_(x, filter, strides, pad8, dilations = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "dilation2d");
-  const $filter = convertToTensor(filter, "filter", "dilation2d");
-  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
-  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    reshapedTo4D = true;
-  }
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dilations};
-  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const dilation2d = op({dilation2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getBroadcastDims(inShape, outShape) {
-  const inRank = inShape.length;
-  const dims = [];
-  for (let i = 0; i < inRank; i++) {
-    const dim = inRank - 1 - i;
-    const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i] || 1;
-    if (b > 1 && a === 1) {
-      dims.unshift(dim);
-    }
-  }
-  return dims;
-}
-function getReductionAxes(inShape, outShape) {
-  const result = [];
-  for (let i = 0; i < outShape.length; i++) {
-    const inDim = inShape[inShape.length - i - 1];
-    const outAxis = outShape.length - i - 1;
-    const outDim = outShape[outAxis];
-    if (inDim == null || inDim === 1 && outDim > 1) {
-      result.unshift(outAxis);
-    }
-  }
-  return result;
-}
-function assertAndGetBroadcastShape(shapeA, shapeB) {
-  const result = [];
-  const l = Math.max(shapeA.length, shapeB.length);
-  for (let i = 0; i < l; i++) {
-    let a = shapeA[shapeA.length - i - 1];
-    if (a == null) {
-      a = 1;
-    }
-    let b = shapeB[shapeB.length - i - 1];
-    if (b == null) {
-      b = 1;
-    }
-    if (a === 1) {
-      result.unshift(b);
-    } else if (b === 1) {
-      result.unshift(a);
-    } else if (a !== b) {
-      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
-      throw Error(errMsg);
-    } else {
-      result.unshift(a);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function equal_(a, b) {
-  let $a = convertToTensor(a, "a", "equal");
-  let $b = convertToTensor(b, "b", "equal");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.equal($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Equal);
-}
-const equal = op({equal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function where_(condition, a, b) {
-  const $a = convertToTensor(a, "a", "where");
-  const $b = convertToTensor(b, "b", "where");
-  const $condition = convertToTensor(condition, "condition", "where", "bool");
-  const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
-  const $broadcastedA = broadcastTo($a, broadcastShape);
-  const $broadcastedB = broadcastTo($b, broadcastShape);
-  if ($condition.rank === 1) {
-    assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
-  }
-  if ($condition.rank !== 1) {
-    assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.select($condition, $broadcastedA, $broadcastedB);
-    save([$condition]);
-    return res;
-  };
-  const inputs = {
-    condition: $condition,
-    t: $broadcastedA,
-    e: $broadcastedB
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, SelectV2);
-}
-const where = op({where_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zerosLike_(x) {
-  const $x = convertToTensor(x, "x", "zerosLike");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.zerosLike($x), inputs, null, ZerosLike);
-}
-const zerosLike = op({zerosLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function divNoNan_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const divResult = div($a, $b);
-  const zeros9 = zerosLike(divResult);
-  const bEqualsZero = equal($b, zeros9);
-  return where(bEqualsZero, zeros9, divResult);
-}
-const divNoNan = op({divNoNan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dot_(t1, t2) {
-  const $t1 = convertToTensor(t1, "t1", "dot");
-  const $t2 = convertToTensor(t2, "t2", "dot");
-  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
-  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
-  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
-  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
-  if ($t1.rank === 1 && $t2.rank === 1) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, []);
-  } else if ($t1.rank === 1 && $t2.rank === 2) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else if ($t1.rank === 2 && $t2.rank === 1) {
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul($t1, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else {
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul($t1, t22D);
-    return t1t2;
-  }
-}
-const dot = op({dot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function elu_(x) {
-  const $x = convertToTensor(x, "x", "elu");
-  const forward = (backend2, save) => {
-    const y = backend2.elu($x);
-    save([y]);
-    return y;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Elu);
-}
-const elu = op({elu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function erf_(x) {
-  let $x = convertToTensor(x, "x", "erf");
-  assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
-  if ($x.dtype === "int32") {
-    $x = cast($x, "float32");
-  }
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.erf($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Erf);
-}
-const erf = op({erf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function exp_(x) {
-  const $x = convertToTensor(x, "x", "exp");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.exp($x);
-    save([res]);
-    return res;
-  }, inputs, null, Exp);
-}
-const exp = op({exp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expandDims_(x, axis = 0) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "expandDims", parseAs);
-  assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
-  const newShape = $x.shape.slice();
-  if (axis < 0) {
-    assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);
-    axis = $x.rank + axis + 1;
-  }
-  newShape.splice(axis, 0, 1);
-  return reshape($x, newShape);
-}
-const expandDims = op({expandDims_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expm1_(x) {
-  const $x = convertToTensor(x, "x", "expm1");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.expm1($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Expm1);
-}
-const expm1 = op({expm1_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile_(x, reps) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "tile", parseAs);
-  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
-  const forward = (backend2, save) => {
-    const res = backend2.tile($x, reps);
-    save([$x]);
-    return res;
-  };
-  const inputsToSave = [$x];
-  const inputs = {x: $x};
-  const attrs = {reps};
-  return ENGINE.runKernelFunc(forward, inputs, null, Tile, attrs, inputsToSave);
-}
-const tile = op({tile_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function eye_(numRows, numColumns, batchShape, dtype = "float32") {
-  if (numColumns == null) {
-    numColumns = numRows;
-  }
-  const buff = buffer([numRows, numColumns], dtype);
-  const n = numRows <= numColumns ? numRows : numColumns;
-  for (let i = 0; i < n; ++i) {
-    buff.set(1, i, i);
-  }
-  const out = reshape(buff.toTensor(), [numRows, numColumns]);
-  if (batchShape == null) {
-    return out;
-  } else {
-    if (batchShape.length === 1) {
-      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
-    } else if (batchShape.length === 2) {
-      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
-    } else if (batchShape.length === 3) {
-      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
-        batchShape[0],
-        batchShape[1],
-        batchShape[2],
-        1,
-        1
-      ]);
-    } else {
-      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
-    }
-  }
-}
-const eye = op({eye_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fill(shape, value, dtype) {
-  const attrs = {shape, value, dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.fill(shape, value, dtype), {}, null, Fill, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floor_(x) {
-  const $x = convertToTensor(x, "x", "floor");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.floor($x), inputs, null, Floor);
-}
-const floor = op({floor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const PARALLELIZE_THRESHOLD = 30;
-function computeOptimalWindowSize(inSize) {
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    return inSize;
-  }
-  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
-const segment_util_exports = {};
-__export(segment_util_exports, {
-  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
-  computeOutShape: () => computeOutShape3,
-  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function segOpComputeOptimalWindowSize(inSize, numSegments) {
-  let done = false;
-  let res;
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    res = inSize;
-    done = true;
-  } else {
-    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-  }
-  while (!done) {
-    if (res > numSegments || res === inSize) {
-      done = true;
-    } else {
-      res = nearestDivisor(inSize, res + 1);
-    }
-  }
-  return res;
-}
-function computeOutShape3(aShape, axis, numSegments) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (dim !== axis) {
-      outShape.push(aShape[dim]);
-    } else {
-      outShape.push(numSegments);
-    }
-  }
-  return outShape;
-}
-function collectGatherOpShapeInfo(x, indices, axis) {
-  const dimSize = x.shape[axis];
-  const outputShape = [];
-  let batchSize = 1;
-  let sliceSize = 1;
-  for (let i = 0; i < axis; i++) {
-    outputShape.push(x.shape[i]);
-    batchSize *= x.shape[i];
-  }
-  for (let i = 0; i < indices.rank; i++) {
-    outputShape.push(indices.shape[i]);
-  }
-  for (let i = axis + 1; i < x.rank; i++) {
-    outputShape.push(x.shape[i]);
-    sliceSize *= x.shape[i];
-  }
-  return {batchSize, sliceSize, dimSize, outputShape};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gather_(x, indices, axis = 0) {
-  const $x = convertToTensor(x, "x", "gather");
-  const $indices = convertToTensor(indices, "indices", "gather", "int32");
-  const inputs = {x: $x, indices: $indices};
-  const attrs = {axis};
-  const forward = (backend2, save) => {
-    const parsedAxis = parseAxisParam(axis, $x.shape)[0];
-    const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);
-    const res = backend2.gather($x, reshape($indices, [$indices.size]), parsedAxis);
-    save([$x, $indices]);
-    return reshape(res, shapeInfo.outputShape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherV2, attrs);
-}
-const gather = op({gather_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greater_(a, b) {
-  let $a = convertToTensor(a, "a", "greater");
-  let $b = convertToTensor(b, "b", "greater");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.greater($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Greater);
-}
-const greater = op({greater_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greaterEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "greaterEqual");
-  let $b = convertToTensor(b, "b", "greaterEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.greaterEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, GreaterEqual);
-}
-const greaterEqual = op({greaterEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function imag_(input) {
-  const $input = convertToTensor(input, "input", "imag");
-  const forward = (backend2) => {
-    return backend2.imag($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Imag);
-}
-const imag = op({imag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isFinite_(x) {
-  const $x = convertToTensor(x, "x", "isFinite");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isFinite($x), inputs, null, IsFinite);
-}
-const isFinite2 = op({isFinite_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isInf_(x) {
-  const $x = convertToTensor(x, "x", "isInf");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isInf($x), inputs, null, IsInf);
-}
-const isInf = op({isInf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isNaN_(x) {
-  const $x = convertToTensor(x, "x", "isNaN");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isNaN($x), inputs, null, IsNan);
-}
-const isNaN2 = op({isNaN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maximum_(a, b) {
-  let $a = convertToTensor(a, "a", "maximum");
-  let $b = convertToTensor(b, "b", "maximum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.maximum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Maximum);
-}
-const maximum = op({maximum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scalar(value, dtype) {
-  if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
-    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
-  }
-  if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
-    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-  }
-  const shape = [];
-  const inferredShape = [];
-  return makeTensor(value, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function leakyRelu_(x, alpha = 0.2) {
-  const $x = convertToTensor(x, "x", "leakyRelu");
-  return maximum(mul(scalar(alpha), $x), $x);
-}
-const leakyRelu = op({leakyRelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function less_(a, b) {
-  let $a = convertToTensor(a, "a", "less");
-  let $b = convertToTensor(b, "b", "less");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.less($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Less);
-}
-const less = op({less_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function lessEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "lessEqual");
-  let $b = convertToTensor(b, "b", "lessEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.lessEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, LessEqual);
-}
-const lessEqual = op({lessEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function linspace(start, stop, num) {
-  if (num <= 0) {
-    throw new Error("The number of values should be positive.");
-  }
-  const attrs = {start, stop, num};
-  return ENGINE.runKernelFunc((backend2) => backend2.linspace(start, stop, num), {}, null, LinSpace, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const $x = convertToTensor(x, "x", "localResponseNormalization");
-  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${$x.rank}.`);
-  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  const forward = (backend2, save) => {
-    const y = backend2.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {depthRadius, bias, alpha, beta};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, LRN, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  } else {
-    return res;
-  }
-}
-const localResponseNormalization = op({localResponseNormalization_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log_(x) {
-  const $x = convertToTensor(x, "x", "log");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log);
-}
-const log = op({log_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log1p_(x) {
-  const $x = convertToTensor(x, "x", "log1p");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log1p($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log1p);
-}
-const log1p = op({log1p_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function grad(f) {
-  assert(isFunction(f), () => "The f passed in grad(f) must be a function");
-  return (x, dy) => {
-    const $x = convertToTensor(x, "x", "tf.grad", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
-      }
-      checkGrads(grads2);
-      return grads2[0];
-    });
-  };
-}
-function grads(f) {
-  assert(isFunction(f), () => "The f passed in grads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    const $args = convertToTensorArray(args, "args", "tf.grads", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-      }
-      checkGrads(grads2);
-      return grads2;
-    });
-  };
-}
-function valueAndGrad(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
-  return (x, dy) => {
-    assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
-    const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
-    checkGrads(grads2);
-    return {grad: grads2[0], value};
-  };
-}
-function valueAndGrads(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
-    const res = ENGINE.gradients(() => f(...args), args, dy);
-    if (dy != null) {
-      assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-    }
-    checkGrads(res.grads);
-    return res;
-  };
-}
-function variableGrads(f, varList) {
-  assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
-  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
-  const specifiedVarList = varList != null;
-  if (!specifiedVarList) {
-    varList = [];
-    for (const varName in ENGINE.registeredVariables) {
-      varList.push(ENGINE.registeredVariables[varName]);
-    }
-  }
-  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable3) => !variable3.trainable) : null;
-  const originalVarCount = varList.length;
-  varList = varList.filter((variable3) => variable3.trainable);
-  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
-  const allowNoGradients = true;
-  const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
-  assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
-  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
-  const namedGrads = {};
-  varList.forEach((v, i) => {
-    if (grads2[i] != null) {
-      namedGrads[v.name] = grads2[i];
-    }
-  });
-  if (specifiedNonTrainable != null) {
-    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
-  }
-  return {value, grads: namedGrads};
-}
-function customGrad(f) {
-  return ENGINE.customGrad(f);
-}
-function checkGrads(grads2) {
-  const numNullGradients = grads2.filter((g) => g == null).length;
-  if (numNullGradients > 0) {
-    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function neg_(x) {
-  const $x = convertToTensor(x, "x", "neg");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.neg($x), inputs, null, Negate);
-}
-const neg = op({neg_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softplus_(x) {
-  const $x = convertToTensor(x, "x", "softplus");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.softplus($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Softplus);
-}
-const softplus = op({softplus_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSigmoid_(x) {
-  const $x = convertToTensor(x, "x", "logSigmoid");
-  const customOp = customGrad((x2) => {
-    const value = neg(softplus(neg(x2)));
-    const gradFunc = (dy) => {
-      const derX = mul(dy, sigmoid(neg(x2)));
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const logSigmoid = op({logSigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function max_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "max");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let maxInput = $x;
-    if (permutedAxes != null) {
-      maxInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, maxInput.rank);
-    }
-    const y = backend2.max(maxInput, axes);
-    if (permutedAxes != null) {
-      maxInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, parseAxisParam(axis, $x.shape));
-      res = reshape(res, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {reductionIndices: axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Max, attrs);
-}
-const max = op({max_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sub_(a, b) {
-  let $a = convertToTensor(a, "a", "sub");
-  let $b = convertToTensor(b, "b", "sub");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.subtract($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sub);
-}
-const sub = op({sub_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sum_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "sum");
-  if ($x.dtype === "bool") {
-    $x = cast($x, "int32");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.sum(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sum, attrs);
-}
-const sum2 = op({sum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSoftmax_(logits, axis = -1) {
-  const $logits = convertToTensor(logits, "logits", "logSoftmax");
-  if (axis === -1) {
-    axis = $logits.rank - 1;
-  }
-  if (axis !== $logits.rank - 1) {
-    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
-  }
-  const forward = (backend2, save) => {
-    const keepDims = true;
-    const xMax = max(logits, axis, true);
-    const shifted = sub(logits, xMax);
-    const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
-    save([value]);
-    return value;
-  };
-  const inputs = {logits: $logits};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, LogSoftmax, attrs);
-}
-const logSoftmax = op({logSoftmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSumExp_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "logSumExp");
-  const axes = parseAxisParam(axis, $x.shape);
-  const xMax = max($x, axes, true);
-  const a = sub($x, xMax);
-  const b = exp(a);
-  const c = sum2(b, axes);
-  const d = log(c);
-  const res = add2(reshape(xMax, d.shape), d);
-  if (keepDims) {
-    const newShape = expandShapeToKeepDim(res.shape, axes);
-    return reshape(res, newShape);
-  }
-  return res;
-}
-const logSumExp = op({logSumExp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalAnd_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalAnd", "bool");
-  const $b = convertToTensor(b, "b", "logicalAnd", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalAnd($a, $b), inputs, null, LogicalAnd);
-}
-const logicalAnd = op({logicalAnd_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalNot_(x) {
-  const $x = convertToTensor(x, "x", "logicalNot", "bool");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalNot($x), inputs, null, LogicalNot);
-}
-const logicalNot = op({logicalNot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalOr_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalOr", "bool");
-  const $b = convertToTensor(b, "b", "logicalOr", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalOr($a, $b), inputs, null, LogicalOr);
-}
-const logicalOr = op({logicalOr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalXor_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalXor", "bool");
-  const $b = convertToTensor(b, "b", "logicalXor", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
-}
-const logicalXor = op({logicalXor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "maxPool");
-  const dilations = 1;
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    let y;
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      y = x4D.clone();
-    } else {
-      y = backend2.maxPool(x4D, convInfo);
-    }
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const maxPool = op({maxPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "maxPool3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    const y = backend2.maxPool3d(x5D, convInfo);
-    save([x5D, y]);
-    return y;
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3d = op({maxPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolWithArgmax_(x, filterSize, strides, pad8, includeBatchInIndex = false) {
-  const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
-  const inputs = {x: $x};
-  const attrs = {filterSize, strides, pad: pad8, includeBatchInIndex};
-  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
-  return {result: result[0], indexes: result[1]};
-}
-const maxPoolWithArgmax = op({maxPoolWithArgmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zeros(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = zeros(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ones2(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = ones2(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mean_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "mean");
-  const axes = parseAxisParam(axis, $x.shape);
-  const shapes = computeOutAndReduceShapes($x.shape, axes);
-  const reduceShape = shapes[1];
-  const reduceSize = sizeFromShape(reduceShape);
-  const customOp = customGrad((x2) => {
-    const reduceSizeScalar = scalar(reduceSize);
-    const xReduce = reduceSizeScalar.dtype === x2.dtype ? x2 : cast(x2, reduceSizeScalar.dtype);
-    const res = div(xReduce, reduceSizeScalar);
-    const value = sum2(res, axis, keepDims);
-    const gradFunc = (dy) => {
-      const expandedDyShape = x2.shape.slice();
-      axes.forEach((axis2) => {
-        expandedDyShape[axis2] = 1;
-      });
-      const expandedDy = reshape(dy, expandedDyShape);
-      const derX = div(mul(expandedDy, ones2(x2.shape, "float32")), reduceSize);
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const mean = op({mean_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/min.js
-function min_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "min");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let minInput = $x;
-    if (permutedAxes != null) {
-      minInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const y = backend2.min(minInput, axes);
-    if (permutedAxes != null) {
-      minInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, origAxes);
-      res = reshape(y, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Min, attrs);
-}
-const min = op({min_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function minimum_(a, b) {
-  let $a = convertToTensor(a, "a", "minimum");
-  let $b = convertToTensor(b, "b", "minimum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.minimum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Minimum);
-}
-const minimum = op({minimum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mod_(a, b) {
-  let $a = convertToTensor(a, "a", "mod");
-  let $b = convertToTensor(b, "b", "mod");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.mod($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Mod);
-}
-const mod = op({mod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/square.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function square_(x) {
-  const $x = convertToTensor(x, "x", "square");
-  const attrs = {};
-  const inputsToSave = [$x];
-  const outputsToSave = [];
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    return backend2.square($x);
-  }, {x: $x}, null, "Square", attrs, inputsToSave, outputsToSave);
-}
-const square = op({square_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function moments_(x, axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "moments");
-  const axes = parseAxisParam(axis, x.shape);
-  const xMean = mean(x, axes, keepDims);
-  let keepDimsShape = xMean.shape;
-  if (!keepDims) {
-    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
-  }
-  const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
-  const variance = mean(devSquared, axes, keepDims);
-  return {mean: xMean, variance};
-}
-const moments = op({moments_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
-function multiRNNCell_(lstmCells, data, c, h) {
-  const $data = convertToTensor(data, "data", "multiRNNCell");
-  const $c = convertToTensorArray(c, "c", "multiRNNCell");
-  const $h = convertToTensorArray(h, "h", "multiRNNCell");
-  let input = $data;
-  const newStates = [];
-  for (let i = 0; i < lstmCells.length; i++) {
-    const output = lstmCells[i](input, $c[i], $h[i]);
-    newStates.push(output[0]);
-    newStates.push(output[1]);
-    input = output[1];
-  }
-  const newC = [];
-  const newH = [];
-  for (let i = 0; i < newStates.length; i += 2) {
-    newC.push(newStates[i]);
-    newH.push(newStates[i + 1]);
-  }
-  return [newC, newH];
-}
-const multiRNNCell = op({multiRNNCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function multinomial_(logits, numSamples, seed, normalized = false) {
-  const $logits = convertToTensor(logits, "logits", "multinomial");
-  const numOutcomes = $logits.size;
-  const origRank = $logits.rank;
-  if (numOutcomes < 2) {
-    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
-  }
-  if (origRank > 2) {
-    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
-  }
-  seed = seed || Math.random();
-  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
-  const res = ENGINE.runKernelFunc((backend2) => backend2.multinomial(logits2D, normalized, numSamples, seed), {logits2D});
-  return origRank === 1 ? reshape(res, [res.size]) : res;
-}
-const multinomial = op({multinomial_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "notEqual");
-  let $b = convertToTensor(b, "b", "notEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.notEqual($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, NotEqual);
-}
-const notEqual = op({notEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/real.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function real_(input) {
-  const $input = convertToTensor(input, "input", "real");
-  const forward = (backend2) => {
-    return backend2.real($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Real);
-}
-const real = op({real_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function onesLike_(x) {
-  const $x = convertToTensor(x, "x", "onesLike");
-  const forward = (backend2, save) => {
-    if ($x.dtype === "complex64") {
-      const r = onesLike(real($x));
-      const i = zerosLike(imag($x));
-      return complex(r, i);
-    }
-    return backend2.onesLike($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, OnesLike);
-}
-const onesLike = op({onesLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
-function outerProduct_(v1, v2) {
-  const $v1 = convertToTensor(v1, "v1", "outerProduct");
-  const $v2 = convertToTensor(v2, "v2", "outerProduct");
-  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
-  const v12D = reshape($v1, [-1, 1]);
-  const v22D = reshape($v2, [1, -1]);
-  return matMul(v12D, v22D);
-}
-const outerProduct = op({outerProduct_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pad_(x, paddings, constantValue = 0) {
-  const $x = convertToTensor(x, "x", "pad");
-  if ($x.rank === 0) {
-    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.pad($x, paddings, constantValue);
-  };
-  const attrs = {paddings, constantValue};
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, PadV2, attrs);
-}
-const pad = op({pad_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
-function pad1d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
-  return pad(x, [paddings], constantValue);
-}
-const pad1d = op({pad1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
-function pad2d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad2d = op({pad2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
-function pad3d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad3d = op({pad3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
-function pad4d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad4d = op({pad4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function spaceToBatchND_(x, blockShape, paddings) {
-  const $x = convertToTensor(x, "x", "spaceToBatchND");
-  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
-  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-  assert($x.shape.reduce((a, b, i) => {
-    if (i > 0 && i <= blockShape.length) {
-      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
-    }
-    return a;
-  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
-  const forward = (backend2) => backend2.spaceToBatchND($x, blockShape, paddings);
-  const inputs = {x: $x};
-  const attrs = {blockShape, paddings};
-  return ENGINE.runKernelFunc(forward, inputs, null, SpaceToBatchND, attrs);
-}
-const spaceToBatchND = op({spaceToBatchND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pool_(input, windowShape, poolingType, pad8, dilations, strides) {
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  if (strides == null) {
-    strides = 1;
-  }
-  if (pad8 === 0) {
-    pad8 = "valid";
-  }
-  const $x = convertToTensor(input, "x", "maxPool");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad8);
-  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
-  let basePadding;
-  if (pad8 === "same") {
-    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
-  } else {
-    basePadding = [[0, 0], [0, 0]];
-  }
-  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
-  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
-  const convertedPad = isDilationOne ? pad8 : "valid";
-  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
-  const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
-  const y = forwardOp();
-  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
-  const padStart = basePadding.map((b) => b[0]);
-  const origPadEnd = basePadding.map((b) => b[1]);
-  const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
-  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
-  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
-  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
-  return [paddings, crops];
-}
-function withSpaceToBatchBasePaddings(filterShape, dilation) {
-  const dilatedFilterShape = filterShape.map((s, i) => {
-    return s + (s - 1) * (dilation[i] - 1);
-  });
-  const padExtraShape = dilatedFilterShape.map((s) => s - 1);
-  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
-  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
-  return padExtraShape.map((_, i) => {
-    return [padExtraStart[i], padExtraEnd[i]];
-  });
-}
-const pool = op({pool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pow_(base, exp11) {
-  let $base = convertToTensor(base, "base", "pow");
-  let $exp = convertToTensor(exp11, "exp", "pow");
-  [$base, $exp] = makeTypesMatch($base, $exp);
-  const inputs = {a: $base, b: $exp};
-  const forward = (backend2, save) => {
-    const y = backend2.pow($base, $exp);
-    save([$base, $exp, y]);
-    return y;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Pow);
-}
-const pow = op({pow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prelu_(x, alpha) {
-  const $x = convertToTensor(x, "x", "prelu");
-  const $alpha = convertToTensor(alpha, "alpha", "prelu");
-  const forward = (backend2, save) => {
-    const res = backend2.prelu($x, $alpha);
-    save([$x, $alpha]);
-    return res;
-  };
-  const inputs = {x: $x, alpha: $alpha};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prelu);
-}
-const prelu = op({prelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prod_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "prod");
-  const forward = (backend2) => {
-    if ($x.dtype === "bool") {
-      $x = cast($x, "int32");
-    }
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.prod(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prod, attrs);
-}
-const prod = op({prod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rand_(shape, randFunction, dtype) {
-  const size = sizeFromShape(shape);
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  for (let i = 0; i < size; i++) {
-    values[i] = randFunction();
-  }
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-const rand = op({rand_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
-const seedrandom = __toModule(require_seedrandom2());
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MPRandGauss {
-  constructor(mean5, stdDeviation, dtype, truncated, seed) {
-    this.mean = mean5;
-    this.stdDev = stdDeviation;
-    this.dtype = dtype;
-    this.nextVal = NaN;
-    this.truncated = truncated;
-    if (this.truncated) {
-      this.upper = this.mean + this.stdDev * 2;
-      this.lower = this.mean - this.stdDev * 2;
-    }
-    const seedValue = seed ? seed : Math.random();
-    this.random = seedrandom.alea(seedValue.toString());
-  }
-  nextValue() {
-    if (!isNaN(this.nextVal)) {
-      const value = this.nextVal;
-      this.nextVal = NaN;
-      return value;
-    }
-    let resultX, resultY;
-    let isValid = false;
-    while (!isValid) {
-      let v1, v2, s;
-      do {
-        v1 = 2 * this.random() - 1;
-        v2 = 2 * this.random() - 1;
-        s = v1 * v1 + v2 * v2;
-      } while (s >= 1 || s === 0);
-      const mul62 = Math.sqrt(-2 * Math.log(s) / s);
-      resultX = this.mean + this.stdDev * v1 * mul62;
-      resultY = this.mean + this.stdDev * v2 * mul62;
-      if (!this.truncated || this.isValidTruncated(resultX)) {
-        isValid = true;
-      }
-    }
-    if (!this.truncated || this.isValidTruncated(resultY)) {
-      this.nextVal = this.convertValue(resultY);
-    }
-    return this.convertValue(resultX);
-  }
-  convertValue(value) {
-    if (this.dtype == null || this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-  isValidTruncated(value) {
-    return value <= this.upper && value >= this.lower;
-  }
-}
-class RandGamma {
-  constructor(alpha, beta, dtype, seed) {
-    this.alpha = alpha;
-    this.beta = 1 / beta;
-    this.dtype = dtype;
-    const seedValue = seed ? seed : Math.random();
-    this.randu = seedrandom.alea(seedValue.toString());
-    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
-    if (alpha < 1) {
-      this.d = alpha + 2 / 3;
-    } else {
-      this.d = alpha - 1 / 3;
-    }
-    this.c = 1 / Math.sqrt(9 * this.d);
-  }
-  nextValue() {
-    let x2, v0, v1, x, u, v;
-    while (true) {
-      do {
-        x = this.randn.nextValue();
-        v = 1 + this.c * x;
-      } while (v <= 0);
-      v *= v * v;
-      x2 = x * x;
-      v0 = 1 - 0.331 * x2 * x2;
-      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
-      u = this.randu();
-      if (u < v0 || Math.log(u) < v1) {
-        break;
-      }
-    }
-    v = 1 / this.beta * this.d * v;
-    if (this.alpha < 1) {
-      v *= Math.pow(this.randu(), 1 / this.alpha);
-    }
-    return this.convertValue(v);
-  }
-  convertValue(value) {
-    if (this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-}
-class UniformRandom {
-  constructor(min4 = 0, max6 = 1, dtype, seed) {
-    this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
-    this.min = min4;
-    this.range = max6 - min4;
-    this.dtype = dtype;
-    if (seed == null) {
-      seed = Math.random();
-    }
-    if (typeof seed === "number") {
-      seed = seed.toString();
-    }
-    if (!this.canReturnFloat() && this.range <= 1) {
-      throw new Error(`The difference between ${min4} - ${max6} <= 1 and dtype is not float`);
-    }
-    this.random = seedrandom.alea(seed);
-  }
-  convertValue(value) {
-    if (this.canReturnFloat()) {
-      return value;
-    }
-    return Math.round(value);
-  }
-  nextValue() {
-    return this.convertValue(this.min + this.range * this.random());
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
-  if (beta == null) {
-    beta = 1;
-  }
-  if (dtype == null) {
-    dtype = "float32";
-  }
-  if (dtype !== "float32" && dtype !== "int32") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const rgamma = new RandGamma(alpha, beta, dtype, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = rgamma.nextValue();
-  }
-  return res.toTensor();
-}
-const randomGamma = op({randomGamma_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const randomNormal = op({randomNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
-  const res = buffer(shape, dtype);
-  const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = random.nextValue();
-  }
-  return res.toTensor();
-}
-const randomUniform = op({randomUniform_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor1d(values, dtype) {
-  assertNonNull(values);
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 1) {
-    throw new Error("tensor1d() requires values to be a flat/TypedArray");
-  }
-  const shape = null;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/range.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function range(start, stop, step7 = 1, dtype = "float32") {
-  if (step7 === 0) {
-    throw new Error("Cannot have a step of zero");
-  }
-  const forward = () => {
-    const sameStartStop = start === stop;
-    const increasingRangeNegativeStep = start < stop && step7 < 0;
-    const decreasingRangePositiveStep = stop < start && step7 > 1;
-    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
-      return zeros([0], dtype);
-    }
-    const numElements = Math.abs(Math.ceil((stop - start) / step7));
-    const values = makeZerosTypedArray(numElements, dtype);
-    if (stop < start && step7 === 1) {
-      step7 = -1;
-    }
-    values[0] = start;
-    for (let i = 1; i < values.length; i++) {
-      values[i] = values[i - 1] + step7;
-    }
-    return tensor1d(values, dtype);
-  };
-  const attrs = {start, stop, step: step7, dtype};
-  return ENGINE.runKernelFunc(forward, {}, null, Range, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reciprocal_(x) {
-  const $x = convertToTensor(x, "x", "reciprocal");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.reciprocal($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Reciprocal);
-}
-const reciprocal = op({reciprocal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu_(x) {
-  const $x = convertToTensor(x, "x", "relu");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu);
-}
-const relu = op({relu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu6_(x) {
-  const $x = convertToTensor(x, "x", "relu6");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu6($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu6);
-}
-const relu6 = op({relu6_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  const forward = (backend2) => {
-    const axes = parseAxisParam(axis, $x.shape);
-    if ($x.rank === 0) {
-      return clone($x);
-    }
-    const res = backend2.reverse($x, axes);
-    return reshape(res, $x.shape);
-  };
-  const inputs = {x: $x};
-  const attrs = {dims: axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Reverse, attrs);
-}
-const reverse = op({reverse_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse1d_(x) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
-  return reverse($x, 0);
-}
-const reverse1d = op({reverse1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse2d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse2d = op({reverse2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse3d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse3d = op({reverse3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse4d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse4d = op({reverse4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/round.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function round_(x) {
-  const $x = convertToTensor(x, "x", "round");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.round($x), inputs, null, Round);
-}
-const round = op({round_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rsqrt_(x) {
-  const $x = convertToTensor(x, "x", "rsqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.rsqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Rsqrt);
-}
-const rsqrt = op({rsqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function selu_(x) {
-  const $x = convertToTensor(x, "x", "selu");
-  const forward = (backend2, save) => {
-    const res = backend2.selu($x);
-    save([$x]);
-    return res;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Selu);
-}
-const selu = op({selu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
-function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad8, dilation = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "separableConv2d");
-  const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
-  const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  if (dataFormat === "NCHW") {
-    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
-  }
-  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
-  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
-  const inChannels = $depthwiseFilter.shape[2];
-  const channelMultiplier = $depthwiseFilter.shape[3];
-  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
-  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad8, dataFormat, dilation);
-  const pointwiseStride = 1;
-  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const separableConv2d = op({separableConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function setdiff1dAsync_(x, y) {
-  const $x = convertToTensor(x, "x", "setdiff1d");
-  const $y = convertToTensor(y, "y", "setdiff1d");
-  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
-  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
-  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
-  const xVals = await $x.data();
-  const yVals = await $y.data();
-  const ySet = new Set(yVals);
-  let outputSize = 0;
-  for (let i = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      outputSize++;
-    }
-  }
-  const buffer11 = new TensorBuffer([outputSize], $x.dtype);
-  const indices = new TensorBuffer([outputSize], "int32");
-  for (let i = 0, p = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      buffer11.values[p] = xVals[i];
-      indices.values[p] = i;
-      p++;
-    }
-  }
-  return [buffer11.toTensor(), indices.toTensor()];
-}
-const setdiff1dAsync = setdiff1dAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sign_(x) {
-  const $x = convertToTensor(x, "x", "sign");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.sign($x), inputs, null, Sign);
-}
-const sign = op({sign_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sin_(x) {
-  const $x = convertToTensor(x, "x", "sin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sin);
-}
-const sin = op({sin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sinh_(x) {
-  const $x = convertToTensor(x, "x", "sinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sinh);
-}
-const sinh = op({sinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice1d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice1d");
-  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, [begin], [size]);
-}
-const slice1d = op({slice1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice2d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice2d");
-  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice2d = op({slice2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice3d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice3d");
-  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice3d = op({slice3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice4d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice4d");
-  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice4d = op({slice4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmax_(logits, dim = -1) {
-  const $logits = convertToTensor(logits, "logits", "softmax", "float32");
-  if (dim === -1) {
-    dim = $logits.rank - 1;
-  }
-  if (dim !== $logits.rank - 1) {
-    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
-  }
-  const inputs = {logits: $logits};
-  const attrs = {dim};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.softmax($logits, dim);
-    save([y]);
-    return y;
-  }, inputs, null, Softmax, attrs);
-}
-const softmax = op({softmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = input.as2D(batch, innerDimensionSize);
-    const result = backend2.fft(input2D);
-    return result.reshape(input.shape);
-  }, inputs, null, FFT);
-}
-const fft = op({fft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ifft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = reshape(input, [batch, innerDimensionSize]);
-    const result = backend2.ifft(input2D);
-    return reshape(result, input.shape);
-  }, inputs, null, IFFT);
-}
-const ifft = op({ifft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function irfft_(input) {
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let ret;
-  if (innerDimensionSize <= 2) {
-    const complexInput = reshape(input, [batch, innerDimensionSize]);
-    ret = ifft(complexInput);
-  } else {
-    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
-    const realInput = reshape(real(input), [batch, innerDimensionSize]);
-    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
-    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
-    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-    const r = concat([realInput, realConjugate], 1);
-    const i = concat([imagInput, imagConjugate], 1);
-    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
-    ret = ifft(complexInput);
-  }
-  ret = real(ret);
-  if (input.rank === 3 && input.shape[0] !== 0) {
-    const temp = ret;
-    const batch2 = input.shape[0];
-    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
-    temp.dispose();
-  }
-  return ret;
-}
-const irfft = op({irfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
-function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
-  let splitSizes = [];
-  if (typeof numOrSizeSplits === "number") {
-    assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
-    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
-  } else {
-    const numOfNegs = numOrSizeSplits.reduce((count, value) => {
-      if (value === -1) {
-        count += 1;
-      }
-      return count;
-    }, 0);
-    assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
-    const negIndex = numOrSizeSplits.indexOf(-1);
-    if (negIndex !== -1) {
-      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
-      numOrSizeSplits[negIndex] = x.shape[axis] - total;
-    }
-    assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
-    splitSizes = numOrSizeSplits;
-  }
-  return splitSizes;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split_(x, numOrSizeSplits, axis = 0) {
-  const $x = convertToTensor(x, "x", "split");
-  const forward = (backend2, _) => {
-    const $axis = parseAxisParam(axis, $x.shape)[0];
-    const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);
-    return backend2.split($x, splitSizes, $axis);
-  };
-  const inputs = {x: $x};
-  const attr = {numOrSizeSplits, axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, SplitV, attr);
-}
-const split = op({split_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rfft_(input, fftLength) {
-  assert(input.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input.dtype}`);
-  let innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let adjustedInput;
-  if (fftLength != null && fftLength < innerDimensionSize) {
-    const begin = input.shape.map((v) => 0);
-    const size = input.shape.map((v) => v);
-    size[input.shape.length - 1] = fftLength;
-    adjustedInput = slice(input, begin, size);
-    innerDimensionSize = fftLength;
-  } else if (fftLength != null && fftLength > innerDimensionSize) {
-    const zerosShape = input.shape.map((v) => v);
-    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
-    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
-    innerDimensionSize = fftLength;
-  } else {
-    adjustedInput = input;
-  }
-  const zerosInput = zerosLike(adjustedInput);
-  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
-  const ret = fft(complexInput);
-  const half = Math.floor(innerDimensionSize / 2) + 1;
-  const realValues = real(ret);
-  const imagValues = imag(ret);
-  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
-  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
-  const outputShape = adjustedInput.shape.slice();
-  outputShape[adjustedInput.shape.length - 1] = half;
-  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
-}
-const rfft = op({rfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sqrt_(x) {
-  const $x = convertToTensor(x, "x", "sqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sqrt);
-}
-const sqrt = op({sqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squaredDifference_(a, b) {
-  let $a = convertToTensor(a, "a", "squaredDifference");
-  let $b = convertToTensor(b, "b", "squaredDifference");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.squaredDifference($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, SquaredDifference, attrs);
-}
-const squaredDifference = op({squaredDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squeeze_(x, axis) {
-  const $x = convertToTensor(x, "x", "squeeze");
-  return reshape($x, squeezeShape($x.shape, axis).newShape);
-}
-const squeeze = op({squeeze_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stack_(tensors, axis = 0) {
-  const $tensors = convertToTensorArray(tensors, "tensors", "stack");
-  assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
-  if ($tensors.length === 1) {
-    return expandDims($tensors[0], axis);
-  }
-  const rank = $tensors[0].rank;
-  const shape = $tensors[0].shape;
-  const dtype = $tensors[0].dtype;
-  assert(axis <= rank, () => "Axis must be <= rank of the tensor");
-  $tensors.forEach((t) => {
-    assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
-    assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
-  });
-  const expandedTensors = $tensors.map((t) => expandDims(t, axis));
-  return concat(expandedTensors, axis);
-}
-const stack = op({stack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/step.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function step_(x, alpha = 0) {
-  const $x = convertToTensor(x, "x", "step");
-  const inputs = {x: $x};
-  const attrs = {alpha};
-  return ENGINE.runKernelFunc((backend2) => backend2.step($x, alpha), inputs, null, Step, attrs);
-}
-const step = op({step_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
-  let $x = convertToTensor(x, "x", "stridedSlice");
-  const forward = (backend2) => {
-    if (strides == null) {
-      strides = new Array(begin.length);
-    }
-    const ellipsisAxes = maskToAxes(ellipsisMask);
-    if (ellipsisAxes.length > 1) {
-      throw new Error("Multiple ellipses in slice is not allowed.");
-    }
-    if (ellipsisMask !== 0 && newAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
-    }
-    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
-    }
-    const numInterpolatedAxes = $x.rank - begin.length;
-    const expandAxes = maskToAxes(newAxisMask);
-    const newShape = $x.shape.slice();
-    expandAxes.forEach((axis) => {
-      begin[axis] = 0;
-      end[axis] = 1;
-      newShape.splice(axis, 0, 1);
-    });
-    $x = reshape($x, newShape);
-    const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
-    begin = normalizedBegin;
-    end = normalizedEnd;
-    strides = normalizedStrides;
-    const shrinkAxes = maskToAxes(shrinkAxisMask);
-    shrinkAxes.forEach((axis) => {
-      end[axis] = begin[axis] + 1;
-      strides[axis] = 1;
-    });
-    const size = computeOutShape(begin, end, strides);
-    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
-    const nonStrided = strides.every((v) => v === 1);
-    if (nonStrided) {
-      return reshape(slice($x, begin, size), outShape);
-    }
-    const res = backend2.stridedSlice($x, begin, end, strides);
-    return reshape(res, outShape);
-  };
-  const inputs = {x: $x};
-  const attrs = {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, StridedSlice, attrs);
-}
-const stridedSlice = op({stridedSlice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tan_(x) {
-  const $x = convertToTensor(x, "x", "tan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.tan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Tan);
-}
-const tan = op({tan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor2d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 2) {
-    throw new Error("tensor2d() requires shape to have two numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
-    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 4) {
-    throw new Error("tensor4d() requires shape to have four numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
-    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor5d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 5) {
-    throw new Error("tensor5d() requires shape to have five numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
-    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor6d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 6) {
-    throw new Error("tensor6d() requires shape to have six numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
-    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-  }
-  shape = shape || inferredShape;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topk_(x, k = 1, sorted = true) {
-  const $x = convertToTensor(x, "x", "topk");
-  if ($x.rank === 0) {
-    throw new Error("topk() expects the input to be of rank 1 or higher");
-  }
-  const lastDim = $x.shape[$x.shape.length - 1];
-  if (k > lastDim) {
-    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
-  }
-  const inputs = {x: $x};
-  const attrs = {k, sorted};
-  const [values, indices] = ENGINE.runKernelFunc((b) => b.topk($x, k, sorted), inputs, null, TopK, attrs);
-  return {values, indices};
-}
-const topk = op({topk_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type $ { dtype }`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const truncatedNormal = op({truncatedNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unsortedSegmentSum_(x, segmentIds, numSegments) {
-  const $x = convertToTensor(x, "x", "unsortedSegmentSum");
-  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
-  assert(isInt(numSegments), () => "numSegments must be of dtype int");
-  const inputs = {x: $x, segmentIds: $segmentIds};
-  const attrs = {numSegments};
-  const forward = (backend2, save) => {
-    const res = backend2.unsortedSegmentSum($x, $segmentIds, numSegments);
-    save([$segmentIds]);
-    return res;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, UnsortedSegmentSum, attrs);
-}
-const unsortedSegmentSum = op({unsortedSegmentSum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unstack_(x, axis = 0) {
-  const $x = convertToTensor(x, "x", "unstack");
-  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
-  if (axis < 0) {
-    axis += $x.shape.length;
-  }
-  const inputs = {value: $x};
-  const attrs = {axis};
-  const forward = (backend2) => backend2.unstack($x, axis);
-  return ENGINE.runKernelFunc(forward, inputs, null, Unpack, attrs);
-}
-const unstack = op({unstack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function variable(initialValue, trainable = true, name, dtype) {
-  return ENGINE.makeVariable(initialValue, trainable, name, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function whereImpl(condShape, condVals) {
-  const indices = [];
-  for (let i = 0; i < condVals.length; i++) {
-    if (condVals[i]) {
-      indices.push(i);
-    }
-  }
-  const inBuffer = buffer(condShape, "int32");
-  const out = buffer([indices.length, condShape.length], "int32");
-  for (let i = 0; i < indices.length; i++) {
-    const loc = inBuffer.indexToLoc(indices[i]);
-    const offset = i * condShape.length;
-    out.values.set(loc, offset);
-  }
-  return out.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function whereAsync_(condition) {
-  const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
-  const vals = await $condition.data();
-  const res = whereImpl($condition.shape, vals);
-  if (condition !== $condition) {
-    $condition.dispose();
-  }
-  return res;
-}
-const whereAsync = whereAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function booleanMaskAsync_(tensor17, mask, axis) {
-  const $tensor = convertToTensor(tensor17, "tensor", "boolMask");
-  const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
-  const axisFrom = axis == null ? 0 : axis;
-  const maskDim = $mask.rank;
-  const tensorShape = $tensor.shape;
-  assert(maskDim > 0, () => "mask cannot be scalar");
-  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
-  let leadingSize = 1;
-  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
-    leadingSize *= tensorShape[i];
-  }
-  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
-  const reshapedTensor = reshape($tensor, targetTensorShape);
-  const reshapedMask = reshape($mask, [-1]);
-  const positivePositions = await whereAsync(reshapedMask);
-  const indices = squeeze(positivePositions, [1]);
-  const res = gather(reshapedTensor, indices, axisFrom);
-  if (tensor17 !== $tensor) {
-    $tensor.dispose();
-  }
-  if (mask !== $mask) {
-    $mask.dispose();
-  }
-  indices.dispose();
-  reshapedTensor.dispose();
-  reshapedMask.dispose();
-  positivePositions.dispose();
-  return res;
-}
-const booleanMaskAsync = booleanMaskAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "notEqualStrict");
-  const $b = convertToTensor(b, "b", "notEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in notEqualStrict: ");
-  return notEqual($a, $b);
-}
-function lessStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessStrict");
-  const $b = convertToTensor(b, "b", "lessStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessStrict: ");
-  return less($a, $b);
-}
-function equalStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "equalStrict");
-  const $b = convertToTensor(b, "b", "equalStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in equalStrict: ");
-  return equal($a, $b);
-}
-function lessEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessEqualStrict");
-  const $b = convertToTensor(b, "b", "lessEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessEqualStrict: ");
-  return lessEqual($a, $b);
-}
-function greaterStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterStrict");
-  const $b = convertToTensor(b, "b", "greaterStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterStrict: ");
-  return greater($a, $b);
-}
-function greaterEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterEqualStrict");
-  const $b = convertToTensor(b, "b", "greaterEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterEqualStrict: ");
-  return greaterEqual($a, $b);
-}
-const equalStrict = op({equalStrict_});
-const greaterEqualStrict = op({greaterEqualStrict_});
-const greaterStrict = op({greaterStrict_});
-const lessEqualStrict = op({lessEqualStrict_});
-const lessStrict = op({lessStrict_});
-const notEqualStrict = op({notEqualStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "addStrict");
-  const $b = convertToTensor(b, "b", "addStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in addStrict: ");
-  return add2($a, $b);
-}
-function subStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "subStrict");
-  const $b = convertToTensor(b, "b", "subStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in subStrict: ");
-  return sub($a, $b);
-}
-function powStrict_(base, exp11) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  assertShapesMatch(base.shape, exp11.shape, "Error in powStrict: ");
-  return pow(base, exp11);
-}
-function mulStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "mul");
-  const $b = convertToTensor(b, "b", "mul");
-  assertShapesMatch($a.shape, $b.shape, "Error in multiplyStrict: ");
-  return mul($a, $b);
-}
-function divStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "div");
-  const $b = convertToTensor(b, "b", "div");
-  assertShapesMatch($a.shape, $b.shape, "Error in divideStrict: ");
-  return div($a, $b);
-}
-function modStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "modStrict");
-  const $b = convertToTensor(b, "b", "modStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in modStrict: ");
-  return mod($a, $b);
-}
-function minimumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "minimumStrict");
-  const $b = convertToTensor(b, "b", "minimumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in minimumStrict: ");
-  return minimum($a, $b);
-}
-function maximumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "maximumStrict");
-  const $b = convertToTensor(b, "b", "maximumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in maximumStrict: ");
-  return maximum($a, $b);
-}
-function squaredDifferenceStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "squaredDifferenceStrict");
-  const $b = convertToTensor(b, "b", "squaredDifferenceStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in squaredDifferenceStrict: ");
-  return squaredDifference($a, $b);
-}
-const addStrict = op({addStrict_});
-const divStrict = op({divStrict_});
-const maximumStrict = op({maximumStrict_});
-const minimumStrict = op({minimumStrict_});
-const modStrict = op({modStrict_});
-const mulStrict = op({mulStrict_});
-const powStrict = op({powStrict_});
-const squaredDifferenceStrict = op({squaredDifferenceStrict_});
-const subStrict = op({subStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "norm");
-  const norm4 = normImpl(x, ord, axis);
-  let keepDimsShape = norm4.shape;
-  if (keepDims) {
-    const axes = parseAxisParam(axis, x.shape);
-    keepDimsShape = expandShapeToKeepDim(norm4.shape, axes);
-  }
-  return reshape(norm4, keepDimsShape);
-}
-function normImpl(x, p, axis = null) {
-  if (x.rank === 0) {
-    return abs(x);
-  }
-  if (x.rank !== 1 && axis === null) {
-    return normImpl(reshape(x, [-1]), p, axis);
-  }
-  if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
-    if (p === 1) {
-      return sum2(abs(x), axis);
-    }
-    if (p === Infinity) {
-      return max(abs(x), axis);
-    }
-    if (p === -Infinity) {
-      return min(abs(x), axis);
-    }
-    if (p === "euclidean" || p === 2) {
-      return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  if (Array.isArray(axis) && axis.length === 2) {
-    if (p === 1) {
-      return max(sum2(abs(x), axis[0]), axis[1] - 1);
-    }
-    if (p === Infinity) {
-      return max(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === -Infinity) {
-      return min(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === "fro" || p === "euclidean") {
-      return sqrt(sum2(square(x), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  throw new Error(`Error in norm: invalid axis: ${axis}`);
-}
-const norm = op({norm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function movingAverage_(v, x, decay, step7, zeroDebias = true) {
-  const $v = convertToTensor(v, "v", "movingAverage");
-  const $x = convertToTensor(x, "x", "movingAverage");
-  const $decay = convertToTensor(decay, "decay", "movingAverage");
-  assertTypesMatch($v, $x);
-  assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
-  const one = scalar(1);
-  const oneMinusDecay = sub(one, $decay);
-  let update = mul(sub($x, $v), oneMinusDecay);
-  if (zeroDebias) {
-    assert(step7 != null, () => "When using zeroDebias: true, step is required.");
-    const $step = convertToTensor(step7, "step", "movingAverage");
-    update = div(update, sub(one, pow($decay, $step)));
-  }
-  return add2($v, update);
-}
-const movingAverage = op({movingAverage_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scatterND_(indices, updates, shape) {
-  const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
-  const $updates = convertToTensor(updates, "updates", "scatterND");
-  validateInput($updates, $indices, shape);
-  const forward = (backend2) => {
-    return backend2.scatterND($indices, $updates, shape);
-  };
-  const inputs = {indices: $indices, updates: $updates};
-  const attrs = {shape};
-  return ENGINE.runKernelFunc(forward, inputs, null, ScatterNd, attrs);
-}
-const scatterND = op({scatterND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
-function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
-  if (sparseIndices.dtype !== "int32") {
-    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
-  }
-  if (sparseIndices.rank > 2) {
-    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
-  }
-  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
-  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
-  if (outputShape.length !== numDims) {
-    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
-  }
-  const numValues = sparseValues.size;
-  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
-    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
-  }
-  if (sparseValues.dtype !== defaultValues.dtype) {
-    throw new Error("sparseValues.dtype must match defaultValues.dtype");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
-  const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
-  const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
-  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
-  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
-  const inputs = {
-    sparseIndices: $sparseIndices,
-    sparseValues: $sparseValues,
-    defaultValue: $defaultValue
-  };
-  const attrs = {outputShape};
-  return ENGINE.runKernelFunc((backend2) => backend2.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null, SparseToDense, attrs);
-}
-const sparseToDense = op({sparseToDense_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gatherND_(x, indices) {
-  const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
-  const $x = convertToTensor(x, "x", "gatherND");
-  const forward = (backend2) => {
-    return backend2.gatherND($x, $indices);
-  };
-  const inputs = {params: $x, indices: $indices};
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherNd);
-}
-const gatherND = op({gatherND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getNoiseShape(x, noiseShape) {
-  if (noiseShape == null) {
-    return x.shape.slice();
-  }
-  if (arraysEqual(x.shape, noiseShape)) {
-    return noiseShape;
-  }
-  if (x.shape.length === noiseShape.length) {
-    const newDimension = [];
-    for (let i = 0; i < x.shape.length; i++) {
-      if (noiseShape[i] == null && x.shape[i] != null) {
-        newDimension.push(x.shape[i]);
-      } else {
-        newDimension.push(noiseShape[i]);
-      }
-    }
-    return newDimension;
-  }
-  return noiseShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dropout_(x, rate, noiseShape, seed) {
-  const $x = convertToTensor(x, "x", "dropout");
-  assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
-  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
-  if (rate === 0) {
-    return x instanceof Tensor ? $x.clone() : $x;
-  }
-  const $noiseShape = getNoiseShape($x, noiseShape);
-  const keepProb = 1 - rate;
-  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
-  return mul($x, multiplier);
-}
-const dropout = op({dropout_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enclosingPowerOfTwo(value) {
-  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
-}
-function cosineWindow(windowLength, a, b) {
-  const even = 1 - windowLength % 2;
-  const newValues = new Float32Array(windowLength);
-  for (let i = 0; i < windowLength; ++i) {
-    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
-    newValues[i] = a - b * Math.cos(cosArg);
-  }
-  return tensor1d(newValues, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function inTopKAsync_(predictions, targets, k = 1) {
-  const $predictions = convertToTensor(predictions, "predictions", "inTopK");
-  const $targets = convertToTensor(targets, "targets", "inTopK");
-  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
-  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
-  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
-  const lastDim = $predictions.shape[$predictions.shape.length - 1];
-  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
-  const predictionsVals = await $predictions.data();
-  const targetsVals = await $targets.data();
-  const [batch, size] = [predictionsVals.length / lastDim, lastDim];
-  const precision = getTypedArrayFromDType("bool", batch);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = predictionsVals.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    precision[b] = 0;
-    for (let i = 0; i < k; i++) {
-      if (valAndInd[i].index === targetsVals[b]) {
-        precision[b] = 1;
-        break;
-      }
-    }
-  }
-  if (predictions !== $predictions) {
-    $predictions.dispose();
-  }
-  if (targets !== $targets) {
-    $targets.dispose();
-  }
-  return tensor4(precision, $targets.shape, "bool");
-}
-const inTopKAsync = inTopKAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropFilter_(x, dy, filterShape, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
-  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
-  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    return backend2.conv2dDerFilter(x4D, dy4D, convInfo);
-  };
-  const inputs = {x: x4D, dy: dy4D};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);
-}
-const conv2DBackpropFilter = op({conv2DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFusedDyActivation(dy, y, activation) {
-  if (activation == null || activation === "linear") {
-    return dy;
-  }
-  if (activation === "relu") {
-    return mul(dy, step(y));
-  }
-  throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
-}
-function getFusedBiasGradient(bias, dyActivation) {
-  let res = dyActivation;
-  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
-  if (reduceAxes.length > 0) {
-    res = sum2(res, reduceAxes);
-  }
-  return reshape(res, bias.shape);
-}
-function applyActivation(x, activation, preluActivationWeights) {
-  if (activation === "linear") {
-    return x;
-  } else if (activation === "relu") {
-    return relu(x);
-  } else if (activation === "elu") {
-    return elu(x);
-  } else if (activation === "relu6") {
-    return relu6(x);
-  } else if (activation === "prelu") {
-    return prelu(x, preluActivationWeights);
-  }
-  throw new Error(`Unknown fused activation ${activation}.`);
-}
-const shouldFuse = (gradientDepth, activation) => {
-  const gradientMode = gradientDepth > 0;
-  return !gradientMode || activation === "linear";
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  activation = activation || "linear";
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = conv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
-  }
-  const grad2 = (dy, saved) => {
-    const [$filter2, x4D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8);
-    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8);
-    const der = [xDer, filterDer];
-    if ($bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      der.push(biasDer);
-    }
-    return der;
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedConv2d({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const conv2d5 = op({fusedConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);
-  const inputs = {x: x4D, dy: dy4D};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);
-}
-const depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerInput(dy4D, filter, convInfo);
-  const inputs = {dy: dy4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedDepthwiseConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = depthwiseConv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
-  }
-  const grad2 = (dy, saved) => {
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
-    const [$filter2, x4D2, y, bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, convInfo);
-    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, convInfo);
-    if (bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias, dyActivation);
-      return [xDer, filterDer, biasDer];
-    }
-    return [xDer, filterDer];
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedDepthwiseConv2D({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = matMul(a, b, transposeA, transposeB);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  let $a = convertToTensor(a, "a", "fused matMul");
-  let $b = convertToTensor(b, "b", "fused matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused matMul");
-    [$bias] = makeTypesMatch($bias, $a);
-    assertAndGetBroadcastShape(outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
-  }
-  const grad2 = (dy, saved) => {
-    const [a3D2, b3D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
-    let aDer;
-    let bDer;
-    if (!transposeA && !transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, true);
-      bDer = matMul(a3D2, dyActivation, true, false);
-    } else if (!transposeA && transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, false);
-      bDer = matMul(dyActivation, a3D2, true, false);
-    } else if (transposeA && !transposeB) {
-      aDer = matMul(b3D2, dyActivation, false, true);
-      bDer = matMul(a3D2, dyActivation, false, false);
-    } else {
-      aDer = matMul(b3D2, dyActivation, true, true);
-      bDer = matMul(dyActivation, a3D2, true, true);
-    }
-    if (bias != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      return [aDer, bDer, biasDer];
-    } else {
-      return [aDer, bDer];
-    }
-  };
-  const forward = (backend2) => {
-    const y = backend2.fusedBatchMatMul({
-      a: a3D,
-      b: b3D,
-      transposeA,
-      transposeB,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return y;
-  };
-  const inputs = {
-    a: a3D,
-    b: b3D,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {transposeA, transposeB, activation};
-  if (bias == null) {
-    const customOp = customGrad((a3D2, b3D2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOp(a3D, b3D);
-  } else {
-    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res, $bias2]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOpWithBias(a3D, b3D, $bias);
-  }
-}
-const matMul2 = op({fusedMatMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
-const fused_ops_exports = {};
-__export(fused_ops_exports, {
-  conv2d: () => conv2d5,
-  depthwiseConv2d: () => depthwiseConv2d2,
-  matMul: () => matMul2
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hammingWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.54, 0.46);
-}
-const hammingWindow = op({hammingWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hannWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.5, 0.5);
-}
-const hannWindow = op({hannWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
-  let start = 0;
-  const output = [];
-  while (start + frameLength <= signal2.size) {
-    output.push(slice(signal2, start, frameLength));
-    start += frameStep;
-  }
-  if (padEnd) {
-    while (start < signal2.size) {
-      const padLen = start + frameLength - signal2.size;
-      const pad8 = concat([
-        slice(signal2, start, frameLength - padLen),
-        fill([padLen], padValue)
-      ]);
-      output.push(pad8);
-      start += frameStep;
-    }
-  }
-  if (output.length === 0) {
-    return tensor2d([], [0, frameLength]);
-  }
-  return reshape(concat(output), [output.length, frameLength]);
-}
-const frame = op({frame_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
-  if (fftLength == null) {
-    fftLength = enclosingPowerOfTwo(frameLength);
-  }
-  const framedSignal = frame(signal2, frameLength, frameStep);
-  const windowedSignal = mul(framedSignal, windowFn(frameLength));
-  const output = [];
-  for (let i = 0; i < framedSignal.shape[0]; i++) {
-    output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
-  }
-  return concat(output);
-}
-const stft = op({stft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cropAndResize_(image2, boxes, boxInd, cropSize, method, extrapolationValue) {
-  const $image = convertToTensor(image2, "image", "cropAndResize");
-  const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
-  const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
-  method = method || "bilinear";
-  extrapolationValue = extrapolationValue || 0;
-  const numBoxes = $boxes.shape[0];
-  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
-  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
-  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
-  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
-  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-  assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
-  const forward = (backend2) => backend2.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
-  const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
-  const attrs = {method, extrapolationValue, cropSize};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, CropAndResize, attrs);
-  return res;
-}
-const cropAndResize = op({cropAndResize_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function flipLeftRight_(image2) {
-  const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
-  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
-  return res;
-}
-const flipLeftRight = op({flipLeftRight_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
-  const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
-  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const attrs = {radians, fillValue, center};
-  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
-  return res;
-}
-const rotateWithOffset = op({rotateWithOffset_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  if (iouThreshold == null) {
-    iouThreshold = 0.5;
-  }
-  if (scoreThreshold == null) {
-    scoreThreshold = Number.NEGATIVE_INFINITY;
-  }
-  if (softNmsSigma == null) {
-    softNmsSigma = 0;
-  }
-  const numBoxes = boxes.shape[0];
-  maxOutputSize = Math.min(maxOutputSize, numBoxes);
-  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
-  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
-  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
-  assert(scores.rank === 1, () => "scores must be a 1D tensor");
-  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
-  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
-  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
-  return ENGINE.runKernelFunc((b) => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), {boxes: $boxes, scores: $scores}, null, NonMaxSuppressionV3, attrs);
-}
-const nonMaxSuppression = op({nonMaxSuppression_});
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/array_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function binaryInsert(arr, element, comparator) {
-  const index = binarySearch(arr, element, comparator);
-  const insertionPoint = index < 0 ? -(index + 1) : index;
-  arr.splice(insertionPoint, 0, element);
-}
-function binarySearch(arr, target, comparator) {
-  return binarySearch_(arr, target, comparator || defaultComparator);
-}
-function defaultComparator(a, b) {
-  return a > b ? 1 : a < b ? -1 : 0;
-}
-function binarySearch_(arr, target, comparator) {
-  let left = 0;
-  let right = arr.length;
-  let middle = 0;
-  let found = false;
-  while (left < right) {
-    middle = left + (right - left >>> 1);
-    const compareResult = comparator(target, arr[middle]);
-    if (compareResult > 0) {
-      left = middle + 1;
-    } else {
-      right = middle;
-      found = !compareResult;
-    }
-  }
-  return found ? left : -left - 1;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0).selectedIndices;
-}
-function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
-}
-function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
-}
-function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
-  const candidates = [];
-  for (let i = 0; i < scores.length; i++) {
-    if (scores[i] > scoreThreshold) {
-      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
-    }
-  }
-  candidates.sort(ascendingComparator);
-  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
-  const selectedIndices = [];
-  const selectedScores = [];
-  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
-    const candidate = candidates.pop();
-    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
-    if (originalScore < scoreThreshold) {
-      break;
-    }
-    let ignoreCandidate = false;
-    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
-      const iou3 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
-      if (iou3 >= iouThreshold) {
-        ignoreCandidate = true;
-        break;
-      }
-      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou3);
-      if (candidate.score <= scoreThreshold) {
-        break;
-      }
-    }
-    candidate.suppressBeginIndex = selectedIndices.length;
-    if (!ignoreCandidate) {
-      if (candidate.score === originalScore) {
-        selectedIndices.push(boxIndex);
-        selectedScores.push(candidate.score);
-      } else if (candidate.score > scoreThreshold) {
-        binaryInsert(candidates, candidate, ascendingComparator);
-      }
-    }
-  }
-  const validOutputs = selectedIndices.length;
-  const elemsToPad = maxOutputSize - validOutputs;
-  if (padToMaxOutputSize && elemsToPad > 0) {
-    selectedIndices.push(...new Array(elemsToPad).fill(0));
-    selectedScores.push(...new Array(elemsToPad).fill(0));
-  }
-  const result = {selectedIndices: tensor1d(selectedIndices, "int32")};
-  if (returnScoresTensor) {
-    result["selectedScores"] = tensor1d(selectedScores, "float32");
-  }
-  if (returnValidOutputs) {
-    result["validOutputs"] = scalar(validOutputs, "int32");
-  }
-  return result;
-}
-function intersectionOverUnion(boxes, i, j) {
-  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
-  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
-  const yminI = Math.min(iCoord[0], iCoord[2]);
-  const xminI = Math.min(iCoord[1], iCoord[3]);
-  const ymaxI = Math.max(iCoord[0], iCoord[2]);
-  const xmaxI = Math.max(iCoord[1], iCoord[3]);
-  const yminJ = Math.min(jCoord[0], jCoord[2]);
-  const xminJ = Math.min(jCoord[1], jCoord[3]);
-  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
-  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
-  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
-  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-  if (areaI <= 0 || areaJ <= 0) {
-    return 0;
-  }
-  const intersectionYmin = Math.max(yminI, yminJ);
-  const intersectionXmin = Math.max(xminI, xminJ);
-  const intersectionYmax = Math.min(ymaxI, ymaxJ);
-  const intersectionXmax = Math.min(xmaxI, xmaxJ);
-  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
-  return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-function suppressWeight(iouThreshold, scale2, iou3) {
-  const weight = Math.exp(scale2 * iou3 * iou3);
-  return iou3 <= iouThreshold ? weight : 0;
-}
-function ascendingComparator(c1, c2) {
-  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
-  return {selectedIndices: result[0], selectedScores: result[1]};
-}
-const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {
-    maxOutputSize: $maxOutputSize,
-    iouThreshold: $iouThreshold,
-    scoreThreshold: $scoreThreshold,
-    padToMaxOutputSize
-  };
-  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
-  return {selectedIndices: result[0], validOutputs: result[1]};
-}
-const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
-  const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeBilinear_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeBilinear");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeBilinear, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeBilinear = op({resizeBilinear_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeNearestNeighbor_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
-  assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeNearestNeighbor, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeNearestNeighbor = op({resizeNearestNeighbor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function bandPart_(a, numLower, numUpper) {
-  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
-  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
-  const $a = convertToTensor(a, "a", "bandPart");
-  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
-  const shape = $a.shape;
-  const [M, N] = $a.shape.slice(-2);
-  if (!(numLower <= M)) {
-    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
-  }
-  if (!(numUpper <= N)) {
-    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
-  }
-  if (numLower < 0) {
-    numLower = M;
-  }
-  if (numUpper < 0) {
-    numUpper = N;
-  }
-  const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
-  const j = range(0, N, 1, "int32");
-  const ij = sub(i, j);
-  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
-  const zero = zeros([M, N], $a.dtype);
-  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
-}
-const bandPart = op({bandPart_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gramSchmidt_(xs) {
-  let inputIsTensor2D;
-  if (Array.isArray(xs)) {
-    inputIsTensor2D = false;
-    assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
-    const dim = xs[0].shape[0];
-    for (let i = 1; i < xs.length; ++i) {
-      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
-    }
-  } else {
-    inputIsTensor2D = true;
-    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
-  }
-  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
-  const ys = [];
-  const xs1d = xs;
-  for (let i = 0; i < xs.length; ++i) {
-    ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i];
-      if (i > 0) {
-        for (let j = 0; j < i; ++j) {
-          const proj = mul(sum2(mul(ys[j], x)), ys[j]);
-          x = sub(x, proj);
-        }
-      }
-      return div(x, norm(x, "euclidean"));
-    }));
-  }
-  if (inputIsTensor2D) {
-    return stack(ys, 0);
-  } else {
-    return ys;
-  }
-}
-const gramSchmidt = op({gramSchmidt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function qr_(x, fullMatrices = false) {
-  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
-  if (x.rank === 2) {
-    return qr2d(x, fullMatrices);
-  } else {
-    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
-    const x2ds = unstack(reshape(x, [
-      outerDimsProd,
-      x.shape[x.shape.length - 2],
-      x.shape[x.shape.length - 1]
-    ]), 0);
-    const q2ds = [];
-    const r2ds = [];
-    x2ds.forEach((x2d) => {
-      const [q2d, r2d] = qr2d(x2d, fullMatrices);
-      q2ds.push(q2d);
-      r2ds.push(r2d);
-    });
-    const q = reshape(stack(q2ds, 0), x.shape);
-    const r = reshape(stack(r2ds, 0), x.shape);
-    return [q, r];
-  }
-}
-function qr2d(x, fullMatrices = false) {
-  return ENGINE.tidy(() => {
-    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
-    const m = x.shape[0];
-    const n = x.shape[1];
-    let q = eye(m);
-    let r = clone(x);
-    const one2D = tensor2d([[1]], [1, 1]);
-    let w = clone(one2D);
-    const iters = m >= n ? n : m;
-    for (let j = 0; j < iters; ++j) {
-      const rTemp = r;
-      const wTemp = w;
-      const qTemp = q;
-      [w, r, q] = ENGINE.tidy(() => {
-        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
-        const normX = norm(rjEnd1);
-        const rjj = slice(r, [j, j], [1, 1]);
-        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-        const u1 = sub(rjj, mul(s, normX));
-        const wPre = div(rjEnd1, u1);
-        if (wPre.shape[0] === 1) {
-          w = clone(one2D);
-        } else {
-          w = concat([
-            one2D,
-            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
-          ], 0);
-        }
-        const tau = neg(div(matMul(s, u1), normX));
-        const rjEndAll = slice(r, [j, 0], [m - j, n]);
-        const tauTimesW = mul(tau, w);
-        const wT = transpose(w);
-        if (j === 0) {
-          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-        } else {
-          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
-        }
-        const tawTimesWT = transpose(tauTimesW);
-        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
-        if (j === 0) {
-          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-        } else {
-          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
-        }
-        return [w, r, q];
-      });
-      dispose([rTemp, wTemp, qTemp]);
-    }
-    if (!fullMatrices && m > n) {
-      q = slice(q, [0, 0], [m, n]);
-      r = slice(r, [0, 0], [n, n]);
-    }
-    return [q, r];
-  });
-}
-const qr = op({qr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Reduction;
-(function(Reduction2) {
-  Reduction2[Reduction2["NONE"] = 0] = "NONE";
-  Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
-  Reduction2[Reduction2["SUM"] = 2] = "SUM";
-  Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
-})(Reduction || (Reduction = {}));
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
-function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
-  }
-  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
-  if (reduction === Reduction.NONE) {
-    return weightedLoss;
-  }
-  if (reduction === Reduction.SUM) {
-    return sum2(weightedLoss);
-  }
-  if (reduction === Reduction.MEAN) {
-    if ($weights == null) {
-      return mean(weightedLoss);
-    } else {
-      const broadcastFactor = $losses.size / $weights.size;
-      const result = div(sum2(weightedLoss), sum2($weights));
-      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
-    }
-  }
-  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    if ($weights == null) {
-      return div(sum2(weightedLoss), scalar($losses.size));
-    } else {
-      const broadcastedWeights = mul($weights, ones2($losses.shape));
-      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
-      return div(sum2(weightedLoss), numNonZeros);
-    }
-  }
-  throw Error(`Unknown reduction: ${reduction}`);
-}
-const computeWeightedLoss = op({computeWeightedLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "absoluteDifference");
-  const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "absoluteDifference");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
-  const losses2 = abs(sub($labels, $predictions));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const absoluteDifference = op({absoluteDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
-function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "cosineDistance");
-  const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "cosineDistance");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
-  const one = scalar(1);
-  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const cosineDistance = op({cosineDistance_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
-function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $labels = convertToTensor(labels, "labels", "hingeLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "hingeLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
-  const one = scalar(1);
-  $labels = sub(mul(scalar(2), $labels), one);
-  const losses2 = relu(sub(one, mul($labels, $predictions)));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const hingeLoss = op({hingeLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "huberLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "huberLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
-  const deltaScalar = scalar(delta);
-  const error = abs(sub($predictions, $labels));
-  const quadratic = minimum(error, deltaScalar);
-  const linear = sub(error, quadratic);
-  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const huberLoss = op({huberLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logLoss_(labels, predictions, weights, epsilon2 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "logLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "logLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "logLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
-  const one = scalar(1);
-  const epsilonScalar = scalar(epsilon2);
-  const l1 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
-  const l2 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
-  const losses2 = sub(l1, l2);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const logLoss = op({logLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "meanSquaredError");
-  const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "meanSquaredError");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
-  const losses2 = squaredDifference($labels, $predictions);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const meanSquaredError = op({meanSquaredError_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoidCrossEntropyWithLogits_(labels, logits) {
-  const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
-  assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  const maxOutput = relu($logits);
-  const outputXTarget = mul($logits, $labels);
-  const sigmoidOutput = log1p(exp(neg(abs($logits))));
-  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
-}
-function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
-  }
-  assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const half = scalar(0.5);
-    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
-  }
-  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
-  if (dim === -1) {
-    dim = logits.rank - 1;
-  }
-  if (dim !== logits.rank - 1) {
-    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
-  }
-  const customOp = customGrad((labels2, logits2, save) => {
-    const keepDims = true;
-    const lse = logSumExp(logits2, [dim], keepDims);
-    const logResult = sub(cast(logits2, "float32"), lse);
-    save([labels2, logResult]);
-    const costVector = neg(mul(logResult, labels2));
-    const value = sum2(costVector, [dim]);
-    const gradFunc = (dy, saved) => {
-      const [labels3, logResult2] = saved;
-      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
-      return [
-        mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
-        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
-      ];
-    };
-    return {value, gradFunc};
-  });
-  return customOp(labels, logits);
-}
-function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
-  }
-  assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const numClasses = scalar($onehotLabels.shape[1]);
-    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
-  }
-  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const softmaxCrossEntropy = op({softmaxCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spectral = {
-  fft,
-  ifft,
-  rfft,
-  irfft
-};
-const signal = {
-  hammingWindow,
-  hannWindow,
-  frame,
-  stft
-};
-const image = {
-  flipLeftRight,
-  resizeNearestNeighbor,
-  resizeBilinear,
-  rotateWithOffset,
-  cropAndResize,
-  nonMaxSuppression,
-  nonMaxSuppressionAsync,
-  nonMaxSuppressionWithScore,
-  nonMaxSuppressionWithScoreAsync,
-  nonMaxSuppressionPadded,
-  nonMaxSuppressionPaddedAsync
-};
-const linalg = {
-  bandPart,
-  gramSchmidt,
-  qr
-};
-const losses = {
-  absoluteDifference,
-  computeWeightedLoss,
-  cosineDistance,
-  hingeLoss,
-  huberLoss,
-  logLoss,
-  meanSquaredError,
-  sigmoidCrossEntropy,
-  softmaxCrossEntropy
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Optimizer extends Serializable {
-  minimize(f, returnCost = false, varList) {
-    const {value, grads: grads2} = this.computeGradients(f, varList);
-    if (varList != null) {
-      const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
-      this.applyGradients(gradArray);
-    } else {
-      this.applyGradients(grads2);
-    }
-    dispose(grads2);
-    if (returnCost) {
-      return value;
-    } else {
-      value.dispose();
-      return null;
-    }
-  }
-  get iterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return this.iterations_;
-  }
-  incrementIterations() {
-    this.iterations_ = this.iterations + 1;
-  }
-  computeGradients(f, varList) {
-    return variableGrads(f, varList);
-  }
-  dispose() {
-    if (this.iterations_ != null) {
-      dispose(this.iterations_);
-    }
-  }
-  async saveIterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return {
-      name: "iter",
-      tensor: scalar(this.iterations_, "int32")
-    };
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for this optimizer yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
-  }
-  async extractIterations(weightValues) {
-    this.iterations_ = (await weightValues[0].tensor.data())[0];
-    return weightValues.slice(1);
-  }
-}
-Object.defineProperty(Optimizer, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdadeltaOptimizer extends Optimizer {
-  constructor(learningRate, rho, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.rho = rho;
-    this.epsilon = epsilon2;
-    this.accumulatedGrads = [];
-    this.accumulatedUpdates = [];
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedGrads[i] == null) {
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accum_grad`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedUpdates[i] == null) {
-        this.accumulatedUpdates[i] = {
-          originalName: `${name}/accum_var`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
-        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
-        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        accumulatedUpdate.assign(newAccumulatedUpdate);
-        const newValue = add2(mul(updates, -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedUpdates != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-      dispose(this.accumulatedUpdates.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      rho: this.rho,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["rho"], config2["epsilon"]);
-  }
-}
-AdadeltaOptimizer.className = "Adadelta";
-registerClass(AdadeltaOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdagradOptimizer extends Optimizer {
-  constructor(learningRate, initialAccumulatorValue = 0.1) {
-    super();
-    this.learningRate = learningRate;
-    this.initialAccumulatorValue = initialAccumulatorValue;
-    this.accumulatedGrads = [];
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i] == null) {
-        const trainable = false;
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accumulator`,
-          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedGrads != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      initialAccumulatorValue: this.initialAccumulatorValue
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["initialAccumulatorValue"]);
-  }
-}
-AdagradOptimizer.className = "Adagrad";
-registerClass(AdagradOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedSecondMoment = [];
-    tidy(() => {
-      this.accBeta1 = scalar(beta1).variable();
-      this.accBeta2 = scalar(beta2).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const oneMinusAccBeta2 = sub(1, this.accBeta2);
-      varNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedSecondMoment[i] == null) {
-          this.accumulatedSecondMoment[i] = {
-            originalName: `${name}/v`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const secondMoment = this.accumulatedSecondMoment[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
-        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
-        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
-        firstMoment.assign(newFirstMoment);
-        secondMoment.assign(newSecondMoment);
-        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
-        value.assign(newValue);
-      });
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.accBeta2.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedSecondMoment != null) {
-      dispose(this.accumulatedSecondMoment.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    tidy(() => {
-      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
-      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
-    });
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"]);
-  }
-}
-AdamOptimizer.className = "Adam";
-registerClass(AdamOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamaxOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null, decay = 0) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.decay = decay;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedWeightedInfNorm = [];
-    tidy(() => {
-      this.iteration = scalar(0).variable();
-      this.accBeta1 = scalar(beta1).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        if (this.accumulatedWeightedInfNorm[i] == null) {
-          this.accumulatedWeightedInfNorm[i] = {
-            originalName: `${name}/v`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const ut0 = mul(weightedInfNorm, this.beta2);
-        const ut1 = abs(gradient);
-        const newWeightedInfNorm = maximum(ut0, ut1);
-        firstMoment.assign(newFirstMoment);
-        weightedInfNorm.assign(newWeightedInfNorm);
-        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
-        value.assign(newValue);
-      });
-      this.iteration.assign(add2(this.iteration, 1));
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.iteration.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedWeightedInfNorm != null) {
-      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for Adamax yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error("setWeights() is not implemented for Adamax yet.");
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon,
-      decay: this.decay
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"], config2["decay"]);
-  }
-}
-AdamaxOptimizer.className = "Adamax";
-registerClass(AdamaxOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class SGDOptimizer extends Optimizer {
-  constructor(learningRate) {
-    super();
-    this.learningRate = learningRate;
-    this.setLearningRate(learningRate);
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    varNames.forEach((name, i) => {
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const value = ENGINE.registeredVariables[name];
-      tidy(() => {
-        const newValue = add2(mul(this.c, gradient), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  setLearningRate(learningRate) {
-    this.learningRate = learningRate;
-    if (this.c != null) {
-      this.c.dispose();
-    }
-    this.c = keep(scalar(-learningRate));
-  }
-  dispose() {
-    this.c.dispose();
-  }
-  async getWeights() {
-    return [await this.saveIterations()];
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    if (weightValues.length !== 0) {
-      throw new Error("SGD optimizer does not have settable weights.");
-    }
-  }
-  getConfig() {
-    return {learningRate: this.learningRate};
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"]);
-  }
-}
-SGDOptimizer.className = "SGD";
-registerClass(SGDOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MomentumOptimizer extends SGDOptimizer {
-  constructor(learningRate, momentum, useNesterov = false) {
-    super(learningRate);
-    this.learningRate = learningRate;
-    this.momentum = momentum;
-    this.useNesterov = useNesterov;
-    this.accumulations = [];
-    this.m = scalar(this.momentum);
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i] == null) {
-        const trainable = false;
-        this.accumulations[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const accumulation = this.accumulations[i].variable;
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      tidy(() => {
-        let newValue;
-        const newAccumulation = add2(mul(this.m, accumulation), gradient);
-        if (this.useNesterov) {
-          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
-        } else {
-          newValue = add2(mul(this.c, newAccumulation), value);
-        }
-        accumulation.assign(newAccumulation);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.m.dispose();
-    if (this.accumulations != null) {
-      dispose(this.accumulations.map((v) => v.variable));
-    }
-  }
-  setMomentum(momentum) {
-    this.momentum = momentum;
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      momentum: this.momentum,
-      useNesterov: this.useNesterov
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["momentum"], config2["useNesterov"]);
-  }
-}
-MomentumOptimizer.className = "Momentum";
-registerClass(MomentumOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class RMSPropOptimizer extends Optimizer {
-  constructor(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    super();
-    this.learningRate = learningRate;
-    this.decay = decay;
-    this.momentum = momentum;
-    this.epsilon = epsilon2;
-    this.accumulatedMeanSquares = [];
-    this.accumulatedMoments = [];
-    this.accumulatedMeanGrads = [];
-    this.centered = centered;
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-    if (learningRate == null) {
-      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedMeanSquares[i] == null) {
-        this.accumulatedMeanSquares[i] = {
-          originalName: `${name}/rms`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMoments[i] == null) {
-        this.accumulatedMoments[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMeanGrads[i] == null && this.centered) {
-        this.accumulatedMeanGrads[i] = {
-          originalName: `${name}/mg`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
-      const accumulatedMoments = this.accumulatedMoments[i].variable;
-      tidy(() => {
-        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-        if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
-          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
-          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        } else {
-          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        }
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedMeanSquares != null) {
-      dispose(this.accumulatedMeanSquares.map((v) => v.variable));
-    }
-    if (this.accumulatedMeanGrads != null && this.centered) {
-      dispose(this.accumulatedMeanGrads.map((v) => v.variable));
-    }
-    if (this.accumulatedMoments != null) {
-      dispose(this.accumulatedMoments.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
-    if (this.centered) {
-      variables.push(...this.accumulatedMeanGrads);
-    }
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    if (this.centered) {
-      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-    }
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      decay: this.decay,
-      momentum: this.momentum,
-      epsilon: this.epsilon,
-      centered: this.centered
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["decay"], config2["momentum"], config2["epsilon"], config2["centered"]);
-  }
-}
-RMSPropOptimizer.className = "RMSProp";
-registerClass(RMSPropOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class OptimizerConstructors {
-  static sgd(learningRate) {
-    return new SGDOptimizer(learningRate);
-  }
-  static momentum(learningRate, momentum, useNesterov = false) {
-    return new MomentumOptimizer(learningRate, momentum, useNesterov);
-  }
-  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon2, centered);
-  }
-  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null) {
-    return new AdamOptimizer(learningRate, beta1, beta2, epsilon2);
-  }
-  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon2 = null) {
-    return new AdadeltaOptimizer(learningRate, rho, epsilon2);
-  }
-  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null, decay = 0) {
-    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon2, decay);
-  }
-  static adagrad(learningRate, initialAccumulatorValue = 0.1) {
-    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/train.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-[
-  MomentumOptimizer,
-  SGDOptimizer,
-  AdadeltaOptimizer,
-  AdagradOptimizer,
-  RMSPropOptimizer,
-  AdamaxOptimizer,
-  AdamOptimizer
-];
-const train = {
-  sgd: OptimizerConstructors.sgd,
-  momentum: OptimizerConstructors.momentum,
-  adadelta: OptimizerConstructors.adadelta,
-  adagrad: OptimizerConstructors.adagrad,
-  rmsprop: OptimizerConstructors.rmsprop,
-  adamax: OptimizerConstructors.adamax,
-  adam: OptimizerConstructors.adam
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/browser_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const delayCallback = (() => {
-  if (typeof requestAnimationFrame !== "undefined") {
-    return requestAnimationFrame;
-  } else if (typeof setImmediate !== "undefined") {
-    return setImmediate;
-  }
-  return (f) => f();
-})();
-function nextFrame() {
-  return new Promise((resolve) => delayCallback(() => resolve()));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getImageCenter(center, imageHeight, imageWidth) {
-  const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
-  const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
-  return [centerX, centerY];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getReshaped(inputShape, blockShape, prod3, batchToSpace = true) {
-  let reshaped = [];
-  if (batchToSpace) {
-    reshaped = reshaped.concat(blockShape.slice(0));
-    reshaped.push(inputShape[0] / prod3);
-    reshaped = reshaped.concat(inputShape.slice(1));
-  } else {
-    reshaped = reshaped.concat(inputShape[0]);
-    const spatialLength = blockShape.length;
-    for (let i = 0; i < spatialLength; ++i) {
-      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
-    }
-    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
-  }
-  return reshaped;
-}
-function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
-  const permuted = [];
-  if (batchToSpace) {
-    permuted.push(blockShapeRank);
-    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
-      if (i <= 2 * blockShapeRank) {
-        permuted.push(i);
-        permuted.push(i - (blockShapeRank + 1));
-      } else {
-        permuted.push(i);
-      }
-    }
-  } else {
-    const permutedBeforeBatch = [];
-    const permutedAfterBatch = [];
-    for (let i = 1; i < reshapedRank; ++i) {
-      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
-        permutedAfterBatch.push(i);
-      } else {
-        permutedBeforeBatch.push(i);
-      }
-    }
-    permuted.push(...permutedBeforeBatch);
-    permuted.push(0);
-    permuted.push(...permutedAfterBatch);
-  }
-  return permuted;
-}
-function getReshapedPermuted(inputShape, blockShape, prod3, batchToSpace = true) {
-  const reshapedPermuted = [];
-  if (batchToSpace) {
-    reshapedPermuted.push(inputShape[0] / prod3);
-  } else {
-    reshapedPermuted.push(inputShape[0] * prod3);
-  }
-  for (let i = 1; i < inputShape.length; ++i) {
-    if (i <= blockShape.length) {
-      if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
-      } else {
-        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
-      }
-    } else {
-      reshapedPermuted.push(inputShape[i]);
-    }
-  }
-  return reshapedPermuted;
-}
-function getSliceBeginCoords(crops, blockShape) {
-  const sliceBeginCoords = [0];
-  for (let i = 0; i < blockShape; ++i) {
-    sliceBeginCoords.push(crops[i][0]);
-  }
-  return sliceBeginCoords;
-}
-function getSliceSize(uncroppedShape, crops, blockShape) {
-  const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i = 0; i < blockShape; ++i) {
-    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
-  }
-  return sliceSize;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const SELU_SCALEALPHA = 1.7580993408473768;
-const SELU_SCALE = 1.0507009873554805;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ERF_P = 0.3275911;
-const ERF_A1 = 0.254829592;
-const ERF_A2 = -0.284496736;
-const ERF_A3 = 1.421413741;
-const ERF_A4 = -1.453152027;
-const ERF_A5 = 1.061405429;
-
-// node_modules/@tensorflow/tfjs-core/dist/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function warn(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.warn(...msg);
-  }
-}
-function log6(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.log(...msg);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mergeRealAndImagArrays(real6, imag6) {
-  if (real6.length !== imag6.length) {
-    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real6.length}, imag: ${imag6.length}.`);
-  }
-  const result = new Float32Array(real6.length * 2);
-  for (let i = 0; i < result.length; i += 2) {
-    result[i] = real6[i / 2];
-    result[i + 1] = imag6[i / 2];
-  }
-  return result;
-}
-function splitRealAndImagArrays(complex9) {
-  const real6 = new Float32Array(complex9.length / 2);
-  const imag6 = new Float32Array(complex9.length / 2);
-  for (let i = 0; i < complex9.length; i += 2) {
-    real6[i / 2] = complex9[i];
-    imag6[i / 2] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithEvenIndex(complex9) {
-  const len = Math.ceil(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 0; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithOddIndex(complex9) {
-  const len = Math.floor(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 2; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function getComplexWithIndex(complex9, index) {
-  const real6 = complex9[index * 2];
-  const imag6 = complex9[index * 2 + 1];
-  return {real: real6, imag: imag6};
-}
-function assignToTypedArray(data, real6, imag6, index) {
-  data[index * 2] = real6;
-  data[index * 2 + 1] = imag6;
-}
-function exponents(n, inverse) {
-  const real6 = new Float32Array(n / 2);
-  const imag6 = new Float32Array(n / 2);
-  for (let i = 0; i < Math.ceil(n / 2); i++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
-    real6[i] = Math.cos(x);
-    imag6[i] = Math.sin(x);
-  }
-  return {real: real6, imag: imag6};
-}
-function exponent(k, n, inverse) {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
-  const real6 = Math.cos(x);
-  const imag6 = Math.sin(x);
-  return {real: real6, imag: imag6};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
-const backend_util_exports = {};
-__export(backend_util_exports, {
-  ERF_A1: () => ERF_A1,
-  ERF_A2: () => ERF_A2,
-  ERF_A3: () => ERF_A3,
-  ERF_A4: () => ERF_A4,
-  ERF_A5: () => ERF_A5,
-  ERF_P: () => ERF_P,
-  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
-  SELU_SCALE: () => SELU_SCALE,
-  SELU_SCALEALPHA: () => SELU_SCALEALPHA,
-  applyActivation: () => applyActivation,
-  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
-  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
-  assertParamsConsistent: () => assertParamsConsistent,
-  assignToTypedArray: () => assignToTypedArray,
-  axesAreInnerMostDims: () => axesAreInnerMostDims,
-  calculateShapes: () => calculateShapes,
-  castTensor: () => castTensor,
-  combineLocations: () => combineLocations,
-  complexWithEvenIndex: () => complexWithEvenIndex,
-  complexWithOddIndex: () => complexWithOddIndex,
-  computeConv2DInfo: () => computeConv2DInfo,
-  computeConv3DInfo: () => computeConv3DInfo,
-  computeDefaultPad: () => computeDefaultPad,
-  computeDilation2DInfo: () => computeDilation2DInfo,
-  computeOptimalWindowSize: () => computeOptimalWindowSize,
-  computeOutAndReduceShapes: () => computeOutAndReduceShapes,
-  computeOutShape: () => computeOutShape2,
-  computePool2DInfo: () => computePool2DInfo,
-  computePool3DInfo: () => computePool3DInfo,
-  convertConv2DDataFormat: () => convertConv2DDataFormat,
-  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
-  expandShapeToKeepDim: () => expandShapeToKeepDim,
-  exponent: () => exponent,
-  exponents: () => exponents,
-  getAxesPermutation: () => getAxesPermutation,
-  getBroadcastDims: () => getBroadcastDims,
-  getComplexWithIndex: () => getComplexWithIndex,
-  getFusedBiasGradient: () => getFusedBiasGradient,
-  getFusedDyActivation: () => getFusedDyActivation,
-  getImageCenter: () => getImageCenter,
-  getInnerMostAxes: () => getInnerMostAxes,
-  getPermuted: () => getPermuted,
-  getReductionAxes: () => getReductionAxes,
-  getReshaped: () => getReshaped,
-  getReshapedPermuted: () => getReshapedPermuted,
-  getSliceBeginCoords: () => getSliceBeginCoords,
-  getSliceSize: () => getSliceSize,
-  getUndoAxesPermutation: () => getUndoAxesPermutation,
-  linspaceImpl: () => linspaceImpl,
-  log: () => log6,
-  mergeRealAndImagArrays: () => mergeRealAndImagArrays,
-  prepareAndValidate: () => prepareAndValidate,
-  prepareSplitSize: () => prepareSplitSize,
-  reshapeTensor: () => reshapeTensor,
-  segment_util: () => segment_util_exports,
-  shouldFuse: () => shouldFuse,
-  slice_util: () => slice_util_exports,
-  splitRealAndImagArrays: () => splitRealAndImagArrays,
-  tupleValuesAreOne: () => tupleValuesAreOne,
-  upcastType: () => upcastType,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape,
-  warn: () => warn
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function castTensor(x, dtype, backend2) {
-  if (dtype === "complex64") {
-    if (x.dtype === "complex64") {
-      return x.clone();
-    }
-    const zerosTensor = zeros(x.shape);
-    const floatX = cast(x, "float32");
-    const result = backend2.complex(floatX, zerosTensor);
-    zerosTensor.dispose();
-    floatX.dispose();
-    return result;
-  }
-  if (!hasEncodingLoss(x.dtype, dtype)) {
-    return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
-  }
-  if (x.dtype === "complex64") {
-    const real6 = backend2.real(x);
-    const result = cast(real6, dtype);
-    real6.dispose();
-    return result;
-  }
-  if (dtype === "int32") {
-    return backend2.int(x);
-  } else if (dtype === "bool") {
-    const zero = scalar(0, x.dtype);
-    const result = backend2.notEqual(x, zero);
-    zero.dispose();
-    return result;
-  } else {
-    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-  }
-}
-function reshapeTensor(x, shape) {
-  return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
-}
-function linspaceImpl(start, stop, num) {
-  const step7 = (stop - start) / (num - 1);
-  const values = makeZerosTypedArray(num, "float32");
-  values[0] = start;
-  for (let i = 1; i < values.length; i++) {
-    values[i] = values[i - 1] + step7;
-  }
-  return tensor1d(values, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/split_shared.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split5(x, sizeSplits, axis) {
-  const begin = new Array(x.rank).fill(0);
-  const size = x.shape.slice();
-  return sizeSplits.map((s) => {
-    const sliceSize = [...size];
-    sliceSize[axis] = s;
-    const sliceT = slice(x, begin, sliceSize);
-    begin[axis] += s;
-    return sliceT;
-  });
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/tile_impl.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile4(xBuf, reps) {
-  const newShape = new Array(xBuf.rank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = xBuf.shape[i] * reps[i];
-  }
-  const result = buffer(newShape, xBuf.dtype);
-  for (let i = 0; i < result.values.length; ++i) {
-    const newLoc = result.indexToLoc(i);
-    const originalLoc = new Array(xBuf.rank);
-    for (let j = 0; j < originalLoc.length; j++) {
-      originalLoc[j] = newLoc[j] % xBuf.shape[j];
-    }
-    const originalIndex = xBuf.locToIndex(originalLoc);
-    result.values[i] = xBuf.values[originalIndex];
-  }
-  return result.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/topk_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topkImpl(x, xShape, xDtype, k, sorted) {
-  const lastDim = xShape[xShape.length - 1];
-  const [batch, size] = [x.length / lastDim, lastDim];
-  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
-  const allTopKIndices = getTypedArrayFromDType("int32", batch * k);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = x.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    const outOffset = b * k;
-    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
-    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i = 0; i < k; i++) {
-      topKVals[i] = valAndInd[i].value;
-      topKIndices[i] = valAndInd[i].index;
-    }
-  }
-  const outputShape = xShape.slice();
-  outputShape[outputShape.length - 1] = k;
-  return [
-    tensor4(allTopKVals, outputShape, xDtype),
-    tensor4(allTopKIndices, outputShape, "int32")
-  ];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
-const kernel_impls_exports = {};
-__export(kernel_impls_exports, {
-  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
-  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
-  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
-  split: () => split5,
-  tile: () => tile4,
-  topkImpl: () => topkImpl,
-  whereImpl: () => whereImpl
-});
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const EPSILON_FLOAT32 = 1e-7;
-const EPSILON_FLOAT16 = 1e-4;
-class DataStorage {
-  constructor(backend2, dataMover) {
-    this.backend = backend2;
-    this.dataMover = dataMover;
-    this.data = new WeakMap();
-    this.dataIdsCount = 0;
-  }
-  get(dataId) {
-    if (!this.data.has(dataId)) {
-      this.dataMover.moveData(this.backend, dataId);
-    }
-    return this.data.get(dataId);
-  }
-  set(dataId, value) {
-    this.dataIdsCount++;
-    this.data.set(dataId, value);
-  }
-  has(dataId) {
-    return this.data.has(dataId);
-  }
-  delete(dataId) {
-    this.dataIdsCount--;
-    return this.data.delete(dataId);
-  }
-  numDataIds() {
-    return this.dataIdsCount;
-  }
-}
-class KernelBackend {
-  time(f) {
-    return notYetImplemented("time");
-  }
-  read(dataId) {
-    return notYetImplemented("read");
-  }
-  readSync(dataId) {
-    return notYetImplemented("readSync");
-  }
-  numDataIds() {
-    return notYetImplemented("numDataIds");
-  }
-  disposeData(dataId) {
-    return notYetImplemented("disposeData");
-  }
-  write(values, shape, dtype) {
-    return notYetImplemented("write");
-  }
-  move(dataId, values, shape, dtype) {
-    return notYetImplemented("move");
-  }
-  memory() {
-    return notYetImplemented("memory");
-  }
-  floatPrecision() {
-    return notYetImplemented("floatPrecision");
-  }
-  epsilon() {
-    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-  }
-  batchMatMul(a, b, transposeA, transposeB) {
-    return notYetImplemented("batchMatMul");
-  }
-  fusedBatchMatMul({a, b, transposeA, transposeB, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedBatchMatMul");
-  }
-  slice(x, begin, size) {
-    return notYetImplemented("slice");
-  }
-  stridedSlice(x, begin, end, strides) {
-    return notYetImplemented("stridedSlice");
-  }
-  unstack(x, axis) {
-    return notYetImplemented("unstack");
-  }
-  reverse(a, axis) {
-    return notYetImplemented("reverse");
-  }
-  concat(tensors, axis) {
-    return notYetImplemented("concat");
-  }
-  neg(a) {
-    return notYetImplemented("neg");
-  }
-  add(a, b) {
-    return notYetImplemented("add");
-  }
-  addN(tensors) {
-    return notYetImplemented("addN");
-  }
-  subtract(a, b) {
-    return notYetImplemented("subtract");
-  }
-  multiply(a, b) {
-    return notYetImplemented("multiply");
-  }
-  realDivide(a, b) {
-    return notYetImplemented("realDivide");
-  }
-  floorDiv(a, b) {
-    return notYetImplemented("floorDiv");
-  }
-  sum(x, axes) {
-    return notYetImplemented("sum");
-  }
-  prod(x, axes) {
-    return notYetImplemented("prod");
-  }
-  unsortedSegmentSum(x, segmentIds, numSegments) {
-    return notYetImplemented("unsortedSegmentSum");
-  }
-  argMin(x, axis) {
-    return notYetImplemented("argMin");
-  }
-  argMax(x, axis) {
-    return notYetImplemented("argMax");
-  }
-  equal(a, b) {
-    return notYetImplemented("equal");
-  }
-  notEqual(a, b) {
-    return notYetImplemented("notEqual");
-  }
-  less(a, b) {
-    return notYetImplemented("less");
-  }
-  lessEqual(a, b) {
-    return notYetImplemented("lessEqual");
-  }
-  greater(a, b) {
-    return notYetImplemented("greater");
-  }
-  greaterEqual(a, b) {
-    return notYetImplemented("greaterEqual");
-  }
-  logicalNot(a) {
-    return notYetImplemented("logicalNot");
-  }
-  logicalAnd(a, b) {
-    return notYetImplemented("logicalAnd");
-  }
-  logicalOr(a, b) {
-    return notYetImplemented("logicalOr");
-  }
-  where(condition) {
-    return notYetImplemented("where");
-  }
-  select(condition, a, b) {
-    return notYetImplemented("select");
-  }
-  topk(x, k, sorted) {
-    return notYetImplemented("topk");
-  }
-  min(x, axes) {
-    return notYetImplemented("min");
-  }
-  minimum(a, b) {
-    return notYetImplemented("minimum");
-  }
-  mod(a, b) {
-    return notYetImplemented("mod");
-  }
-  max(x, axes) {
-    return notYetImplemented("max");
-  }
-  maximum(a, b) {
-    return notYetImplemented("maximum");
-  }
-  all(x, axes) {
-    return notYetImplemented("all");
-  }
-  any(x, axes) {
-    return notYetImplemented("any");
-  }
-  squaredDifference(a, b) {
-    return notYetImplemented("squaredDifference");
-  }
-  ceil(x) {
-    return notYetImplemented("ceil");
-  }
-  floor(x) {
-    return notYetImplemented("floor");
-  }
-  round(x) {
-    return notYetImplemented("round");
-  }
-  sign(x) {
-    return notYetImplemented("sign");
-  }
-  isNaN(x) {
-    return notYetImplemented("isNaN");
-  }
-  isInf(x) {
-    return notYetImplemented("isInf");
-  }
-  isFinite(x) {
-    return notYetImplemented("isFinite");
-  }
-  pow(a, b) {
-    return notYetImplemented("pow");
-  }
-  exp(x) {
-    return notYetImplemented("exp");
-  }
-  expm1(x) {
-    return notYetImplemented("expm1");
-  }
-  softmax(x, dim) {
-    return notYetImplemented("softmax");
-  }
-  log(x) {
-    return notYetImplemented("log");
-  }
-  log1p(x) {
-    return notYetImplemented("log1p");
-  }
-  sqrt(x) {
-    return notYetImplemented("sqrt");
-  }
-  rsqrt(x) {
-    return notYetImplemented("rsqrt");
-  }
-  square(x) {
-    return notYetImplemented("square");
-  }
-  reciprocal(x) {
-    return notYetImplemented("reciprocal");
-  }
-  relu(x) {
-    return notYetImplemented("relu");
-  }
-  relu6(x) {
-    return notYetImplemented("relu6");
-  }
-  prelu(x, a) {
-    return notYetImplemented("prelu");
-  }
-  elu(x) {
-    return notYetImplemented("elu");
-  }
-  eluDer(dy, y) {
-    return notYetImplemented("eluDer");
-  }
-  selu(x) {
-    return notYetImplemented("selu");
-  }
-  int(x) {
-    return notYetImplemented("int");
-  }
-  clip(x, min4, max6) {
-    return notYetImplemented("clip");
-  }
-  abs(x) {
-    return notYetImplemented("abs");
-  }
-  complexAbs(x) {
-    return notYetImplemented("complexAbs");
-  }
-  sigmoid(x) {
-    return notYetImplemented("sigmoid");
-  }
-  softplus(x) {
-    return notYetImplemented("softplus");
-  }
-  sin(x) {
-    return notYetImplemented("sin");
-  }
-  cos(x) {
-    return notYetImplemented("cos");
-  }
-  tan(x) {
-    return notYetImplemented("tan");
-  }
-  asin(x) {
-    return notYetImplemented("asin");
-  }
-  acos(x) {
-    return notYetImplemented("acos");
-  }
-  atan(x) {
-    return notYetImplemented("atan");
-  }
-  atan2(a, b) {
-    return notYetImplemented("atan2");
-  }
-  sinh(x) {
-    return notYetImplemented("sinh");
-  }
-  cosh(x) {
-    return notYetImplemented("cosh");
-  }
-  tanh(x) {
-    return notYetImplemented("tanh");
-  }
-  asinh(x) {
-    return notYetImplemented("asinh");
-  }
-  acosh(x) {
-    return notYetImplemented("acosh");
-  }
-  atanh(x) {
-    return notYetImplemented("atanh");
-  }
-  erf(x) {
-    return notYetImplemented("erf");
-  }
-  step(x, alpha) {
-    return notYetImplemented("step");
-  }
-  fusedConv2d({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedConv2d");
-  }
-  conv2d(x, filter, convInfo) {
-    return notYetImplemented("conv2d");
-  }
-  conv2dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv2dDerInput");
-  }
-  conv2dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv2dDerFilter");
-  }
-  fusedDepthwiseConv2D({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedDepthwiseConv2D");
-  }
-  depthwiseConv2D(input, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2D");
-  }
-  depthwiseConv2DDerInput(dy, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerInput");
-  }
-  depthwiseConv2DDerFilter(x, dY, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerFilter");
-  }
-  conv3d(x, filter, convInfo) {
-    return notYetImplemented("conv3d");
-  }
-  conv3dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv3dDerInput");
-  }
-  conv3dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv3dDerFilter");
-  }
-  maxPool(x, convInfo) {
-    return notYetImplemented("maxPool");
-  }
-  maxPoolBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPoolBackprop");
-  }
-  avgPool(x, convInfo) {
-    return notYetImplemented("avgPool");
-  }
-  avgPoolBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPoolBackprop");
-  }
-  avgPool3d(x, convInfo) {
-    return notYetImplemented("avgPool3d");
-  }
-  avgPool3dBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPool3dBackprop");
-  }
-  maxPool3d(x, convInfo) {
-    return notYetImplemented("maxPool3d");
-  }
-  maxPool3dBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPool3dBackprop");
-  }
-  reshape(x, shape) {
-    return notYetImplemented("reshape");
-  }
-  cast(x, dtype) {
-    return notYetImplemented("cast");
-  }
-  tile(x, reps) {
-    return notYetImplemented("tile");
-  }
-  pad(x, paddings, constantValue) {
-    return notYetImplemented("pad");
-  }
-  transpose(x, perm) {
-    return notYetImplemented("transpose");
-  }
-  gather(x, indices, axis) {
-    return notYetImplemented("gather");
-  }
-  gatherND(x, indices) {
-    return notYetImplemented("gatherND");
-  }
-  scatterND(indices, updates, shape) {
-    return notYetImplemented("scatterND");
-  }
-  batchToSpaceND(x, blockShape, crops) {
-    return notYetImplemented("batchToSpaceND");
-  }
-  spaceToBatchND(x, blockShape, paddings) {
-    return notYetImplemented("spaceToBatchND");
-  }
-  resizeBilinear(x, newHeight, newWidth, alignCorners) {
-    return notYetImplemented("resizeBilinear");
-  }
-  resizeBilinearBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeBilinearBackprop");
-  }
-  resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {
-    return notYetImplemented("resizeNearestNeighbor");
-  }
-  resizeNearestNeighborBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeNearestNeighborBackprop");
-  }
-  batchNorm(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    return notYetImplemented("batchNorm");
-  }
-  localResponseNormalization4D(x, radius, bias, alpha, beta) {
-    return notYetImplemented("localResponseNormalization4D");
-  }
-  LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {
-    return notYetImplemented("LRNGrad");
-  }
-  multinomial(logits, normalized, numSamples, seed) {
-    return notYetImplemented("multinomial");
-  }
-  oneHot(indices, depth, onValue, offValue) {
-    return notYetImplemented("oneHot");
-  }
-  cumsum(x, axis, exclusive, reverse9) {
-    return notYetImplemented("cumsum");
-  }
-  nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-    return notYetImplemented("nonMaxSuppression");
-  }
-  fft(x) {
-    return notYetImplemented("fft");
-  }
-  ifft(x) {
-    return notYetImplemented("ifft");
-  }
-  complex(real6, imag6) {
-    return notYetImplemented("complex");
-  }
-  real(input) {
-    return notYetImplemented("real");
-  }
-  imag(input) {
-    return notYetImplemented("imag");
-  }
-  cropAndResize(image2, boxes, boxIndex, cropSize, method, extrapolationValue) {
-    return notYetImplemented("cropAndResize");
-  }
-  depthToSpace(x, blockSize, dataFormat) {
-    return notYetImplemented("depthToSpace");
-  }
-  split(value, sizeSplits, axis) {
-    return notYetImplemented("split");
-  }
-  sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {
-    return notYetImplemented("sparseToDense");
-  }
-  diag(x) {
-    return notYetImplemented("diag");
-  }
-  fill(shape, value, dtype) {
-    return notYetImplemented("fill");
-  }
-  onesLike(x) {
-    return notYetImplemented("onesLike");
-  }
-  zerosLike(x) {
-    return notYetImplemented("zerosLike");
-  }
-  linspace(start, stop, num) {
-    return notYetImplemented("linspace");
-  }
-  dispose() {
-    return notYetImplemented("dispose");
-  }
-}
-function notYetImplemented(kernelName) {
-  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. Did you forget to import the kernel?`);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const absGradConfig = {
-  kernelName: Abs,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, step(cast(x, "float32"), -1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acosGradConfig = {
-  kernelName: Acos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = square(cast(x, "float32"));
-        const b = sqrt(sub(scalar(1), a));
-        return neg(div(dy, b));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acoshGradConfig = {
-  kernelName: Acosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(sub(square(cast(x, "float32")), 1));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addGradConfig = {
-  kernelName: Add,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addNGradConfig = {
-  kernelName: AddN,
-  saveAllInputs: true,
-  gradFunc: (dy, saved) => {
-    const ders = {};
-    saved.forEach((_, i) => {
-      ders[i] = () => dy.clone();
-    });
-    return ders;
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMaxGradConfig = {
-  kernelName: ArgMax,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMinGradConfig = {
-  kernelName: ArgMin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinGradConfig = {
-  kernelName: Asin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32")))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinhGradConfig = {
-  kernelName: Asinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atan2GradConfig = {
-  kernelName: Atan2,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const d = add2(square(a), square(b));
-      let res = mul(dy, div(b, d));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      const d = add2(square(a), square(b));
-      let res = neg(mul(dy, div(a, d)));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanGradConfig = {
-  kernelName: Atan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(square(cast(x, "float32")), 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanhGradConfig = {
-  kernelName: Atanh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sub(scalar(1), square(cast(x, "float32"))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "avgPool3dBackprop");
-  const $input = convertToTensor(input, "input", "avgPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.avgPool3dBackprop(dy5D, input5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3dBackprop = op({avgPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPool3DGradConfig = {
-  kernelName: AvgPool3D,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPoolBackprop_(dy, input, filterSize, strides, pad8) {
-  const $dy = convertToTensor(dy, "dy", "avgPoolBackprop");
-  const $input = convertToTensor(input, "input", "avgPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  let input4D = $input;
-  let dy4D = $dy;
-  let reshapedTo4D = false;
-  if ($input.rank === 3) {
-    reshapedTo4D = true;
-    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
-    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
-  }
-  assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`);
-  assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo(input4D.shape, filterSize, strides, 1, pad8);
-    return backend2.avgPoolBackprop(dy4D, input4D, convInfo);
-  };
-  const inputs = {dy: dy4D, input: input4D};
-  const attrs = {filterSize, strides, pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPoolBackprop = op({avgPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPoolGradConfig = {
-  kernelName: AvgPool,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => avgPoolBackprop(dy, x, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchMatMulGradConfig = {
-  kernelName: BatchMatMul,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved, attrs) => {
-    const [a, b] = saved;
-    const {transposeA, transposeB} = attrs;
-    if (!transposeA && !transposeB) {
-      return {
-        a: () => matMul(dy, b, false, true),
-        b: () => matMul(a, dy, true, false)
-      };
-    } else if (!transposeA && transposeB) {
-      return {
-        a: () => matMul(dy, b, false, false),
-        b: () => matMul(dy, a, true, false)
-      };
-    } else if (transposeA && !transposeB) {
-      return {
-        a: () => matMul(b, dy, false, true),
-        b: () => matMul(a, dy, false, false)
-      };
-    } else {
-      return {
-        a: () => matMul(b, dy, true, true),
-        b: () => matMul(dy, a, true, true)
-      };
-    }
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchToSpaceNDGradConfig = {
-  kernelName: BatchToSpaceND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, crops} = attrs;
-    return {x: () => spaceToBatchND(dy, blockShape, crops)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const broadcastToGradConfig = {
-  kernelName: BroadcastTo,
-  gradFunc: (dy, saved, attrs) => {
-    const broadCastToAttrs = attrs;
-    const inputShape = broadCastToAttrs.inputShape;
-    const outputShape = broadCastToAttrs.shape;
-    const reps = Array.from(outputShape);
-    for (let i = inputShape.length - 1; i >= 0; i--) {
-      if (inputShape[i] === outputShape[i]) {
-        reps[i] = 1;
-      } else if (inputShape[i] !== 1) {
-        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
-      }
-    }
-    const axes = [];
-    for (let i = 0; i < reps.length; i++) {
-      if (reps[i] > 1) {
-        axes.push(i);
-      }
-    }
-    return {x: () => sum2(dy, axes, true)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const castGradConfig = {
-  kernelName: Cast,
-  gradFunc: (dy) => {
-    return {x: () => dy.clone()};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ceilGradConfig = {
-  kernelName: Ceil,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const clipByValueGradConfig = {
-  kernelName: ClipByValue,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {clipValueMin, clipValueMax} = attrs;
-    return {
-      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const concatGradConfig = {
-  kernelName: Concat,
-  saveAllInputs: true,
-  gradFunc: (dy, saved, attrs) => {
-    const shapes = saved.map((t) => t.shape);
-    const {axis} = attrs;
-    const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map((s) => s[$axis]);
-    const derTensors = split(dy, sizeSplits, $axis);
-    return derTensors.map((t) => () => t);
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DGradConfig = {
-  kernelName: Conv2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x4D, $filter] = saved;
-    const {dilations, strides, pad: pad8, dataFormat} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    return {
-      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad8, dataFormat),
-      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad8, dataFormat)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DBackpropInputGradConfig = {
-  kernelName: Conv2DBackpropInput,
-  inputsToSave: ["dy", "filter"],
-  gradFunc: (ddx, saved, attrs) => {
-    const [dy, filter] = saved;
-    const {strides, pad: pad8, dataFormat, dimRoundingMode} = attrs;
-    return {
-      dy: () => conv2d(ddx, filter, strides, pad8, dataFormat, 1, dimRoundingMode),
-      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad8, dataFormat, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropFilter_(x, dy, filterShape, strides, pad8) {
-  let x5D = x;
-  if (x.rank === 4) {
-    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
-  }
-  let dy5D = dy;
-  if (dy5D.rank === 4) {
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
-  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
-  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
-  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad8);
-    return backend2.conv3dDerFilter(x5D, dy5D, convInfo);
-  };
-  const inputs = {x: x5D, y: dy5D};
-  const attrs = {strides, pad: pad8};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);
-}
-const conv3DBackpropFilter = op({conv3DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv3DGradConfig = {
-  kernelName: Conv3D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const [x5D, $filter] = saved;
-    return {
-      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad8),
-      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cosGradConfig = {
-  kernelName: Cos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(neg(sin(cast(x, "float32"))), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const coshGradConfig = {
-  kernelName: Cosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(sinh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cumsumGradConfig = {
-  kernelName: Cumsum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {axis, exclusive, reverse: reverse9} = attrs;
-    return {
-      x: () => {
-        const permutation = getAxesPermutation([axis], x.rank);
-        let out = cumsum(dy, axis, exclusive, !reverse9);
-        if (permutation != null) {
-          out = transpose(out, permutation);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const depthwiseConv2dNativeGradConfig = {
-  kernelName: DepthwiseConv2dNative,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1] : dilations;
-    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
-    const [x, filter] = saved;
-    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
-    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
-    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const convInfo = computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad8, dimRoundingMode, true);
-    return {
-      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),
-      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const dilation2dGradConfig = {
-  kernelName: Dilation2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, filter] = saved;
-    const inputInputs = {x, filter, dy};
-    const filterInputs = {x, filter, dy};
-    return {
-      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
-      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Div_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const divGradConfig = {
-  kernelName: Div,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const eluGradConfig = {
-  kernelName: Elu,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    const backPropKernelFunc = (backend2) => {
-      return backend2.eluDer(dy, y);
-    };
-    const inputs = {dy, y};
-    return {
-      x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, EluGrad)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const erfGradConfig = {
-  kernelName: Erf,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
-    return {x: () => mul(dy, a)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expGradConfig = {
-  kernelName: Exp,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, y)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expm1GradConfig = {
-  kernelName: Expm1,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, exp(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorGradConfig = {
-  kernelName: Floor,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorDivGradConfig = {
-  kernelName: FloorDiv,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const fusedBatchNormGradConfig = {
-  kernelName: FusedBatchNorm,
-  inputsToSave: ["x", "mean", "variance", "scale"],
-  gradFunc: (dy, saved, attrs) => {
-    const {varianceEpsilon} = attrs;
-    const [x, mean5, variance, scale2] = saved;
-    const scaleValue = scale2 == null ? scalar(1) : scale2;
-    const reductionAxes = getReductionAxes(mean5.shape, x.shape);
-    const tileShape = [];
-    if (mean5.rank === 1) {
-      for (let i = 0; i < x.shape.length - 1; ++i) {
-        tileShape.push(x.shape[i]);
-      }
-      tileShape.push(1);
-    }
-    const xMinusMean = sub(x, mean5);
-    const dyTimesScaleValue = mul(dy, scaleValue);
-    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
-    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
-    const derX = () => {
-      if (mean5.rank === 1) {
-        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
-      } else {
-        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
-      }
-    };
-    const derMean = () => {
-      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        meanDer = sum2(meanDer, reductionAxes);
-      }
-      return reshape(meanDer, mean5.shape);
-    };
-    const derVariance = () => {
-      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        varianceDer = sum2(varianceDer, reductionAxes);
-      }
-      return reshape(varianceDer, mean5.shape);
-    };
-    const derScale = () => {
-      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
-      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
-      if (mean5.rank === 1) {
-        scaleDer = sum2(scaleDer, reductionAxes);
-      }
-      return reshape(scaleDer, mean5.shape);
-    };
-    const derOffset = () => {
-      let offsetDer = dy;
-      if (mean5.rank === 1) {
-        offsetDer = sum2(offsetDer, reductionAxes);
-      }
-      return reshape(offsetDer, mean5.shape);
-    };
-    return {
-      x: derX,
-      mean: derMean,
-      variance: derVariance,
-      scale: derScale,
-      offset: derOffset
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gatherGradConfig = {
-  kernelName: GatherV2,
-  inputsToSave: ["x", "indices"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, indices] = saved;
-    const {axis} = attrs;
-    const parsedAxis = parseAxisParam(axis, x.shape)[0];
-    const derX = () => {
-      const paramsShape = x.shape;
-      const indicesSize = indices.size;
-      const outerShape = paramsShape.slice(0, parsedAxis);
-      const outerDims = outerShape.length;
-      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
-      const innerDims = innerShape.length;
-      const outerAxesIndices = arrayRange(0, outerDims);
-      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
-      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
-      const values = reshape(dy, valuesShape);
-      const reshapedIndices = reshape(indices, [indicesSize]);
-      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
-      const valuesTranspose = transpose(values, transposeDims);
-      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
-      const invertTransposeDims = getUndoAxesPermutation(transposeDims);
-      paramsGrad = transpose(paramsGrad, invertTransposeDims);
-      return paramsGrad;
-    };
-    return {x: derX, indices: () => indices};
-  }
-};
-function arrayRange(start, stop) {
-  const result = [];
-  for (let i = start; i < stop; ++i) {
-    result.push(i);
-  }
-  return result;
-}
-function arrayConcat(arrays) {
-  const result = [];
-  for (let i = 0; i < arrays.length; ++i) {
-    for (let j = 0; j < arrays[i].length; ++j) {
-      result.push(arrays[i][j]);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const greaterEqualGradConfig = {
-  kernelName: GreaterEqual,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    return {a: () => zerosLike(a), b: () => zerosLike(b)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const identityGradConfig = {
-  kernelName: Identity,
-  gradFunc: (dy) => {
-    return {x: () => cast(dy, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isFiniteGradConfig = {
-  kernelName: IsFinite,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isInfGradConfig = {
-  kernelName: IsInf,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isNanGradConfig = {
-  kernelName: IsNan,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const log1pGradConfig = {
-  kernelName: Log1p,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(x, 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logGradConfig = {
-  kernelName: Log,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, cast(x, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logSoftmaxGradConfig = {
-  kernelName: LogSoftmax,
-  inputsToSave: [],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [value] = saved;
-    const {axis} = attrs;
-    return {
-      logits: () => {
-        const keepDims = true;
-        const softmax3 = exp(value);
-        return sub(dy, mul(sum2(dy, axis, keepDims), softmax3));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const forward = (backend2) => backend2.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);
-  const inputs = {x, y, dy};
-  const attrs = {depthRadius, bias, alpha, beta};
-  return ENGINE.runKernelFunc(forward, inputs, null, LRNBackprop, attrs);
-}
-const localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const lrnGradConfig = {
-  kernelName: LRN,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {depthRadius, bias, alpha, beta} = attrs;
-    return {
-      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
-  if (y.rank < xOrig.rank) {
-    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
-  }
-  if (dy.rank < xOrig.rank) {
-    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
-  }
-  return {
-    x: () => {
-      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
-      return permutedAxes == null ? dx : transpose(dx, permutedAxes);
-    }
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxGradConfig = {
-  kernelName: Max,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const maxAttrs = attrs;
-    const {reductionIndices} = maxAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(reductionIndices, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = maxGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maximumGradConfig = {
-  kernelName: Maximum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(less(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPool3dBackprop");
-  const $input = convertToTensor(input, "input", "maxPool3dBackprop");
-  const $output = convertToTensor(output, "output", "maxPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let output5D = $output;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-    output5D = reshape($output, [
-      1,
-      $output.shape[0],
-      $output.shape[1],
-      $output.shape[2],
-      $output.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D, output: output5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3dBackprop = op({maxPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPool3DGradConfig = {
-  kernelName: MaxPool3D,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolBackprop_(dy, input, output, filterSize, strides, pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPoolBackprop");
-  const $input = convertToTensor(input, "input", "maxPoolBackprop");
-  const $output = convertToTensor(output, "output", "maxPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`);
-  assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo($input.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    return backend2.maxPoolBackprop($dy, $input, $output, convInfo);
-  };
-  const inputs = {dy: $dy, input: $input, output: $output};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);
-}
-const maxPoolBackprop = op({maxPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPoolGradConfig = {
-  kernelName: MaxPool,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minGradConfig = {
-  kernelName: Min,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const minAttrs = attrs;
-    const {axis} = minAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(axis, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = minGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minimumGradConfig = {
-  kernelName: Minimum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(greater(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const modGradConfig = {
-  kernelName: Mod,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(dy, reduceAxes), a.shape);
-      }
-      return dy;
-    };
-    const derB = () => {
-      const res = mul(dy, neg(floor(div(a, b))));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const multiplyGradConfig = {
-  kernelName: Multiply,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = mul(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      const res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Negate_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const negateGradConfig = {
-  kernelName: Negate,
-  gradFunc: (dy) => {
-    return {x: () => neg(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const oneHotGradConfig = {
-  kernelName: OneHot,
-  inputsToSave: ["indices"],
-  gradFunc: (dy, saved) => {
-    const indices = saved[0];
-    return {indices: () => zeros(indices.shape, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const onesLikeGradConfig = {
-  kernelName: OnesLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const padV2GradConfig = {
-  kernelName: PadV2,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const x = saved[0];
-    const {paddings} = attrs;
-    const begin = paddings.map((p) => p[0]);
-    return {x: () => slice(dy, begin, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const powGradConfig = {
-  kernelName: Pow,
-  inputsToSave: ["a", "b"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [a, b, y] = saved;
-    const base = a;
-    const exp11 = b;
-    const outShape = assertAndGetBroadcastShape(base.shape, exp11.shape);
-    const derBase = () => {
-      const expFloat = cast(exp11, "float32");
-      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
-      const reduceAxes = getReductionAxes(base.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, base.shape);
-    };
-    const derExp = () => {
-      const condition = greater(base, 0);
-      const logBase = where(condition, log(base), zerosLike(base));
-      let res = mul(dy, mul(y, logBase));
-      const reduceAxes = getReductionAxes(exp11.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, exp11.shape);
-    };
-    return {a: derBase, b: derExp};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const preluGradConfig = {
-  kernelName: Prelu,
-  inputsToSave: ["x", "alpha"],
-  gradFunc: (dy, saved) => {
-    const [x, alpha] = saved;
-    const mask = greater(x, 0);
-    return {
-      x: () => where(mask, dy, mul(dy, alpha)),
-      alpha: () => {
-        let res = where(mask, zerosLike(dy), mul(dy, x));
-        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, alpha.shape);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reciprocalGradConfig = {
-  kernelName: Reciprocal,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, neg(square(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const relu6GradConfig = {
-  kernelName: Relu6,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const mask = mul(lessEqual(x, 6), step(x));
-    return {x: () => mul(dy, cast(mask, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reluGradConfig = {
-  kernelName: Relu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, cast(step(x), "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reshapeGradConfig = {
-  kernelName: Reshape,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => reshape(dy, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeBilinearGradConfig = {
-  kernelName: ResizeBilinear,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeBilinearBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeBilinearGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeNearestNeighborGradConfig = {
-  kernelName: ResizeNearestNeighbor,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeNearestNeighborBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeNearestNeighborGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reverseGradConfig = {
-  kernelName: Reverse,
-  gradFunc: (dy, saved, attrs) => {
-    const {dims} = attrs;
-    const axes = parseAxisParam(dims, dy.shape);
-    return {x: () => reverse(dy, axes)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const roundGradConfig = {
-  kernelName: Round,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const rsqrtGradConfig = {
-  kernelName: Rsqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SelectV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const selectV2PoolGradConfig = {
-  kernelName: SelectV2,
-  inputsToSave: ["condition"],
-  gradFunc: (dy, saved) => {
-    const [condition] = saved;
-    return {
-      condition: () => cast(zerosLike(condition), "float32"),
-      t: () => mul(dy, cast(condition, dy.dtype)),
-      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const seluGradConfig = {
-  kernelName: Selu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const mask = greater(x, scalar(0));
-        const scaleAlpha = scalar(SELU_SCALEALPHA);
-        const scale2 = scalar(SELU_SCALE);
-        const greaterThanZeroDer = mul(dy, scale2);
-        const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, "float32")));
-        return where(mask, greaterThanZeroDer, lessEqualZeroDer);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sigmoidGradConfig = {
-  kernelName: Sigmoid,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const signGradConfig = {
-  kernelName: Sign,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinGradConfig = {
-  kernelName: Sin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cos(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinhGradConfig = {
-  kernelName: Sinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cosh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sliceGradConfig = {
-  kernelName: Slice,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {begin, size} = attrs;
-    const inputShape = x.shape;
-    const [begin_, size_] = parseSliceParams(x, begin, size);
-    const paddings = [];
-    for (let i = 0; i < dy.rank; i++) {
-      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
-    }
-    return {x: () => pad(dy, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softmaxGradConfig = {
-  kernelName: Softmax,
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [y] = saved;
-    const {dim} = attrs;
-    const keepDims = true;
-    const dyTimesY = mul(dy, y);
-    return {
-      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softplusGradConfig = {
-  kernelName: Softplus,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, sigmoid(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spaceToBatchNDGradConfig = {
-  kernelName: SpaceToBatchND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, paddings} = attrs;
-    return {x: () => batchToSpaceND(dy, blockShape, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const splitVGradConfig = {
-  kernelName: SplitV,
-  gradFunc: (dy, saved, attrs) => {
-    const {axis} = attrs;
-    return {x: () => concat(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sqrtGradConfig = {
-  kernelName: Sqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, mul(sqrt(cast(x, "float32")), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squareGradConfig = {
-  kernelName: Square,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, mul(cast(x, "float32"), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squaredDifferenceGradConfig = {
-  kernelName: SquaredDifference,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const two = scalar(2);
-    const derA = () => mul(dy, mul(two, sub(a, b)));
-    const derB = () => mul(dy, mul(two, sub(b, a)));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const stepGradConfig = {
-  kernelName: Step,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const subGradConfig = {
-  kernelName: Sub,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(neg(res), b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sumGradConfig = {
-  kernelName: Sum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const expandedDyShape = x.shape.slice();
-    const {axis} = attrs;
-    const axes = parseAxisParam(axis, x.shape);
-    axes.forEach((axis2) => {
-      expandedDyShape[axis2] = 1;
-    });
-    const expandedDy = reshape(dy, expandedDyShape);
-    const derX = mul(expandedDy, ones2(x.shape, "float32"));
-    return {x: () => derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanGradConfig = {
-  kernelName: Tan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, square(cos(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanhGradConfig = {
-  kernelName: Tanh,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(sub(scalar(1), square(y)), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tileGradConfig = {
-  kernelName: Tile,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {reps} = attrs;
-    const derX = () => {
-      let xGrad = zerosLike(x);
-      if (x.rank === 1) {
-        for (let i = 0; i < reps[0]; ++i) {
-          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
-        }
-      } else if (x.rank === 2) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
-              x.shape[0],
-              x.shape[1]
-            ]));
-          }
-        }
-      } else if (x.rank === 3) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
-            }
-          }
-        }
-      } else if (x.rank === 4) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              for (let l = 0; l < reps[3]; ++l) {
-                xGrad = add2(xGrad, slice(dy, [
-                  i * x.shape[0],
-                  j * x.shape[1],
-                  k * x.shape[2],
-                  l * x.shape[3]
-                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
-              }
-            }
-          }
-        }
-      } else {
-        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
-      }
-      return xGrad;
-    };
-    return {x: derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const transposeGradConfig = {
-  kernelName: Transpose,
-  gradFunc: (dy, saved, attrs) => {
-    const transposeAttrs = attrs;
-    const {perm} = transposeAttrs;
-    const undoPerm = getUndoAxesPermutation(perm);
-    return {x: () => transpose(dy, undoPerm)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unpackGradConfig = {
-  kernelName: Unpack,
-  gradFunc: (dy, saved, attrs) => {
-    const unpackAttrs = attrs;
-    const {axis} = unpackAttrs;
-    return {value: () => stack(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unsortedSegmentSumGradConfig = {
-  kernelName: UnsortedSegmentSum,
-  inputsToSave: ["segmentIds"],
-  gradFunc: (dy, saved) => {
-    const [segmentIds] = saved;
-    const derX = () => {
-      return gatherDropNegatives(dy, segmentIds);
-    };
-    return {x: derX};
-  }
-};
-function gatherDropNegatives(x, indices) {
-  const zeroClippedIndices = maximum(indices, zerosLike(indices));
-  const gathered = gather(x, zeroClippedIndices);
-  let isPositive = greaterEqual(indices, scalar(0, "int32"));
-  const numIters = gathered.rank - isPositive.rank;
-  for (let i = 0; i < numIters; ++i) {
-    isPositive = expandDims(isPositive, i + 1);
-  }
-  isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
-  const zeroSlice = zerosLike(gathered);
-  return where(isPositive, gathered, zeroSlice);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const zerosLikeGradConfig = {
-  kernelName: ZerosLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gradConfigs = [
-  absGradConfig,
-  acosGradConfig,
-  acoshGradConfig,
-  addGradConfig,
-  addNGradConfig,
-  argMaxGradConfig,
-  argMinGradConfig,
-  asinGradConfig,
-  asinhGradConfig,
-  atan2GradConfig,
-  atanGradConfig,
-  atanhGradConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulGradConfig,
-  batchToSpaceNDGradConfig,
-  broadcastToGradConfig,
-  castGradConfig,
-  ceilGradConfig,
-  clipByValueGradConfig,
-  concatGradConfig,
-  conv2DBackpropInputGradConfig,
-  conv2DGradConfig,
-  conv3DGradConfig,
-  cosGradConfig,
-  coshGradConfig,
-  cumsumGradConfig,
-  depthwiseConv2dNativeGradConfig,
-  dilation2dGradConfig,
-  divGradConfig,
-  eluGradConfig,
-  erfGradConfig,
-  expGradConfig,
-  expm1GradConfig,
-  floorDivGradConfig,
-  floorGradConfig,
-  fusedBatchNormGradConfig,
-  gatherGradConfig,
-  greaterEqualGradConfig,
-  identityGradConfig,
-  isFiniteGradConfig,
-  isInfGradConfig,
-  isNanGradConfig,
-  log1pGradConfig,
-  logGradConfig,
-  logSoftmaxGradConfig,
-  lrnGradConfig,
-  maxGradConfig,
-  maxGradConfig,
-  maximumGradConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  minGradConfig,
-  minimumGradConfig,
-  modGradConfig,
-  multiplyGradConfig,
-  negateGradConfig,
-  oneHotGradConfig,
-  onesLikeGradConfig,
-  padV2GradConfig,
-  padV2GradConfig,
-  powGradConfig,
-  preluGradConfig,
-  reciprocalGradConfig,
-  relu6GradConfig,
-  reluGradConfig,
-  reshapeGradConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborGradConfig,
-  reverseGradConfig,
-  roundGradConfig,
-  rsqrtGradConfig,
-  selectV2PoolGradConfig,
-  seluGradConfig,
-  sigmoidGradConfig,
-  signGradConfig,
-  sinGradConfig,
-  sinhGradConfig,
-  sliceGradConfig,
-  softmaxGradConfig,
-  softplusGradConfig,
-  spaceToBatchNDGradConfig,
-  spaceToBatchNDGradConfig,
-  splitVGradConfig,
-  splitVGradConfig,
-  sqrtGradConfig,
-  squaredDifferenceGradConfig,
-  squareGradConfig,
-  stepGradConfig,
-  subGradConfig,
-  sumGradConfig,
-  tanGradConfig,
-  tanhGradConfig,
-  tileGradConfig,
-  transposeGradConfig,
-  unpackGradConfig,
-  unsortedSegmentSumGradConfig,
-  zerosLikeGradConfig
-];
-for (const gradientConfig of gradConfigs) {
-  registerGradient(gradientConfig);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/index.js
-const dist_exports = {};
-__export(dist_exports, {
-  Abs: () => Abs,
-  Acos: () => Acos,
-  Acosh: () => Acosh,
-  AdadeltaOptimizer: () => AdadeltaOptimizer,
-  AdagradOptimizer: () => AdagradOptimizer,
-  AdamOptimizer: () => AdamOptimizer,
-  AdamaxOptimizer: () => AdamaxOptimizer,
-  Add: () => Add,
-  AddN: () => AddN,
-  All: () => All,
-  Any: () => Any,
-  ArgMax: () => ArgMax,
-  ArgMin: () => ArgMin,
-  Asin: () => Asin,
-  Asinh: () => Asinh,
-  Atan: () => Atan,
-  Atan2: () => Atan2,
-  Atanh: () => Atanh,
-  AvgPool: () => AvgPool,
-  AvgPool3D: () => AvgPool3D,
-  AvgPool3DBackprop: () => AvgPool3DBackprop,
-  AvgPoolBackprop: () => AvgPoolBackprop,
-  BatchMatMul: () => BatchMatMul,
-  BatchToSpaceND: () => BatchToSpaceND,
-  BroadcastTo: () => BroadcastTo,
-  Cast: () => Cast,
-  Ceil: () => Ceil,
-  ClipByValue: () => ClipByValue,
-  Complex: () => Complex,
-  Concat: () => Concat,
-  Conv2D: () => Conv2D,
-  Conv2DBackpropFilter: () => Conv2DBackpropFilter,
-  Conv2DBackpropInput: () => Conv2DBackpropInput,
-  Conv3D: () => Conv3D,
-  Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
-  Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
-  Cos: () => Cos,
-  Cosh: () => Cosh,
-  CropAndResize: () => CropAndResize,
-  Cumsum: () => Cumsum,
-  DataStorage: () => DataStorage,
-  DepthToSpace: () => DepthToSpace,
-  DepthwiseConv2dNative: () => DepthwiseConv2dNative,
-  DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
-  DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
-  Diag: () => Diag,
-  Dilation2D: () => Dilation2D,
-  Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
-  Dilation2DBackpropInput: () => Dilation2DBackpropInput,
-  Div: () => Div,
-  ENV: () => ENV,
-  Elu: () => Elu,
-  EluGrad: () => EluGrad,
-  Environment: () => Environment,
-  Equal: () => Equal,
-  Erf: () => Erf,
-  Exp: () => Exp,
-  Expm1: () => Expm1,
-  FFT: () => FFT,
-  Fill: () => Fill,
-  FlipLeftRight: () => FlipLeftRight,
-  Floor: () => Floor,
-  FloorDiv: () => FloorDiv,
-  FromPixels: () => FromPixels,
-  FusedBatchNorm: () => FusedBatchNorm,
-  FusedConv2D: () => FusedConv2D,
-  FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
-  GatherNd: () => GatherNd,
-  GatherV2: () => GatherV2,
-  Greater: () => Greater,
-  GreaterEqual: () => GreaterEqual,
-  IFFT: () => IFFT,
-  Identity: () => Identity,
-  Imag: () => Imag,
-  IsFinite: () => IsFinite,
-  IsInf: () => IsInf,
-  IsNan: () => IsNan,
-  KernelBackend: () => KernelBackend,
-  LRN: () => LRN,
-  LRNBackprop: () => LRNBackprop,
-  Less: () => Less,
-  LessEqual: () => LessEqual,
-  LinSpace: () => LinSpace,
-  Log: () => Log,
-  Log1p: () => Log1p,
-  LogSoftmax: () => LogSoftmax,
-  LogicalAnd: () => LogicalAnd,
-  LogicalNot: () => LogicalNot,
-  LogicalOr: () => LogicalOr,
-  Max: () => Max,
-  MaxPool: () => MaxPool,
-  MaxPool3D: () => MaxPool3D,
-  MaxPool3DBackprop: () => MaxPool3DBackprop,
-  MaxPoolBackprop: () => MaxPoolBackprop,
-  MaxPoolWithArgmax: () => MaxPoolWithArgmax,
-  Maximum: () => Maximum,
-  Mean: () => Mean,
-  Min: () => Min,
-  Minimum: () => Minimum,
-  Mod: () => Mod,
-  MomentumOptimizer: () => MomentumOptimizer,
-  Multiply: () => Multiply,
-  Negate: () => Negate,
-  NonMaxSuppressionV3: () => NonMaxSuppressionV3,
-  NonMaxSuppressionV4: () => NonMaxSuppressionV4,
-  NonMaxSuppressionV5: () => NonMaxSuppressionV5,
-  NotEqual: () => NotEqual,
-  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
-  OneHot: () => OneHot,
-  OnesLike: () => OnesLike,
-  Optimizer: () => Optimizer,
-  PadV2: () => PadV2,
-  Pool: () => Pool,
-  Pow: () => Pow,
-  Prelu: () => Prelu,
-  Prod: () => Prod,
-  RMSPropOptimizer: () => RMSPropOptimizer,
-  Range: () => Range,
-  Rank: () => Rank,
-  Real: () => Real,
-  Reciprocal: () => Reciprocal,
-  Reduction: () => Reduction,
-  Relu: () => Relu,
-  Relu6: () => Relu6,
-  Reshape: () => Reshape,
-  ResizeBilinear: () => ResizeBilinear,
-  ResizeBilinearGrad: () => ResizeBilinearGrad,
-  ResizeNearestNeighbor: () => ResizeNearestNeighbor,
-  ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
-  Reverse: () => Reverse,
-  RotateWithOffset: () => RotateWithOffset,
-  Round: () => Round,
-  Rsqrt: () => Rsqrt,
-  SGDOptimizer: () => SGDOptimizer,
-  ScatterNd: () => ScatterNd,
-  SelectV2: () => SelectV2,
-  Selu: () => Selu,
-  Sigmoid: () => Sigmoid,
-  Sign: () => Sign,
-  Sin: () => Sin,
-  Sinh: () => Sinh,
-  Slice: () => Slice,
-  Softmax: () => Softmax,
-  Softplus: () => Softplus,
-  SpaceToBatchND: () => SpaceToBatchND,
-  SparseToDense: () => SparseToDense,
-  SplitV: () => SplitV,
-  Sqrt: () => Sqrt,
-  Square: () => Square,
-  SquaredDifference: () => SquaredDifference,
-  Step: () => Step,
-  StridedSlice: () => StridedSlice,
-  Sub: () => Sub,
-  Sum: () => Sum,
-  Tan: () => Tan,
-  Tanh: () => Tanh,
-  Tensor: () => Tensor,
-  TensorBuffer: () => TensorBuffer,
-  Tile: () => Tile,
-  TopK: () => TopK,
-  Transpose: () => Transpose,
-  Unpack: () => Unpack,
-  UnsortedSegmentSum: () => UnsortedSegmentSum,
-  Variable: () => Variable,
-  ZerosLike: () => ZerosLike,
-  _FusedMatMul: () => _FusedMatMul,
-  abs: () => abs,
-  acos: () => acos,
-  acosh: () => acosh,
-  add: () => add2,
-  addN: () => addN,
-  addStrict: () => addStrict,
-  all: () => all,
-  any: () => any,
-  argMax: () => argMax,
-  argMin: () => argMin,
-  asin: () => asin,
-  asinh: () => asinh,
-  atan: () => atan,
-  atan2: () => atan2,
-  atanh: () => atanh,
-  avgPool: () => avgPool,
-  avgPool3d: () => avgPool3d,
-  backend: () => backend,
-  backend_util: () => backend_util_exports,
-  basicLSTMCell: () => basicLSTMCell,
-  batchNorm: () => batchNorm,
-  batchNorm2d: () => batchNorm2d,
-  batchNorm3d: () => batchNorm3d,
-  batchNorm4d: () => batchNorm4d,
-  batchToSpaceND: () => batchToSpaceND,
-  booleanMaskAsync: () => booleanMaskAsync,
-  broadcastTo: () => broadcastTo,
-  browser: () => browser_exports,
-  buffer: () => buffer,
-  cast: () => cast,
-  ceil: () => ceil,
-  clipByValue: () => clipByValue,
-  clone: () => clone,
-  complex: () => complex,
-  concat: () => concat,
-  concat1d: () => concat1d,
-  concat2d: () => concat2d,
-  concat3d: () => concat3d,
-  concat4d: () => concat4d,
-  conv1d: () => conv1d,
-  conv2d: () => conv2d,
-  conv2dTranspose: () => conv2dTranspose,
-  conv3d: () => conv3d,
-  conv3dTranspose: () => conv3dTranspose,
-  cos: () => cos,
-  cosh: () => cosh,
-  cosineWindow: () => cosineWindow,
-  cumsum: () => cumsum,
-  customGrad: () => customGrad,
-  deprecationWarn: () => deprecationWarn,
-  depthToSpace: () => depthToSpace,
-  depthwiseConv2d: () => depthwiseConv2d,
-  device_util: () => device_util_exports,
-  diag: () => diag,
-  dilation2d: () => dilation2d,
-  disableDeprecationWarnings: () => disableDeprecationWarnings,
-  dispose: () => dispose,
-  disposeVariables: () => disposeVariables,
-  div: () => div,
-  divNoNan: () => divNoNan,
-  divStrict: () => divStrict,
-  dot: () => dot,
-  dropout: () => dropout,
-  elu: () => elu,
-  enableDebugMode: () => enableDebugMode,
-  enableProdMode: () => enableProdMode,
-  enclosingPowerOfTwo: () => enclosingPowerOfTwo,
-  engine: () => engine15,
-  env: () => env,
-  equal: () => equal,
-  equalStrict: () => equalStrict,
-  erf: () => erf,
-  exp: () => exp,
-  expandDims: () => expandDims,
-  expm1: () => expm1,
-  eye: () => eye,
-  fft: () => fft,
-  fill: () => fill,
-  findBackend: () => findBackend,
-  findBackendFactory: () => findBackendFactory,
-  floor: () => floor,
-  floorDiv: () => floorDiv,
-  fused: () => fused_ops_exports,
-  gather: () => gather,
-  gatherND: () => gatherND,
-  gather_util: () => gather_nd_util_exports,
-  getBackend: () => getBackend,
-  getGradient: () => getGradient,
-  getKernel: () => getKernel,
-  getKernelsForBackend: () => getKernelsForBackend,
-  grad: () => grad,
-  grads: () => grads,
-  greater: () => greater,
-  greaterEqual: () => greaterEqual,
-  greaterEqualStrict: () => greaterEqualStrict,
-  greaterStrict: () => greaterStrict,
-  ifft: () => ifft,
-  imag: () => imag,
-  image: () => image,
-  inTopKAsync: () => inTopKAsync,
-  io: () => io_exports,
-  irfft: () => irfft,
-  isFinite: () => isFinite2,
-  isInf: () => isInf,
-  isNaN: () => isNaN2,
-  keep: () => keep,
-  kernel_impls: () => kernel_impls_exports,
-  leakyRelu: () => leakyRelu,
-  less: () => less,
-  lessEqual: () => lessEqual,
-  lessEqualStrict: () => lessEqualStrict,
-  lessStrict: () => lessStrict,
-  linalg: () => linalg,
-  linspace: () => linspace,
-  localResponseNormalization: () => localResponseNormalization,
-  log: () => log,
-  log1p: () => log1p,
-  logSigmoid: () => logSigmoid,
-  logSoftmax: () => logSoftmax,
-  logSumExp: () => logSumExp,
-  logicalAnd: () => logicalAnd,
-  logicalNot: () => logicalNot,
-  logicalOr: () => logicalOr,
-  logicalXor: () => logicalXor,
-  losses: () => losses,
-  matMul: () => matMul,
-  math: () => math_exports,
-  max: () => max,
-  maxPool: () => maxPool,
-  maxPool3d: () => maxPool3d,
-  maxPoolWithArgmax: () => maxPoolWithArgmax,
-  maximum: () => maximum,
-  maximumStrict: () => maximumStrict,
-  mean: () => mean,
-  memory: () => memory,
-  min: () => min,
-  minimum: () => minimum,
-  minimumStrict: () => minimumStrict,
-  mod: () => mod,
-  modStrict: () => modStrict,
-  moments: () => moments,
-  movingAverage: () => movingAverage,
-  mul: () => mul,
-  mulStrict: () => mulStrict,
-  multiRNNCell: () => multiRNNCell,
-  multinomial: () => multinomial,
-  neg: () => neg,
-  nextFrame: () => nextFrame,
-  norm: () => norm,
-  notEqual: () => notEqual,
-  notEqualStrict: () => notEqualStrict,
-  oneHot: () => oneHot,
-  ones: () => ones2,
-  onesLike: () => onesLike,
-  op: () => op,
-  outerProduct: () => outerProduct,
-  pad: () => pad,
-  pad1d: () => pad1d,
-  pad2d: () => pad2d,
-  pad3d: () => pad3d,
-  pad4d: () => pad4d,
-  pool: () => pool,
-  pow: () => pow,
-  powStrict: () => powStrict,
-  prelu: () => prelu,
-  print: () => print,
-  prod: () => prod,
-  profile: () => profile,
-  rand: () => rand,
-  randomGamma: () => randomGamma,
-  randomNormal: () => randomNormal,
-  randomUniform: () => randomUniform,
-  range: () => range,
-  ready: () => ready,
-  real: () => real,
-  reciprocal: () => reciprocal,
-  registerBackend: () => registerBackend,
-  registerGradient: () => registerGradient,
-  registerKernel: () => registerKernel,
-  relu: () => relu,
-  relu6: () => relu6,
-  removeBackend: () => removeBackend,
-  reshape: () => reshape,
-  reverse: () => reverse,
-  reverse1d: () => reverse1d,
-  reverse2d: () => reverse2d,
-  reverse3d: () => reverse3d,
-  reverse4d: () => reverse4d,
-  rfft: () => rfft,
-  round: () => round,
-  rsqrt: () => rsqrt,
-  scalar: () => scalar,
-  scatterND: () => scatterND,
-  scatter_util: () => scatter_nd_util_exports,
-  selu: () => selu,
-  separableConv2d: () => separableConv2d,
-  serialization: () => serialization_exports,
-  setBackend: () => setBackend,
-  setPlatform: () => setPlatform,
-  setdiff1dAsync: () => setdiff1dAsync,
-  sigmoid: () => sigmoid,
-  sign: () => sign,
-  signal: () => signal,
-  sin: () => sin,
-  sinh: () => sinh,
-  slice: () => slice,
-  slice1d: () => slice1d,
-  slice2d: () => slice2d,
-  slice3d: () => slice3d,
-  slice4d: () => slice4d,
-  slice_util: () => slice_util_exports,
-  softmax: () => softmax,
-  softplus: () => softplus,
-  spaceToBatchND: () => spaceToBatchND,
-  sparseToDense: () => sparseToDense,
-  spectral: () => spectral,
-  split: () => split,
-  sqrt: () => sqrt,
-  square: () => square,
-  squaredDifference: () => squaredDifference,
-  squaredDifferenceStrict: () => squaredDifferenceStrict,
-  squeeze: () => squeeze,
-  stack: () => stack,
-  step: () => step,
-  stridedSlice: () => stridedSlice,
-  sub: () => sub,
-  subStrict: () => subStrict,
-  sum: () => sum2,
-  sumOutType: () => sumOutType,
-  tan: () => tan,
-  tanh: () => tanh2,
-  tensor: () => tensor4,
-  tensor1d: () => tensor1d,
-  tensor2d: () => tensor2d,
-  tensor3d: () => tensor3d,
-  tensor4d: () => tensor4d,
-  tensor5d: () => tensor5d,
-  tensor6d: () => tensor6d,
-  tensor_util: () => tensor_util_exports,
-  test_util: () => test_util_exports,
-  tidy: () => tidy,
-  tile: () => tile,
-  time: () => time,
-  topk: () => topk,
-  train: () => train,
-  transpose: () => transpose,
-  truncatedNormal: () => truncatedNormal,
-  unregisterGradient: () => unregisterGradient,
-  unregisterKernel: () => unregisterKernel,
-  unsortedSegmentSum: () => unsortedSegmentSum,
-  unstack: () => unstack,
-  upcastType: () => upcastType,
-  util: () => util_exports,
-  valueAndGrad: () => valueAndGrad,
-  valueAndGrads: () => valueAndGrads,
-  variable: () => variable,
-  variableGrads: () => variableGrads,
-  version_core: () => version,
-  where: () => where,
-  whereAsync: () => whereAsync,
-  zeros: () => zeros,
-  zerosLike: () => zerosLike
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
 // build/src/draw/drawContour.js
 function drawContour(ctx, points, isClosed = false) {
   ctx.beginPath();
@@ -19987,23 +95,24 @@ __export(utils_exports, {
   isTensor4D: () => isTensor4D,
   isValidNumber: () => isValidNumber,
   isValidProbablitiy: () => isValidProbablitiy,
-  range: () => range4,
-  round: () => round3
+  range: () => range,
+  round: () => round
 });
-function isTensor(tensor17, dim) {
-  return tensor17 instanceof Tensor && tensor17.shape.length === dim;
+const tf = __toModule(require("@tensorflow/tfjs"));
+function isTensor(tensor2, dim) {
+  return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim;
 }
-function isTensor1D(tensor17) {
-  return isTensor(tensor17, 1);
+function isTensor1D(tensor2) {
+  return isTensor(tensor2, 1);
 }
-function isTensor2D(tensor17) {
-  return isTensor(tensor17, 2);
+function isTensor2D(tensor2) {
+  return isTensor(tensor2, 2);
 }
-function isTensor3D(tensor17) {
-  return isTensor(tensor17, 3);
+function isTensor3D(tensor2) {
+  return isTensor(tensor2, 3);
 }
-function isTensor4D(tensor17) {
-  return isTensor(tensor17, 4);
+function isTensor4D(tensor2) {
+  return isTensor(tensor2, 4);
 }
 function isFloat(num) {
   return num % 1 !== 0;
@@ -20011,7 +120,7 @@ function isFloat(num) {
 function isEven(num) {
   return num % 2 === 0;
 }
-function round3(num, prec = 2) {
+function round(num, prec = 2) {
   const f = Math.pow(10, prec);
   return Math.floor(num * f) / f;
 }
@@ -20023,10 +132,10 @@ function computeReshapedDimensions({width, height}, inputSize) {
   return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
 }
 function getCenterPoint(pts) {
-  return pts.reduce((sum26, pt) => sum26.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
+  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
 }
-function range4(num, start, step7) {
-  return Array(num).fill(0).map((_, i) => start + i * step7);
+function range(num, start, step) {
+  return Array(num).fill(0).map((_, i) => start + i * step);
 }
 function isValidNumber(num) {
   return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
@@ -20299,15 +408,15 @@ function iou(box1, box2, isIOU = true) {
 function minBbox(pts) {
   const xs = pts.map((pt) => pt.x);
   const ys = pts.map((pt) => pt.y);
-  const minX = xs.reduce((min4, x) => x < min4 ? x : min4, Infinity);
-  const minY = ys.reduce((min4, y) => y < min4 ? y : min4, Infinity);
-  const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0);
-  const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0);
+  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity);
+  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity);
+  const maxX = xs.reduce((max, x) => max < x ? x : max, 0);
+  const maxY = ys.reduce((max, y) => max < y ? y : max, 0);
   return new BoundingBox(minX, minY, maxX, maxY);
 }
 
 // build/src/ops/nonMaxSuppression.js
-function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
+function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) {
   let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex);
   const pick = [];
   while (indicesSortedByScore.length > 0) {
@@ -20327,20 +436,22 @@ function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
 }
 
 // build/src/ops/normalize.js
+const tf2 = __toModule(require("@tensorflow/tfjs"));
 function normalize(x, meanRgb) {
-  return tidy(() => {
+  return tf2.tidy(() => {
     const [r, g, b] = meanRgb;
-    const avg_r = fill([...x.shape.slice(0, 3), 1], r);
-    const avg_g = fill([...x.shape.slice(0, 3), 1], g);
-    const avg_b = fill([...x.shape.slice(0, 3), 1], b);
-    const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
-    return sub(x, avg_rgb);
+    const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r);
+    const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g);
+    const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b);
+    const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
+    return tf2.sub(x, avg_rgb);
   });
 }
 
 // build/src/ops/padToSquare.js
+const tf3 = __toModule(require("@tensorflow/tfjs"));
 function padToSquare(imgTensor, isCenterImage = false) {
-  return tidy(() => {
+  return tf3.tidy(() => {
     const [height, width] = imgTensor.shape.slice(1);
     if (height === width) {
       return imgTensor;
@@ -20351,7 +462,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
     const createPaddingTensor = (paddingAmount2) => {
       const paddingTensorShape = imgTensor.shape.slice();
       paddingTensorShape[paddingAxis] = paddingAmount2;
-      return fill(paddingTensorShape, 0);
+      return tf3.fill(paddingTensorShape, 0);
     };
     const paddingTensorAppend = createPaddingTensor(paddingAmount);
     const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -20360,8 +471,8 @@ function padToSquare(imgTensor, isCenterImage = false) {
       paddingTensorPrepend,
       imgTensor,
       paddingTensorAppend
-    ].filter((t) => !!t).map((t) => cast(t, "float32"));
-    return concat(tensorsToStack, paddingAxis);
+    ].filter((t) => !!t).map((t) => tf3.cast(t, "float32"));
+    return tf3.concat(tensorsToStack, paddingAxis);
   });
 }
 
@@ -20378,7 +489,7 @@ function shuffleArray(inputArray) {
 }
 
 // build/src/ops/index.js
-function sigmoid6(x) {
+function sigmoid(x) {
   return 1 / (1 + Math.exp(-x));
 }
 function inverseSigmoid(x) {
@@ -20515,7 +626,7 @@ class FaceMatch {
     return this._distance;
   }
   toString(withDistance = true) {
-    return `${this.label}${withDistance ? ` (${round3(this.distance)})` : ""}`;
+    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
   }
 }
 
@@ -20602,7 +713,7 @@ function extendWithFaceDetection(sourceObj, detection) {
 
 // build/src/env/createBrowserEnv.js
 function createBrowserEnv() {
-  const fetch3 = window["fetch"] || function() {
+  const fetch = window["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for browser environment");
   };
   const readFile = function() {
@@ -20616,7 +727,7 @@ function createBrowserEnv() {
     Video: HTMLVideoElement,
     createCanvasElement: () => document.createElement("canvas"),
     createImageElement: () => document.createElement("img"),
-    fetch: fetch3,
+    fetch,
     readFile
   };
 }
@@ -20633,8 +744,8 @@ function createFileSystem(fs) {
   }
   const readFile = fs ? function(filePath) {
     return new Promise((res, rej) => {
-      fs.readFile(filePath, function(err, buffer11) {
-        return err ? rej(err) : res(buffer11);
+      fs.readFile(filePath, function(err, buffer) {
+        return err ? rej(err) : res(buffer);
       });
     });
   } : function() {
@@ -20661,11 +772,11 @@ function createNodejsEnv() {
     }
     throw new Error("createImageElement - missing Image implementation for nodejs environment");
   };
-  const fetch3 = global["fetch"] || function() {
+  const fetch = global["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for nodejs environment");
   };
   const fileSystem = createFileSystem();
-  return __assign({
+  return {
     Canvas: Canvas || class {
     },
     CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
@@ -20678,12 +789,13 @@ function createNodejsEnv() {
     },
     createCanvasElement,
     createImageElement,
-    fetch: fetch3
-  }, fileSystem);
+    fetch,
+    ...fileSystem
+  };
 }
 
 // build/src/env/isBrowser.js
-function isBrowser2() {
+function isBrowser() {
   return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
 }
 
@@ -20691,42 +803,42 @@ function isBrowser2() {
 
 // build/src/env/index.js
 const isNodejs = __toModule(require_isNodejs());
-let environment11;
+let environment;
 function getEnv() {
-  if (!environment11) {
+  if (!environment) {
     throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
   }
-  return environment11;
+  return environment;
 }
-function setEnv(env17) {
-  environment11 = env17;
+function setEnv(env16) {
+  environment = env16;
 }
 function initialize() {
-  if (isBrowser2()) {
+  if (isBrowser()) {
     return setEnv(createBrowserEnv());
   }
   if (isNodejs.isNodejs()) {
     return setEnv(createNodejsEnv());
   }
 }
-function monkeyPatch(env17) {
-  if (!environment11) {
+function monkeyPatch(env16) {
+  if (!environment) {
     initialize();
   }
-  if (!environment11) {
+  if (!environment) {
     throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
   }
-  const {Canvas = environment11.Canvas, Image = environment11.Image} = env17;
-  environment11.Canvas = Canvas;
-  environment11.Image = Image;
-  environment11.createCanvasElement = env17.createCanvasElement || (() => new Canvas());
-  environment11.createImageElement = env17.createImageElement || (() => new Image());
-  environment11.ImageData = env17.ImageData || environment11.ImageData;
-  environment11.Video = env17.Video || environment11.Video;
-  environment11.fetch = env17.fetch || environment11.fetch;
-  environment11.readFile = env17.readFile || environment11.readFile;
+  const {Canvas = environment.Canvas, Image = environment.Image} = env16;
+  environment.Canvas = Canvas;
+  environment.Image = Image;
+  environment.createCanvasElement = env16.createCanvasElement || (() => new Canvas());
+  environment.createImageElement = env16.createImageElement || (() => new Image());
+  environment.ImageData = env16.ImageData || environment.ImageData;
+  environment.Video = env16.Video || environment.Video;
+  environment.fetch = env16.fetch || environment.fetch;
+  environment.readFile = env16.readFile || environment.readFile;
 }
-const env2 = {
+const env = {
   getEnv,
   setEnv,
   initialize,
@@ -20734,14 +846,14 @@ const env2 = {
   createFileSystem,
   createNodejsEnv,
   monkeyPatch,
-  isBrowser: isBrowser2,
+  isBrowser,
   isNodejs: isNodejs.isNodejs
 };
 initialize();
 
 // build/src/dom/resolveInput.js
 function resolveInput(arg) {
-  if (!env2.isNodejs() && typeof arg === "string") {
+  if (!env.isNodejs() && typeof arg === "string") {
     return document.getElementById(arg);
   }
   return arg;
@@ -20749,7 +861,7 @@ function resolveInput(arg) {
 
 // build/src/dom/getContext2dOrThrow.js
 function getContext2dOrThrow(canvasArg) {
-  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env2.getEnv();
+  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
   if (canvasArg instanceof CanvasRenderingContext2D2) {
     return canvasArg;
   }
@@ -20871,21 +983,21 @@ function drawDetections(canvasArg, detections) {
   detectionsArray.forEach((det) => {
     const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0;
     const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det);
-    const label = score ? `${round3(score)}` : void 0;
+    const label = score ? `${round(score)}` : void 0;
     new DrawBox(box, {label}).draw(canvasArg);
   });
 }
 
 // build/src/dom/isMediaLoaded.js
 function isMediaLoaded(media) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
 }
 
 // build/src/dom/awaitMediaLoaded.js
 function awaitMediaLoaded(media) {
   return new Promise((resolve, reject) => {
-    if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media)) {
+    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
       return resolve(null);
     }
     function onLoad(e) {
@@ -20918,7 +1030,7 @@ function bufferToImage(buf) {
       if (typeof reader.result !== "string") {
         return reject("bufferToImage - expected reader.result to be a string, in onload");
       }
-      const img = env2.getEnv().createImageElement();
+      const img = env.getEnv().createImageElement();
       img.onload = () => resolve(img);
       img.onerror = reject;
       img.src = reader.result;
@@ -20930,7 +1042,7 @@ function bufferToImage(buf) {
 
 // build/src/dom/getMediaDimensions.js
 function getMediaDimensions(input) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   if (input instanceof Image) {
     return new Dimensions(input.naturalWidth, input.naturalHeight);
   }
@@ -20942,14 +1054,14 @@ function getMediaDimensions(input) {
 
 // build/src/dom/createCanvas.js
 function createCanvas({width, height}) {
-  const {createCanvasElement} = env2.getEnv();
+  const {createCanvasElement} = env.getEnv();
   const canvas = createCanvasElement();
   canvas.width = width;
   canvas.height = height;
   return canvas;
 }
 function createCanvasFromMedia(media, dims) {
-  const {ImageData: ImageData2} = env2.getEnv();
+  const {ImageData: ImageData2} = env.getEnv();
   if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
     throw new Error("createCanvasFromMedia - media has not finished loading yet");
   }
@@ -20964,24 +1076,25 @@ function createCanvasFromMedia(media, dims) {
 }
 
 // build/src/dom/imageTensorToCanvas.js
+const tf4 = __toModule(require("@tensorflow/tfjs"));
 async function imageTensorToCanvas(imgTensor, canvas) {
-  const targetCanvas = canvas || env2.getEnv().createCanvasElement();
+  const targetCanvas = canvas || env.getEnv().createCanvasElement();
   const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-  const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-  await browser_exports.toPixels(imgTensor3D, targetCanvas);
+  const imgTensor3D = tf4.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
+  await tf4.browser.toPixels(imgTensor3D, targetCanvas);
   imgTensor3D.dispose();
   return targetCanvas;
 }
 
 // build/src/dom/isMediaElement.js
 function isMediaElement(input) {
-  const {Image, Canvas, Video} = env2.getEnv();
+  const {Image, Canvas, Video} = env.getEnv();
   return input instanceof Image || input instanceof Canvas || input instanceof Video;
 }
 
 // build/src/dom/imageToSquare.js
 function imageToSquare(input, inputSize, centerImage = false) {
-  const {Image, Canvas} = env2.getEnv();
+  const {Image, Canvas} = env.getEnv();
   if (!(input instanceof Image || input instanceof Canvas)) {
     throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
   }
@@ -20999,6 +1112,7 @@ function imageToSquare(input, inputSize, centerImage = false) {
 }
 
 // build/src/dom/NetInput.js
+const tf5 = __toModule(require("@tensorflow/tfjs"));
 class NetInput {
   constructor(inputs, treatAsBatchInput = false) {
     this._imageTensors = [];
@@ -21025,7 +1139,7 @@ class NetInput {
         this._inputDimensions[idx] = input.shape.slice(1);
         return;
       }
-      const canvas = input instanceof env2.getEnv().Canvas ? input : createCanvasFromMedia(input);
+      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
       this._canvases[idx] = canvas;
       this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
     });
@@ -21049,7 +1163,7 @@ class NetInput {
     return this._inputSize;
   }
   get reshapedInputDimensions() {
-    return range4(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
+    return range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
   }
   getInput(batchIdx) {
     return this.canvases[batchIdx] || this.imageTensors[batchIdx];
@@ -21073,23 +1187,23 @@ class NetInput {
   }
   toBatchTensor(inputSize, isCenterInputs = true) {
     this._inputSize = inputSize;
-    return tidy(() => {
-      const inputTensors = range4(this.batchSize, 0, 1).map((batchIdx) => {
+    return tf5.tidy(() => {
+      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
         const input = this.getInput(batchIdx);
-        if (input instanceof Tensor) {
+        if (input instanceof tf5.Tensor) {
           let imgTensor = isTensor4D(input) ? input : input.expandDims();
           imgTensor = padToSquare(imgTensor, isCenterInputs);
           if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
-            imgTensor = image.resizeBilinear(imgTensor, [inputSize, inputSize]);
+            imgTensor = tf5.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
           }
           return imgTensor.as3D(inputSize, inputSize, 3);
         }
-        if (input instanceof env2.getEnv().Canvas) {
-          return browser_exports.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+        if (input instanceof env.getEnv().Canvas) {
+          return tf5.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
         }
         throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
       });
-      const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
+      const batchTensor = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
       return batchTensor;
     });
   }
@@ -21126,7 +1240,7 @@ async function toNetInput(inputs) {
 
 // build/src/dom/extractFaces.js
 async function extractFaces(input, detections) {
-  const {Canvas} = env2.getEnv();
+  const {Canvas} = env.getEnv();
   let canvas = input;
   if (!(input instanceof Canvas)) {
     const netInput = await toNetInput(input);
@@ -21146,6 +1260,7 @@ async function extractFaces(input, detections) {
 }
 
 // build/src/dom/extractFaceTensors.js
+const tf6 = __toModule(require("@tensorflow/tfjs"));
 async function extractFaceTensors(imageTensor, detections) {
   if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
     throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");
@@ -21153,18 +1268,18 @@ async function extractFaceTensors(imageTensor, detections) {
   if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
     throw new Error("extractFaceTensors - batchSize > 1 not supported");
   }
-  return tidy(() => {
+  return tf6.tidy(() => {
     const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);
     const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight));
-    const faceTensors = boxes.map(({x, y, width, height}) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
+    const faceTensors = boxes.map(({x, y, width, height}) => tf6.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
     return faceTensors;
   });
 }
 
 // build/src/dom/fetchOrThrow.js
 async function fetchOrThrow(url, init) {
-  const fetch3 = env2.getEnv().fetch;
-  const res = await fetch3(url, init);
+  const fetch = env.getEnv().fetch;
+  const res = await fetch(url, init);
   if (!(res.status < 400)) {
     throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
   }
@@ -21219,10 +1334,11 @@ function getModelUris(uri, defaultModelName) {
 }
 
 // build/src/dom/loadWeightMap.js
+const tf7 = __toModule(require("@tensorflow/tfjs"));
 async function loadWeightMap(uri, defaultModelName) {
   const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
   let manifest = await fetchJson(manifestUri);
-  return io_exports.loadWeights(manifest, modelBaseUri);
+  return tf7.io.loadWeights(manifest, modelBaseUri);
 }
 
 // build/src/dom/matchDimensions.js
@@ -21238,6 +1354,7 @@ function matchDimensions(input, reference, useMediaDimensions = false) {
 // build/src/dom/index.js
 
 // build/src/NeuralNetwork.js
+const tf8 = __toModule(require("@tensorflow/tfjs"));
 class NeuralNetwork {
   constructor(_name) {
     this._name = _name;
@@ -21257,10 +1374,10 @@ class NeuralNetwork {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     return obj[objProp];
   }
-  reassignParamFromPath(paramPath, tensor17) {
+  reassignParamFromPath(paramPath, tensor2) {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     obj[objProp].dispose();
-    obj[objProp] = tensor17;
+    obj[objProp] = tensor2;
   }
   getParamList() {
     return this._paramMappings.map(({paramPath}) => ({
@@ -21269,21 +1386,21 @@ class NeuralNetwork {
     }));
   }
   getTrainableParams() {
-    return this.getParamList().filter((param) => param.tensor instanceof Variable);
+    return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable);
   }
   getFrozenParams() {
-    return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
+    return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable));
   }
   variable() {
-    this.getFrozenParams().forEach(({path, tensor: tensor17}) => {
-      this.reassignParamFromPath(path, tensor17.variable());
+    this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
+      this.reassignParamFromPath(path, tensor2.variable());
     });
   }
   freeze() {
-    this.getTrainableParams().forEach(({path, tensor: variable3}) => {
-      const tensor17 = tensor4(variable3.dataSync());
-      variable3.dispose();
-      this.reassignParamFromPath(path, tensor17);
+    this.getTrainableParams().forEach(({path, tensor: variable}) => {
+      const tensor2 = tf8.tensor(variable.dataSync());
+      variable.dispose();
+      this.reassignParamFromPath(path, tensor2);
     });
   }
   dispose(throwOnRedispose = true) {
@@ -21296,7 +1413,7 @@ class NeuralNetwork {
     this._params = void 0;
   }
   serializeParams() {
-    return new Float32Array(this.getParamList().map(({tensor: tensor17}) => Array.from(tensor17.dataSync())).reduce((flat, arr) => flat.concat(arr)));
+    return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
   }
   async load(weightsOrUrl) {
     if (weightsOrUrl instanceof Float32Array) {
@@ -21316,12 +1433,12 @@ class NeuralNetwork {
     if (filePath && typeof filePath !== "string") {
       throw new Error(`${this._name}.loadFromDisk - expected model file path`);
     }
-    const {readFile} = env2.getEnv();
+    const {readFile} = env.getEnv();
     const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName());
     const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((filePath2) => readFile(filePath2).then((buf) => buf.buffer)));
-    const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk);
+    const loadWeights = tf8.io.weightsLoaderFactory(fetchWeightsFromDisk);
     const manifest = JSON.parse((await readFile(manifestUri)).toString());
-    const weightMap = await loadWeights2(manifest, modelBaseUri);
+    const weightMap = await loadWeights(manifest, modelBaseUri);
     this.loadFromWeightMap(weightMap);
   }
   loadFromWeightMap(weightMap) {
@@ -21345,7 +1462,7 @@ class NeuralNetwork {
       return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
     }, {nextObj: this.params});
     const {obj, objProp} = result;
-    if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+    if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) {
       throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
     }
     return {obj, objProp};
@@ -21353,41 +1470,44 @@ class NeuralNetwork {
 }
 
 // build/src/common/depthwiseSeparableConv.js
+const tf9 = __toModule(require("@tensorflow/tfjs"));
 function depthwiseSeparableConv(x, params, stride) {
-  return tidy(() => {
-    let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
-    out = add2(out, params.bias);
+  return tf9.tidy(() => {
+    let out = tf9.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
+    out = tf9.add(out, params.bias);
     return out;
   });
 }
 
 // build/src/faceFeatureExtractor/denseBlock.js
+const tf10 = __toModule(require("@tensorflow/tfjs"));
 function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
-  return tidy(() => {
-    const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
+  return tf10.tidy(() => {
+    const out1 = tf10.relu(isFirstLayer ? tf10.add(tf10.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
-    const in3 = relu(add2(out1, out2));
+    const in3 = tf10.relu(tf10.add(out1, out2));
     const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    return relu(add2(out1, add2(out2, out3)));
+    return tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
   });
 }
 function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-  return tidy(() => {
-    const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
+  return tf10.tidy(() => {
+    const out1 = tf10.relu(isFirstLayer ? tf10.add(tf10.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
-    const in3 = relu(add2(out1, out2));
+    const in3 = tf10.relu(tf10.add(out1, out2));
     const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    const in4 = relu(add2(out1, add2(out2, out3)));
+    const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
     const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-    return relu(add2(out1, add2(out2, add2(out3, out4))));
+    return tf10.relu(tf10.add(out1, tf10.add(out2, tf10.add(out3, out4))));
   });
 }
 
 // build/src/common/convLayer.js
+const tf11 = __toModule(require("@tensorflow/tfjs"));
 function convLayer(x, params, padding = "same", withRelu = false) {
-  return tidy(() => {
-    const out = add2(conv2d(x, params.filters, [1, 1], padding), params.bias);
-    return withRelu ? relu(out) : out;
+  return tf11.tidy(() => {
+    const out = tf11.add(tf11.conv2d(x, params.filters, [1, 1], padding), params.bias);
+    return withRelu ? tf11.relu(out) : out;
   });
 }
 
@@ -21401,20 +1521,22 @@ function disposeUnusedWeightTensors(weightMap, paramMappings) {
 }
 
 // build/src/common/extractConvParamsFactory.js
+const tf12 = __toModule(require("@tensorflow/tfjs"));
 function extractConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
-    const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const filters = tf12.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+    const bias = tf12.tensor1d(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
     return {filters, bias};
   };
 }
 
 // build/src/common/extractFCParamsFactory.js
+const tf13 = __toModule(require("@tensorflow/tfjs"));
 function extractFCParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
-    const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tensor1d(extractWeights(channelsOut));
+    const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
+    const fc_bias = tf13.tensor1d(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
     return {
       weights: fc_weights,
@@ -21433,11 +1555,12 @@ class SeparableConvParams {
 }
 
 // build/src/common/extractSeparableConvParamsFactory.js
+const tf14 = __toModule(require("@tensorflow/tfjs"));
 function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+    const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+    const bias = tf14.tensor1d(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
     return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
   };
@@ -21454,12 +1577,12 @@ function loadSeparableConvParamsFactory(extractWeightEntry) {
 // build/src/common/extractWeightEntryFactory.js
 function extractWeightEntryFactory(weightMap, paramMappings) {
   return function(originalPath, paramRank, mappedPath) {
-    const tensor17 = weightMap[originalPath];
-    if (!isTensor(tensor17, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor17}`);
+    const tensor2 = weightMap[originalPath];
+    if (!isTensor(tensor2, paramRank)) {
+      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
     }
     paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor17;
+    return tensor2;
   };
 }
 
@@ -21569,6 +1692,7 @@ function extractParamsFromWeigthMap(weightMap) {
 }
 
 // build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+const tf15 = __toModule(require("@tensorflow/tfjs"));
 class FaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("FaceFeatureExtractor");
@@ -21578,15 +1702,15 @@ class FaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tf15.tidy(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+      const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255));
       let out = denseBlock4(normalized, params.dense0, true);
       out = denseBlock4(out, params.dense1);
       out = denseBlock4(out, params.dense2);
       out = denseBlock4(out, params.dense3);
-      out = avgPool(out, [7, 7], [2, 2], "valid");
+      out = tf15.avgPool(out, [7, 7], [2, 2], "valid");
       return out;
     });
   }
@@ -21605,8 +1729,9 @@ class FaceFeatureExtractor extends NeuralNetwork {
 }
 
 // build/src/common/fullyConnectedLayer.js
+const tf16 = __toModule(require("@tensorflow/tfjs"));
 function fullyConnectedLayer(x, params) {
-  return tidy(() => add2(matMul(x, params.weights), params.bias));
+  return tf16.tidy(() => tf16.add(tf16.matMul(x, params.weights), params.bias));
 }
 
 // build/src/faceProcessor/extractParams.js
@@ -21652,6 +1777,7 @@ function seperateWeightMaps(weightMap) {
 }
 
 // build/src/faceProcessor/FaceProcessor.js
+const tf17 = __toModule(require("@tensorflow/tfjs"));
 class FaceProcessor extends NeuralNetwork {
   constructor(_name, faceFeatureExtractor) {
     super(_name);
@@ -21665,7 +1791,7 @@ class FaceProcessor extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tf17.tidy(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
       return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
     });
@@ -21715,12 +1841,13 @@ class FaceExpressions {
 }
 
 // build/src/faceExpressionNet/FaceExpressionNet.js
+const tf18 = __toModule(require("@tensorflow/tfjs"));
 class FaceExpressionNet extends FaceProcessor {
   constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
     super("FaceExpressionNet", faceFeatureExtractor);
   }
   forwardInput(input) {
-    return tidy(() => softmax(this.runNet(input)));
+    return tf18.tidy(() => tf18.softmax(this.runNet(input)));
   }
   async forward(input) {
     return this.forwardInput(await toNetInput(input));
@@ -21728,7 +1855,7 @@ class FaceExpressionNet extends FaceProcessor {
   async predictExpressions(input) {
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput);
-    const probabilitesByBatch = await Promise.all(unstack(out).map(async (t) => {
+    const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => {
       const data = await t.data();
       t.dispose();
       return data;
@@ -21770,7 +1897,7 @@ function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, te
     const sorted = expr.asSortedArray();
     const resultsToDisplay = sorted.filter((expr2) => expr2.probability > minConfidence);
     const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0);
-    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round3(expr2.probability)})`), anchor);
+    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round(expr2.probability)})`), anchor);
     drawTextField.draw(canvasArg);
   });
 }
@@ -21899,7 +2026,7 @@ function extractParams5(weights, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
@@ -21953,7 +2080,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
@@ -21967,22 +2094,23 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
 }
 
 // build/src/xception/TinyXception.js
+const tf19 = __toModule(require("@tensorflow/tfjs"));
 function conv(x, params, stride) {
-  return add2(conv2d(x, params.filters, stride, "same"), params.bias);
+  return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias);
 }
 function reductionBlock(x, params, isActivateInput = true) {
-  let out = isActivateInput ? relu(x) : x;
+  let out = isActivateInput ? tf19.relu(x) : x;
   out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-  out = maxPool(out, [3, 3], [2, 2], "same");
-  out = add2(out, conv(x, params.expansion_conv, [2, 2]));
+  out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]);
+  out = tf19.maxPool(out, [3, 3], [2, 2], "same");
+  out = tf19.add(out, conv(x, params.expansion_conv, [2, 2]));
   return out;
 }
 function mainBlock(x, params) {
-  let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]);
-  out = add2(out, x);
+  let out = depthwiseSeparableConv(tf19.relu(x), params.separable_conv0, [1, 1]);
+  out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]);
+  out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv2, [1, 1]);
+  out = tf19.add(out, x);
   return out;
 }
 class TinyXception extends NeuralNetwork {
@@ -21995,18 +2123,18 @@ class TinyXception extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyXception - load model before inference");
     }
-    return tidy(() => {
+    return tf19.tidy(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
-      let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
+      const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256));
+      let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
       out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
       out = reductionBlock(out, params.entry_flow.reduction_block_1);
-      range4(this._numMainBlocks, 0, 1).forEach((idx) => {
+      range(this._numMainBlocks, 0, 1).forEach((idx) => {
         out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
       });
       out = reductionBlock(out, params.exit_flow.reduction_block);
-      out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
+      out = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
       return out;
     });
   }
@@ -22067,6 +2195,7 @@ var Gender;
 })(Gender || (Gender = {}));
 
 // build/src/ageGenderNet/AgeGenderNet.js
+const tf20 = __toModule(require("@tensorflow/tfjs"));
 class AgeGenderNet extends NeuralNetwork {
   constructor(faceFeatureExtractor = new TinyXception(2)) {
     super("AgeGenderNet");
@@ -22080,18 +2209,18 @@ class AgeGenderNet extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tf20.tidy(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
+      const pooled = tf20.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
       const age = fullyConnectedLayer(pooled, params.fc.age).as1D();
       const gender = fullyConnectedLayer(pooled, params.fc.gender);
       return {age, gender};
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tf20.tidy(() => {
       const {age, gender} = this.runNet(input);
-      return {age, gender: softmax(gender)};
+      return {age, gender: tf20.softmax(gender)};
     });
   }
   async forward(input) {
@@ -22100,8 +2229,8 @@ class AgeGenderNet extends NeuralNetwork {
   async predictAgeAndGender(input) {
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput);
-    const ages = unstack(out.age);
-    const genders = unstack(out.gender);
+    const ages = tf20.unstack(out.age);
+    const genders = tf20.unstack(out.gender);
     const ageAndGenderTensors = ages.map((ageTensor, i) => ({
       ageTensor,
       genderTensor: genders[i]
@@ -22152,6 +2281,7 @@ class AgeGenderNet extends NeuralNetwork {
 // build/src/ageGenderNet/index.js
 
 // build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+const tf21 = __toModule(require("@tensorflow/tfjs"));
 class FaceLandmark68NetBase extends FaceProcessor {
   postProcess(output, inputSize, originalDimensions) {
     const inputDimensions = originalDimensions.map(({width, height}) => {
@@ -22162,10 +2292,10 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
     });
     const batchSize = inputDimensions.length;
-    return tidy(() => {
-      const createInterleavedTensor = (fillX, fillY) => stack([
-        fill([68], fillX),
-        fill([68], fillY)
+    return tf21.tidy(() => {
+      const createInterleavedTensor = (fillX, fillY) => tf21.stack([
+        tf21.fill([68], fillX),
+        tf21.fill([68], fillY)
       ], 1).as2D(1, 136).as1D();
       const getPadding = (batchIdx, cond) => {
         const {width, height} = inputDimensions[batchIdx];
@@ -22173,12 +2303,12 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
       const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
       const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      const landmarkTensors = output.mul(fill([batchSize, 136], inputSize)).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+      const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize)).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
       return landmarkTensors;
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tf21.tidy(() => {
       const out = this.runNet(input);
       return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
     });
@@ -22188,7 +2318,7 @@ class FaceLandmark68NetBase extends FaceProcessor {
   }
   async detectLandmarks(input) {
     const netInput = await toNetInput(input);
-    const landmarkTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const landmarkTensors = tf21.tidy(() => tf21.unstack(this.forwardInput(netInput)));
     const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => {
       const landmarksArray = Array.from(await landmarkTensor.data());
       const xCoords = landmarksArray.filter((_, i) => isEven(i));
@@ -22250,6 +2380,7 @@ function extractParamsTiny(weights) {
 }
 
 // build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+const tf22 = __toModule(require("@tensorflow/tfjs"));
 class TinyFaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("TinyFaceFeatureExtractor");
@@ -22259,14 +2390,14 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyFaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tf22.tidy(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+      const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255));
       let out = denseBlock3(normalized, params.dense0, true);
       out = denseBlock3(out, params.dense1);
       out = denseBlock3(out, params.dense2);
-      out = avgPool(out, [14, 14], [2, 2], "valid");
+      out = tf22.avgPool(out, [14, 14], [2, 2], "valid");
       return out;
     });
   }
@@ -22302,17 +2433,19 @@ class FaceLandmarkNet extends FaceLandmark68Net {
 }
 
 // build/src/faceRecognitionNet/scaleLayer.js
+const tf23 = __toModule(require("@tensorflow/tfjs"));
 function scale(x, params) {
-  return add2(mul(x, params.weights), params.biases);
+  return tf23.add(tf23.mul(x, params.weights), params.biases);
 }
 
 // build/src/faceRecognitionNet/convLayer.js
+const tf24 = __toModule(require("@tensorflow/tfjs"));
 function convLayer2(x, params, strides, withRelu, padding = "same") {
   const {filters, bias} = params.conv;
-  let out = conv2d(x, filters, strides, padding);
-  out = add2(out, bias);
+  let out = tf24.conv2d(x, filters, strides, padding);
+  out = tf24.add(out, bias);
   out = scale(out, params.scale);
-  return withRelu ? relu(out) : out;
+  return withRelu ? tf24.relu(out) : out;
 }
 function conv2(x, params) {
   return convLayer2(x, params, [1, 1], true);
@@ -22325,6 +2458,7 @@ function convDown(x, params) {
 }
 
 // build/src/faceRecognitionNet/extractParams.js
+const tf25 = __toModule(require("@tensorflow/tfjs"));
 function extractorsFactory5(extractWeights, paramMappings) {
   function extractFilterValues(numFilterValues, numFilters, filterSize) {
     const weights = extractWeights(numFilterValues);
@@ -22332,17 +2466,17 @@ function extractorsFactory5(extractWeights, paramMappings) {
     if (isFloat(depth)) {
       throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
     }
-    return tidy(() => transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
+    return tf25.tidy(() => tf25.transpose(tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
   }
   function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
     const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-    const bias = tensor1d(extractWeights(numFilters));
+    const bias = tf25.tensor1d(extractWeights(numFilters));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
     return {filters, bias};
   }
   function extractScaleLayerParams(numWeights, mappedPrefix) {
-    const weights = tensor1d(extractWeights(numWeights));
-    const biases = tensor1d(extractWeights(numWeights));
+    const weights = tf25.tensor1d(extractWeights(numWeights));
+    const biases = tf25.tensor1d(extractWeights(numWeights));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
     return {
       weights,
@@ -22383,7 +2517,7 @@ function extractParams9(weights) {
   const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1");
   const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2");
   const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out");
-  const fc = tidy(() => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
+  const fc = tf25.tidy(() => tf25.transpose(tf25.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
   paramMappings.push({paramPath: `fc`});
   if (getRemainingWeights().length !== 0) {
     throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
@@ -22480,37 +2614,39 @@ function extractParamsFromWeigthMap9(weightMap) {
 }
 
 // build/src/faceRecognitionNet/residualLayer.js
+const tf26 = __toModule(require("@tensorflow/tfjs"));
 function residual(x, params) {
   let out = conv2(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  out = add2(out, x);
-  out = relu(out);
+  out = tf26.add(out, x);
+  out = tf26.relu(out);
   return out;
 }
 function residualDown(x, params) {
   let out = convDown(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  let pooled = avgPool(x, 2, 2, "valid");
-  const zeros9 = zeros(pooled.shape);
+  let pooled = tf26.avgPool(x, 2, 2, "valid");
+  const zeros2 = tf26.zeros(pooled.shape);
   const isPad = pooled.shape[3] !== out.shape[3];
   const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
   if (isAdjustShape) {
     const padShapeX = [...out.shape];
     padShapeX[1] = 1;
-    const zerosW = zeros(padShapeX);
-    out = concat([out, zerosW], 1);
+    const zerosW = tf26.zeros(padShapeX);
+    out = tf26.concat([out, zerosW], 1);
     const padShapeY = [...out.shape];
     padShapeY[2] = 1;
-    const zerosH = zeros(padShapeY);
-    out = concat([out, zerosH], 2);
+    const zerosH = tf26.zeros(padShapeY);
+    out = tf26.concat([out, zerosH], 2);
   }
-  pooled = isPad ? concat([pooled, zeros9], 3) : pooled;
-  out = add2(pooled, out);
-  out = relu(out);
+  pooled = isPad ? tf26.concat([pooled, zeros2], 3) : pooled;
+  out = tf26.add(pooled, out);
+  out = tf26.relu(out);
   return out;
 }
 
 // build/src/faceRecognitionNet/FaceRecognitionNet.js
+const tf27 = __toModule(require("@tensorflow/tfjs"));
 class FaceRecognitionNet extends NeuralNetwork {
   constructor() {
     super("FaceRecognitionNet");
@@ -22520,12 +2656,12 @@ class FaceRecognitionNet extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceRecognitionNet - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(150, true), "float32");
+    return tf27.tidy(() => {
+      const batchTensor = tf27.cast(input.toBatchTensor(150, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
+      const normalized = normalize(batchTensor, meanRgb).div(tf27.scalar(256));
       let out = convDown(normalized, params.conv32_down);
-      out = maxPool(out, 3, 2, "valid");
+      out = tf27.maxPool(out, 3, 2, "valid");
       out = residual(out, params.conv32_1);
       out = residual(out, params.conv32_2);
       out = residual(out, params.conv32_3);
@@ -22541,7 +2677,7 @@ class FaceRecognitionNet extends NeuralNetwork {
       out = residual(out, params.conv256_2);
       out = residualDown(out, params.conv256_down_out);
       const globalAvg = out.mean([1, 2]);
-      const fullyConnected = matMul(globalAvg, params.fc);
+      const fullyConnected = tf27.matMul(globalAvg, params.fc);
       return fullyConnected;
     });
   }
@@ -22550,7 +2686,7 @@ class FaceRecognitionNet extends NeuralNetwork {
   }
   async computeFaceDescriptor(input) {
     const netInput = await toNetInput(input);
-    const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const faceDescriptorTensors = tf27.tidy(() => tf27.unstack(this.forwardInput(netInput)));
     const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data()));
     faceDescriptorTensors.forEach((t) => t.dispose());
     return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];
@@ -22600,13 +2736,14 @@ function extendWithGender(sourceObj, gender, genderProbability) {
 // build/src/factories/index.js
 
 // build/src/ssdMobilenetv1/extractParams.js
+const tf28 = __toModule(require("@tensorflow/tfjs"));
 function extractorsFactory7(extractWeights, paramMappings) {
   function extractDepthwiseConvParams(numChannels, mappedPrefix) {
-    const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
-    const batch_norm_scale = tensor1d(extractWeights(numChannels));
-    const batch_norm_offset = tensor1d(extractWeights(numChannels));
-    const batch_norm_mean = tensor1d(extractWeights(numChannels));
-    const batch_norm_variance = tensor1d(extractWeights(numChannels));
+    const filters = tf28.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+    const batch_norm_scale = tf28.tensor1d(extractWeights(numChannels));
+    const batch_norm_offset = tf28.tensor1d(extractWeights(numChannels));
+    const batch_norm_mean = tf28.tensor1d(extractWeights(numChannels));
+    const batch_norm_variance = tf28.tensor1d(extractWeights(numChannels));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`});
     return {
       filters,
@@ -22617,8 +2754,8 @@ function extractorsFactory7(extractWeights, paramMappings) {
     };
   }
   function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-    const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const filters = tf28.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+    const bias = tf28.tensor1d(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
     return {filters, bias};
   }
@@ -22739,7 +2876,7 @@ function extractParams11(weights) {
   const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory7(extractWeights, paramMappings);
   const mobilenetv1 = extractMobilenetV1Params();
   const prediction_layer = extractPredictionLayerParams();
-  const extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
+  const extra_dim = tf28.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
   const output_layer = {
     extra_dim
   };
@@ -22857,28 +2994,30 @@ function extractParamsFromWeigthMap11(weightMap) {
 }
 
 // build/src/ssdMobilenetv1/pointwiseConvLayer.js
+const tf29 = __toModule(require("@tensorflow/tfjs"));
 function pointwiseConvLayer(x, params, strides) {
-  return tidy(() => {
-    let out = conv2d(x, params.filters, strides, "same");
-    out = add2(out, params.batch_norm_offset);
-    return clipByValue(out, 0, 6);
+  return tf29.tidy(() => {
+    let out = tf29.conv2d(x, params.filters, strides, "same");
+    out = tf29.add(out, params.batch_norm_offset);
+    return tf29.clipByValue(out, 0, 6);
   });
 }
 
 // build/src/ssdMobilenetv1/mobileNetV1.js
+const tf30 = __toModule(require("@tensorflow/tfjs"));
 const epsilon = 0.0010000000474974513;
 function depthwiseConvLayer(x, params, strides) {
-  return tidy(() => {
-    let out = depthwiseConv2d(x, params.filters, strides, "same");
-    out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
-    return clipByValue(out, 0, 6);
+  return tf30.tidy(() => {
+    let out = tf30.depthwiseConv2d(x, params.filters, strides, "same");
+    out = tf30.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
+    return tf30.clipByValue(out, 0, 6);
   });
 }
 function getStridesForLayerIdx(layerIdx) {
   return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];
 }
 function mobileNetV1(x, params) {
-  return tidy(() => {
+  return tf30.tidy(() => {
     let conv11;
     let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
     const convPairParams = [
@@ -22916,7 +3055,7 @@ function mobileNetV1(x, params) {
 }
 
 // build/src/ssdMobilenetv1/nonMaxSuppression.js
-function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
   const numBoxes = boxes.shape[0];
   const outputSize = Math.min(maxOutputSize, numBoxes);
   const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score);
@@ -22967,15 +3106,16 @@ function IOU(boxes, i, j) {
 }
 
 // build/src/ssdMobilenetv1/outputLayer.js
+const tf31 = __toModule(require("@tensorflow/tfjs"));
 function getCenterCoordinatesAndSizesLayer(x) {
-  const vec = unstack(transpose(x, [1, 0]));
+  const vec = tf31.unstack(tf31.transpose(x, [1, 0]));
   const sizes = [
-    sub(vec[2], vec[0]),
-    sub(vec[3], vec[1])
+    tf31.sub(vec[2], vec[0]),
+    tf31.sub(vec[3], vec[1])
   ];
   const centers = [
-    add2(vec[0], div(sizes[0], scalar(2))),
-    add2(vec[1], div(sizes[1], scalar(2)))
+    tf31.add(vec[0], tf31.div(sizes[0], tf31.scalar(2))),
+    tf31.add(vec[1], tf31.div(sizes[1], tf31.scalar(2)))
   ];
   return {
     sizes,
@@ -22984,28 +3124,28 @@ function getCenterCoordinatesAndSizesLayer(x) {
 }
 function decodeBoxesLayer(x0, x1) {
   const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-  const vec = unstack(transpose(x1, [1, 0]));
-  const div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
-  const add0_out = add2(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
-  const div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
-  const add1_out = add2(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
-  return transpose(stack([
-    sub(add0_out, div0_out),
-    sub(add1_out, div1_out),
-    add2(add0_out, div0_out),
-    add2(add1_out, div1_out)
+  const vec = tf31.unstack(tf31.transpose(x1, [1, 0]));
+  const div0_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2], tf31.scalar(5))), sizes[0]), tf31.scalar(2));
+  const add0_out = tf31.add(tf31.mul(tf31.div(vec[0], tf31.scalar(10)), sizes[0]), centers[0]);
+  const div1_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3], tf31.scalar(5))), sizes[1]), tf31.scalar(2));
+  const add1_out = tf31.add(tf31.mul(tf31.div(vec[1], tf31.scalar(10)), sizes[1]), centers[1]);
+  return tf31.transpose(tf31.stack([
+    tf31.sub(add0_out, div0_out),
+    tf31.sub(add1_out, div1_out),
+    tf31.add(add0_out, div0_out),
+    tf31.add(add1_out, div1_out)
   ]), [1, 0]);
 }
 function outputLayer(boxPredictions, classPredictions, params) {
-  return tidy(() => {
+  return tf31.tidy(() => {
     const batchSize = boxPredictions.shape[0];
-    let boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
-    boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
-    const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
-    let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
-    scores = reshape(scores, [batchSize, scores.shape[1]]);
-    const boxesByBatch = unstack(boxes);
-    const scoresByBatch = unstack(scores);
+    let boxes = decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf31.reshape(boxPredictions, [-1, 4]));
+    boxes = tf31.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
+    const scoresAndClasses = tf31.sigmoid(tf31.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+    let scores = tf31.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
+    scores = tf31.reshape(scores, [batchSize, scores.shape[1]]);
+    const boxesByBatch = tf31.unstack(boxes);
+    const scoresByBatch = tf31.unstack(scores);
     return {
       boxes: boxesByBatch,
       scores: scoresByBatch
@@ -23014,11 +3154,12 @@ function outputLayer(boxPredictions, classPredictions, params) {
 }
 
 // build/src/ssdMobilenetv1/boxPredictionLayer.js
+const tf32 = __toModule(require("@tensorflow/tfjs"));
 function boxPredictionLayer(x, params) {
-  return tidy(() => {
+  return tf32.tidy(() => {
     const batchSize = x.shape[0];
-    const boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-    const classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+    const boxPredictionEncoding = tf32.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+    const classPrediction = tf32.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
     return {
       boxPredictionEncoding,
       classPrediction
@@ -23027,8 +3168,9 @@ function boxPredictionLayer(x, params) {
 }
 
 // build/src/ssdMobilenetv1/predictionLayer.js
+const tf33 = __toModule(require("@tensorflow/tfjs"));
 function predictionLayer(x, conv11, params) {
-  return tidy(() => {
+  return tf33.tidy(() => {
     const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
     const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
     const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
@@ -23043,7 +3185,7 @@ function predictionLayer(x, conv11, params) {
     const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
     const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
     const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
-    const boxPredictions = concat([
+    const boxPredictions = tf33.concat([
       boxPrediction0.boxPredictionEncoding,
       boxPrediction1.boxPredictionEncoding,
       boxPrediction2.boxPredictionEncoding,
@@ -23051,7 +3193,7 @@ function predictionLayer(x, conv11, params) {
       boxPrediction4.boxPredictionEncoding,
       boxPrediction5.boxPredictionEncoding
     ], 1);
-    const classPredictions = concat([
+    const classPredictions = tf33.concat([
       boxPrediction0.classPrediction,
       boxPrediction1.classPrediction,
       boxPrediction2.classPrediction,
@@ -23088,6 +3230,7 @@ class SsdMobilenetv1Options {
 }
 
 // build/src/ssdMobilenetv1/SsdMobilenetv1.js
+const tf34 = __toModule(require("@tensorflow/tfjs"));
 class SsdMobilenetv1 extends NeuralNetwork {
   constructor() {
     super("SsdMobilenetv1");
@@ -23097,9 +3240,9 @@ class SsdMobilenetv1 extends NeuralNetwork {
     if (!params) {
       throw new Error("SsdMobilenetv1 - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(512, false), "float32");
-      const x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
+    return tf34.tidy(() => {
+      const batchTensor = tf34.cast(input.toBatchTensor(512, false), "float32");
+      const x = tf34.sub(tf34.mul(batchTensor, tf34.scalar(0.007843137718737125)), tf34.scalar(1));
       const features = mobileNetV1(x, params.mobilenetv1);
       const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer);
       return outputLayer(boxPredictions, classPredictions, params.output_layer);
@@ -23120,7 +3263,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
     }
     const scoresData = Array.from(await scores.data());
     const iouThreshold = 0.5;
-    const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+    const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
     const reshapedDims = netInput.getReshapedInputDimensions(0);
     const inputSize = netInput.inputSize;
     const padX = inputSize / reshapedDims.width;
@@ -23188,7 +3331,7 @@ const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
 const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
 
 // build/src/tinyYolov2/config.js
-const isNumber2 = (arg) => typeof arg === "number";
+const isNumber = (arg) => typeof arg === "number";
 function validateConfig(config2) {
   if (!config2) {
     throw new Error(`invalid config: ${config2}`);
@@ -23196,58 +3339,62 @@ function validateConfig(config2) {
   if (typeof config2.withSeparableConvs !== "boolean") {
     throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
   }
-  if (!isNumber2(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
+  if (!isNumber(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
     throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config2.iouThreshold}`);
   }
   if (!Array.isArray(config2.classes) || !config2.classes.length || !config2.classes.every((c) => typeof c === "string")) {
     throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config2.classes)}`);
   }
-  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) {
+  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) {
     throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config2.anchors)}`);
   }
-  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber2))) {
+  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber))) {
     throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
   }
 }
 
 // build/src/tinyYolov2/leaky.js
+const tf35 = __toModule(require("@tensorflow/tfjs"));
 function leaky(x) {
-  return tidy(() => {
-    const min4 = mul(x, scalar(0.10000000149011612));
-    return add2(relu(sub(x, min4)), min4);
+  return tf35.tidy(() => {
+    const min = tf35.mul(x, tf35.scalar(0.10000000149011612));
+    return tf35.add(tf35.relu(tf35.sub(x, min)), min);
   });
 }
 
 // build/src/tinyYolov2/convWithBatchNorm.js
+const tf36 = __toModule(require("@tensorflow/tfjs"));
 function convWithBatchNorm(x, params) {
-  return tidy(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = conv2d(out, params.conv.filters, [1, 1], "valid");
-    out = sub(out, params.bn.sub);
-    out = mul(out, params.bn.truediv);
-    out = add2(out, params.conv.bias);
+  return tf36.tidy(() => {
+    let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+    out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid");
+    out = tf36.sub(out, params.bn.sub);
+    out = tf36.mul(out, params.bn.truediv);
+    out = tf36.add(out, params.conv.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/depthwiseSeparableConv.js
+const tf37 = __toModule(require("@tensorflow/tfjs"));
 function depthwiseSeparableConv3(x, params) {
-  return tidy(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = add2(out, params.bias);
+  return tf37.tidy(() => {
+    let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+    out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
+    out = tf37.add(out, params.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/extractParams.js
+const tf38 = __toModule(require("@tensorflow/tfjs"));
 function extractorsFactory9(extractWeights, paramMappings) {
   const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
   function extractBatchNormParams(size, mappedPrefix) {
-    const sub32 = tensor1d(extractWeights(size));
-    const truediv = tensor1d(extractWeights(size));
+    const sub6 = tf38.tensor1d(extractWeights(size));
+    const truediv = tf38.tensor1d(extractWeights(size));
     paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
     const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
@@ -23301,9 +3448,9 @@ function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
 function extractorsFactory10(weightMap, paramMappings) {
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
   function extractBatchNormParams(prefix) {
-    const sub32 = extractWeightEntry(`${prefix}/sub`, 1);
+    const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
     const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvParams(prefix) {
     const filters = extractWeightEntry(`${prefix}/filters`, 4);
@@ -23385,6 +3532,7 @@ class TinyYolov2Options {
 }
 
 // build/src/tinyYolov2/TinyYolov2Base.js
+const tf39 = __toModule(require("@tensorflow/tfjs"));
 class TinyYolov2Base extends NeuralNetwork {
   constructor(config2) {
     super("TinyYolov2");
@@ -23402,34 +3550,34 @@ class TinyYolov2Base extends NeuralNetwork {
   }
   runTinyYolov2(x, params) {
     let out = convWithBatchNorm(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = tf39.maxPool(out, [2, 2], [1, 1], "same");
     out = convWithBatchNorm(out, params.conv6);
     out = convWithBatchNorm(out, params.conv7);
     return convLayer(out, params.conv8, "valid", false);
   }
   runMobilenet(x, params) {
     let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv3(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = tf39.maxPool(out, [2, 2], [1, 1], "same");
     out = params.conv6 ? depthwiseSeparableConv3(out, params.conv6) : out;
     out = params.conv7 ? depthwiseSeparableConv3(out, params.conv7) : out;
     return convLayer(out, params.conv8, "valid", false);
@@ -23439,10 +3587,10 @@ class TinyYolov2Base extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyYolov2 - load model before inference");
     }
-    return tidy(() => {
-      let batchTensor = cast(input.toBatchTensor(inputSize, false), "float32");
+    return tf39.tidy(() => {
+      let batchTensor = tf39.cast(input.toBatchTensor(inputSize, false), "float32");
       batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
-      batchTensor = batchTensor.div(scalar(256));
+      batchTensor = batchTensor.div(tf39.scalar(256));
       return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
     });
   }
@@ -23453,7 +3601,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput, inputSize);
-    const out0 = tidy(() => unstack(out)[0].expandDims());
+    const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims());
     const inputDimensions = {
       width: netInput.getInputWidth(0),
       height: netInput.getInputHeight(0)
@@ -23465,7 +3613,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const scores = results.map((res) => res.score);
     const classScores = results.map((res) => res.classScore);
     const classNames = results.map((res) => this.config.classes[res.label]);
-    const indices = nonMaxSuppression2(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
+    const indices = nonMaxSuppression(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
     const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions));
     return detections;
   }
@@ -23490,11 +3638,11 @@ class TinyYolov2Base extends NeuralNetwork {
     const correctionFactorY = inputSize / height;
     const numCells = outputTensor.shape[1];
     const numBoxes = this.config.anchors.length;
-    const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => {
+    const [boxesTensor, scoresTensor, classScoresTensor] = tf39.tidy(() => {
       const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);
       const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
       const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
-      const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0);
+      const classScores = this.withClassScores ? tf39.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tf39.scalar(0);
       return [boxes, scores, classScores];
     });
     const results = [];
@@ -23503,22 +3651,23 @@ class TinyYolov2Base extends NeuralNetwork {
     for (let row = 0; row < numCells; row++) {
       for (let col = 0; col < numCells; col++) {
         for (let anchor = 0; anchor < numBoxes; anchor++) {
-          const score = sigmoid6(scoresData[row][col][anchor][0]);
+          const score = sigmoid(scoresData[row][col][anchor][0]);
           if (!scoreThreshold || score > scoreThreshold) {
-            const ctX = (col + sigmoid6(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
-            const ctY = (row + sigmoid6(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
+            const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
+            const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
             const width2 = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX;
             const height2 = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY;
             const x = ctX - width2 / 2;
             const y = ctY - height2 / 2;
             const pos = {row, col, anchor};
             const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0};
-            results.push(__assign({
+            results.push({
               box: new BoundingBox(x, y, x + width2, y + height2),
               score,
               classScore: score * classScore,
-              label
-            }, pos));
+              label,
+              ...pos
+            });
           }
         }
       }
@@ -23534,7 +3683,7 @@ class TinyYolov2Base extends NeuralNetwork {
     return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
       classScore,
       label
-    })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr);
+    })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
   }
 }
 TinyYolov2Base.DEFAULT_FILTER_SIZES = [
@@ -23611,11 +3760,12 @@ class ComposableTask {
 }
 
 // build/src/globalApi/extractFacesAndComputeResults.js
+const tf40 = __toModule(require("@tensorflow/tfjs"));
 async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) {
   const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection);
-  const faces = extractedFaces || (input instanceof Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
+  const faces = extractedFaces || (input instanceof tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
   const results = await computeResults(faces);
-  faces.forEach((f) => f instanceof Tensor && f.dispose());
+  faces.forEach((f) => f instanceof tf40.Tensor && f.dispose());
   return results;
 }
 async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
@@ -23833,6 +3983,7 @@ class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
 }
 
 // build/src/globalApi/DetectFaceLandmarksTasks.js
+const tf41 = __toModule(require("@tensorflow/tfjs"));
 class DetectFaceLandmarksTaskBase extends ComposableTask {
   constructor(parentTask, input, useTinyLandmarkNet) {
     super();
@@ -23848,9 +3999,9 @@ class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
   async run() {
     const parentResults = await this.parentTask;
     const detections = parentResults.map((res) => res.detection);
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
+    const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
     const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face)));
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof tf41.Tensor && f.dispose());
     return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
   }
   withFaceExpressions() {
@@ -23870,9 +4021,9 @@ class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
       return;
     }
     const {detection} = parentResult;
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
+    const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
     const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof tf41.Tensor && f.dispose());
     return extendWithFaceLandmarks(parentResult, landmarks);
   }
   withFaceExpressions() {
@@ -24061,7 +4212,7 @@ function resizeResults(results, dimensions) {
 }
 
 // build/package.json
-var version2 = "0.5.2";
+var version = "0.5.3";
 
 // build/src/index.js
 __export(exports, {
@@ -24128,7 +4279,7 @@ __export(exports, {
   detectLandmarks: () => detectLandmarks,
   detectSingleFace: () => detectSingleFace,
   draw: () => draw_exports,
-  env: () => env2,
+  env: () => env,
   euclideanDistance: () => euclideanDistance,
   extendWithAge: () => extendWithAge,
   extendWithFaceDescriptor: () => extendWithFaceDescriptor,
@@ -24169,7 +4320,7 @@ __export(exports, {
   matchDimensions: () => matchDimensions,
   minBbox: () => minBbox,
   nets: () => nets,
-  nonMaxSuppression: () => nonMaxSuppression2,
+  nonMaxSuppression: () => nonMaxSuppression,
   normalize: () => normalize,
   padToSquare: () => padToSquare,
   predictAgeAndGender: () => predictAgeAndGender,
@@ -24177,17 +4328,18 @@ __export(exports, {
   resizeResults: () => resizeResults,
   resolveInput: () => resolveInput,
   shuffleArray: () => shuffleArray,
-  sigmoid: () => sigmoid6,
+  sigmoid: () => sigmoid,
   ssdMobilenetv1: () => ssdMobilenetv1,
-  tf: () => dist_exports,
+  tf: () => tf42,
   tinyFaceDetector: () => tinyFaceDetector,
   tinyYolov2: () => tinyYolov23,
   toNetInput: () => toNetInput,
   utils: () => utils_exports,
   validateConfig: () => validateConfig,
-  version: () => version3
+  version: () => version2
 });
+const tf42 = __toModule(require("@tensorflow/tfjs"));
 const node = typeof process !== "undefined" ? process.version : false;
-const browser = typeof navigator !== "undefined" ? navigator.userAgent : false;
-const version3 = {faceapi: version2, node, browser, tfjs: version, backend: getBackend()};
+const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
+const version2 = {faceapi: version, node, browser: browser3};
 //# sourceMappingURL=face-api.cjs.js.map
diff --git a/dist/face-api.cjs.js.map b/dist/face-api.cjs.js.map
index d519cff..8ff9d11 100644
--- a/dist/face-api.cjs.js.map
+++ b/dist/face-api.cjs.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["node_modules/seedrandom/lib/alea.js", "node_modules/seedrandom/lib/xor128.js", "node_modules/seedrandom/lib/xorwow.js", "node_modules/seedrandom/lib/xorshift7.js", "node_modules/seedrandom/lib/xor4096.js", "node_modules/seedrandom/lib/tychei.js", "disabled:crypto", "node_modules/seedrandom/seedrandom.js", "node_modules/seedrandom/index.js", "build/src/env/../../../src/env/isNodejs.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/environment.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/global_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_names.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/profiler.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tape.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_format.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/types.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/engine.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/device_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/flags.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util_env.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/operation.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/complex.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor_ops_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/types.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io_utils.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/router_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/model_management.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/buffer.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cast.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clone.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/print.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/base_side_effects.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/browser_files.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/progress.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/weights_loader.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/http.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/passthrough.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reshape.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mat_mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/one_hot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/confusion_matrix.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/math.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/browser.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/serialization.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/test_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/version.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/globals.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floorDiv.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/abs.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add_n.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/axis_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/all.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/any.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan2.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/basic_lstm_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batch_to_space_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_to.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ceil.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clip_by_value.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cumsum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depth_to_space.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depthwise_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/diag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dilation2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div_no_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/elu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/erf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expand_dims.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expm1.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tile.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/eye.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/fill.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reduce_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/segment_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/imag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_finite.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_inf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/maximum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scalar.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/leaky_relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/linspace.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/local_response_normalization.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log1p.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/gradients.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/neg.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softplus.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sub.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sum_exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_and.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_not.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_or.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_xor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_with_argmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mean.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/minimum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mod.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/square.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/moments.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multi_rnn_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multinomial.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/not_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/real.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/outer_product.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad3d.ts", 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-  "sourcesContent": ["// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],
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   "names": []
 }
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index 7429a85..5853e94 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -1,6 +1,5 @@
 var __defineProperty = Object.defineProperty;
 var __hasOwnProperty = Object.prototype.hasOwnProperty;
-var __assign = Object.assign;
 var __commonJS = (callback, module) => () => {
   if (!module) {
     module = {exports: {}};
@@ -11,10 +10,10 @@ var __commonJS = (callback, module) => () => {
 var __markAsModule = (target) => {
   return __defineProperty(target, "__esModule", {value: true});
 };
-var __export = (target, all3) => {
+var __export = (target, all) => {
   __markAsModule(target);
-  for (var name in all3)
-    __defineProperty(target, name, {get: all3[name], enumerable: true});
+  for (var name in all)
+    __defineProperty(target, name, {get: all[name], enumerable: true});
 };
 var __exportStar = (target, module) => {
   __markAsModule(target);
@@ -31,646 +30,6 @@ var __toModule = (module) => {
   return __exportStar(__defineProperty({}, "default", {value: module, enumerable: true}), module);
 };
 
-// node_modules/seedrandom/lib/alea.js
-var require_alea = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function Alea(seed) {
-      var me = this, mash = Mash();
-      me.next = function() {
-        var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
-        me.s0 = me.s1;
-        me.s1 = me.s2;
-        return me.s2 = t - (me.c = t | 0);
-      };
-      me.c = 1;
-      me.s0 = mash(" ");
-      me.s1 = mash(" ");
-      me.s2 = mash(" ");
-      me.s0 -= mash(seed);
-      if (me.s0 < 0) {
-        me.s0 += 1;
-      }
-      me.s1 -= mash(seed);
-      if (me.s1 < 0) {
-        me.s1 += 1;
-      }
-      me.s2 -= mash(seed);
-      if (me.s2 < 0) {
-        me.s2 += 1;
-      }
-      mash = null;
-    }
-    function copy(f, t) {
-      t.c = f.c;
-      t.s0 = f.s0;
-      t.s1 = f.s1;
-      t.s2 = f.s2;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
-      prng.int32 = function() {
-        return xg.next() * 4294967296 | 0;
-      };
-      prng.double = function() {
-        return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
-      };
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    function Mash() {
-      var n = 4022871197;
-      var mash = function(data) {
-        data = data.toString();
-        for (var i = 0; i < data.length; i++) {
-          n += data.charCodeAt(i);
-          var h = 0.02519603282416938 * n;
-          n = h >>> 0;
-          h -= n;
-          h *= n;
-          n = h >>> 0;
-          h -= n;
-          n += h * 4294967296;
-        }
-        return (n >>> 0) * 23283064365386963e-26;
-      };
-      return mash;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.alea = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor128.js
-var require_xor128 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.next = function() {
-        var t = me.x ^ me.x << 11;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
-      };
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor128 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorwow.js
-var require_xorwow = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var t = me.x ^ me.x >>> 2;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        me.w = me.v;
-        return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
-      };
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.v = 0;
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        if (k == strseed.length) {
-          me.d = me.x << 10 ^ me.x >>> 4;
-        }
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      t.v = f.v;
-      t.d = f.d;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorwow = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorshift7.js
-var require_xorshift7 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var X = me.x, i = me.i, t, v, w;
-        t = X[i];
-        t ^= t >>> 7;
-        v = t ^ t << 24;
-        t = X[i + 1 & 7];
-        v ^= t ^ t >>> 10;
-        t = X[i + 3 & 7];
-        v ^= t ^ t >>> 3;
-        t = X[i + 4 & 7];
-        v ^= t ^ t << 7;
-        t = X[i + 7 & 7];
-        t = t ^ t << 13;
-        v ^= t ^ t << 9;
-        X[i] = v;
-        me.i = i + 1 & 7;
-        return v;
-      };
-      function init(me2, seed2) {
-        var j, w, X = [];
-        if (seed2 === (seed2 | 0)) {
-          w = X[0] = seed2;
-        } else {
-          seed2 = "" + seed2;
-          for (j = 0; j < seed2.length; ++j) {
-            X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
-          }
-        }
-        while (X.length < 8)
-          X.push(0);
-        for (j = 0; j < 8 && X[j] === 0; ++j)
-          ;
-        if (j == 8)
-          w = X[7] = -1;
-        else
-          w = X[j];
-        me2.x = X;
-        me2.i = 0;
-        for (j = 256; j > 0; --j) {
-          me2.next();
-        }
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.x = f.x.slice();
-      t.i = f.i;
-      return t;
-    }
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.x)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorshift7 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor4096.js
-var require_xor4096 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var w = me.w, X = me.X, i = me.i, t, v;
-        me.w = w = w + 1640531527 | 0;
-        v = X[i + 34 & 127];
-        t = X[i = i + 1 & 127];
-        v ^= v << 13;
-        t ^= t << 17;
-        v ^= v >>> 15;
-        t ^= t >>> 12;
-        v = X[i] = v ^ t;
-        me.i = i;
-        return v + (w ^ w >>> 16) | 0;
-      };
-      function init(me2, seed2) {
-        var t, v, i, j, w, X = [], limit = 128;
-        if (seed2 === (seed2 | 0)) {
-          v = seed2;
-          seed2 = null;
-        } else {
-          seed2 = seed2 + "\0";
-          v = 0;
-          limit = Math.max(limit, seed2.length);
-        }
-        for (i = 0, j = -32; j < limit; ++j) {
-          if (seed2)
-            v ^= seed2.charCodeAt((j + 32) % seed2.length);
-          if (j === 0)
-            w = v;
-          v ^= v << 10;
-          v ^= v >>> 15;
-          v ^= v << 4;
-          v ^= v >>> 13;
-          if (j >= 0) {
-            w = w + 1640531527 | 0;
-            t = X[j & 127] ^= v + w;
-            i = t == 0 ? i + 1 : 0;
-          }
-        }
-        if (i >= 128) {
-          X[(seed2 && seed2.length || 0) & 127] = -1;
-        }
-        i = 127;
-        for (j = 4 * 128; j > 0; --j) {
-          v = X[i + 34 & 127];
-          t = X[i = i + 1 & 127];
-          v ^= v << 13;
-          t ^= t << 17;
-          v ^= v >>> 15;
-          t ^= t >>> 12;
-          X[i] = v ^ t;
-        }
-        me2.w = w;
-        me2.X = X;
-        me2.i = i;
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.w = f.w;
-      t.X = f.X.slice();
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.X)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor4096 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/tychei.js
-var require_tychei = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var b = me.b, c = me.c, d = me.d, a = me.a;
-        b = b << 25 ^ b >>> 7 ^ c;
-        c = c - d | 0;
-        d = d << 24 ^ d >>> 8 ^ a;
-        a = a - b | 0;
-        me.b = b = b << 20 ^ b >>> 12 ^ c;
-        me.c = c = c - d | 0;
-        me.d = d << 16 ^ c >>> 16 ^ a;
-        return me.a = a - b | 0;
-      };
-      me.a = 0;
-      me.b = 0;
-      me.c = 2654435769 | 0;
-      me.d = 1367130551;
-      if (seed === Math.floor(seed)) {
-        me.a = seed / 4294967296 | 0;
-        me.b = seed | 0;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 20; k++) {
-        me.b ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.a = f.a;
-      t.b = f.b;
-      t.c = f.c;
-      t.d = f.d;
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.tychei = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// disabled:crypto
-var require_disabled_crypto = __commonJS(() => {
-});
-
-// node_modules/seedrandom/seedrandom.js
-var require_seedrandom = __commonJS((exports, module) => {
-  (function(pool3, math) {
-    var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
-    function seedrandom2(seed, options, callback) {
-      var key = [];
-      options = options == true ? {entropy: true} : options || {};
-      var shortseed = mixkey(flatten2(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
-      var arc4 = new ARC4(key);
-      var prng = function() {
-        var n = arc4.g(chunks), d = startdenom, x = 0;
-        while (n < significance) {
-          n = (n + x) * width;
-          d *= width;
-          x = arc4.g(1);
-        }
-        while (n >= overflow) {
-          n /= 2;
-          d /= 2;
-          x >>>= 1;
-        }
-        return (n + x) / d;
-      };
-      prng.int32 = function() {
-        return arc4.g(4) | 0;
-      };
-      prng.quick = function() {
-        return arc4.g(4) / 4294967296;
-      };
-      prng.double = prng;
-      mixkey(tostring(arc4.S), pool3);
-      return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
-        if (state) {
-          if (state.S) {
-            copy(state, arc4);
-          }
-          prng2.state = function() {
-            return copy(arc4, {});
-          };
-        }
-        if (is_math_call) {
-          math[rngname] = prng2;
-          return seed2;
-        } else
-          return prng2;
-      })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
-    }
-    math["seed" + rngname] = seedrandom2;
-    function ARC4(key) {
-      var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
-      if (!keylen) {
-        key = [keylen++];
-      }
-      while (i < width) {
-        s[i] = i++;
-      }
-      for (i = 0; i < width; i++) {
-        s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
-        s[j] = t;
-      }
-      (me.g = function(count) {
-        var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
-        while (count--) {
-          t2 = s2[i2 = mask & i2 + 1];
-          r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
-        }
-        me.i = i2;
-        me.j = j2;
-        return r;
-      })(width);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.j = f.j;
-      t.S = f.S.slice();
-      return t;
-    }
-    ;
-    function flatten2(obj, depth) {
-      var result = [], typ = typeof obj, prop;
-      if (depth && typ == "object") {
-        for (prop in obj) {
-          try {
-            result.push(flatten2(obj[prop], depth - 1));
-          } catch (e) {
-          }
-        }
-      }
-      return result.length ? result : typ == "string" ? obj : obj + "\0";
-    }
-    function mixkey(seed, key) {
-      var stringseed = seed + "", smear, j = 0;
-      while (j < stringseed.length) {
-        key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
-      }
-      return tostring(key);
-    }
-    function autoseed() {
-      try {
-        var out;
-        if (nodecrypto && (out = nodecrypto.randomBytes)) {
-          out = out(width);
-        } else {
-          out = new Uint8Array(width);
-          (global2.crypto || global2.msCrypto).getRandomValues(out);
-        }
-        return tostring(out);
-      } catch (e) {
-        var browser2 = global2.navigator, plugins = browser2 && browser2.plugins;
-        return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
-      }
-    }
-    function tostring(a) {
-      return String.fromCharCode.apply(0, a);
-    }
-    mixkey(math.random(), pool3);
-    if (typeof module == "object" && module.exports) {
-      module.exports = seedrandom2;
-      try {
-        nodecrypto = require_disabled_crypto();
-      } catch (ex) {
-      }
-    } else if (typeof define == "function" && define.amd) {
-      define(function() {
-        return seedrandom2;
-      });
-    }
-  })([], Math);
-});
-
-// node_modules/seedrandom/index.js
-var require_seedrandom2 = __commonJS((exports, module) => {
-  var alea2 = require_alea();
-  var xor128 = require_xor128();
-  var xorwow = require_xorwow();
-  var xorshift7 = require_xorshift7();
-  var xor4096 = require_xor4096();
-  var tychei = require_tychei();
-  var sr = require_seedrandom();
-  sr.alea = alea2;
-  sr.xor128 = xor128;
-  sr.xorwow = xorwow;
-  sr.xorshift7 = xorshift7;
-  sr.xor4096 = xor4096;
-  sr.tychei = tychei;
-  module.exports = sr;
-});
-
 // build/src/env/isNodejs.js
 var require_isNodejs = __commonJS((exports, module) => {
   __export(exports, {
@@ -681,19257 +40,6 @@ var require_isNodejs = __commonJS((exports, module) => {
   }
 });
 
-// node_modules/@tensorflow/tfjs-core/dist/environment.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
-class Environment {
-  constructor(global2) {
-    this.global = global2;
-    this.flags = {};
-    this.flagRegistry = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  setPlatform(platformName, platform) {
-    if (this.platform != null) {
-      console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
-    }
-    this.platformName = platformName;
-    this.platform = platform;
-  }
-  registerFlag(flagName, evaluationFn, setHook) {
-    this.flagRegistry[flagName] = {evaluationFn, setHook};
-    if (this.urlFlags[flagName] != null) {
-      const flagValue = this.urlFlags[flagName];
-      console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
-      this.set(flagName, flagValue);
-    }
-  }
-  async getAsync(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    this.flags[flagName] = await this.evaluateFlag(flagName);
-    return this.flags[flagName];
-  }
-  get(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    const flagValue = this.evaluateFlag(flagName);
-    if (flagValue instanceof Promise) {
-      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
-    }
-    this.flags[flagName] = flagValue;
-    return this.flags[flagName];
-  }
-  getNumber(flagName) {
-    return this.get(flagName);
-  }
-  getBool(flagName) {
-    return this.get(flagName);
-  }
-  getFlags() {
-    return this.flags;
-  }
-  get features() {
-    return this.flags;
-  }
-  set(flagName, value) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
-    }
-    this.flags[flagName] = value;
-    if (this.flagRegistry[flagName].setHook != null) {
-      this.flagRegistry[flagName].setHook(value);
-    }
-  }
-  evaluateFlag(flagName) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
-    }
-    return this.flagRegistry[flagName].evaluationFn();
-  }
-  setFlags(flags3) {
-    this.flags = Object.assign({}, flags3);
-  }
-  reset() {
-    this.flags = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  populateURLFlags() {
-    if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
-      return;
-    }
-    const urlParams = getQueryParams(this.global.location.search);
-    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
-      keyValues.forEach((keyValue) => {
-        const [key, value] = keyValue.split(":");
-        this.urlFlags[key] = parseValue(key, value);
-      });
-    }
-  }
-}
-function getQueryParams(queryString) {
-  const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
-    decodeParam(params, t[0], t[1]);
-    return t.join("=");
-  });
-  return params;
-}
-function decodeParam(params, name, value) {
-  params[decodeURIComponent(name)] = decodeURIComponent(value || "");
-}
-function parseValue(flagName, value) {
-  value = value.toLowerCase();
-  if (value === "true" || value === "false") {
-    return value === "true";
-  } else if (`${+value}` === value) {
-    return +value;
-  }
-  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
-}
-function env() {
-  return ENV;
-}
-let ENV = null;
-function setEnvironmentGlobal(environment12) {
-  ENV = environment12;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/global_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let globalNameSpace;
-function getGlobalNamespace() {
-  if (globalNameSpace == null) {
-    let ns;
-    if (typeof window !== "undefined") {
-      ns = window;
-    } else if (typeof global !== "undefined") {
-      ns = global;
-    } else if (typeof process !== "undefined") {
-      ns = process;
-    } else if (typeof self !== "undefined") {
-      ns = self;
-    } else {
-      throw new Error("Could not find a global object");
-    }
-    globalNameSpace = ns;
-  }
-  return globalNameSpace;
-}
-function getGlobalMap() {
-  const ns = getGlobalNamespace();
-  if (ns._tfGlobals == null) {
-    ns._tfGlobals = new Map();
-  }
-  return ns._tfGlobals;
-}
-function getGlobal(key, init) {
-  const globalMap = getGlobalMap();
-  if (globalMap.has(key)) {
-    return globalMap.get(key);
-  } else {
-    const singleton = init();
-    globalMap.set(key, singleton);
-    return globalMap.get(key);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
-const Abs = "Abs";
-const Acos = "Acos";
-const Acosh = "Acosh";
-const Add = "Add";
-const AddN = "AddN";
-const All = "All";
-const Any = "Any";
-const ArgMax = "ArgMax";
-const ArgMin = "ArgMin";
-const Asin = "Asin";
-const Asinh = "Asinh";
-const Atan = "Atan";
-const Atanh = "Atanh";
-const Atan2 = "Atan2";
-const AvgPool = "AvgPool";
-const AvgPoolBackprop = "AvgPoolBackprop";
-const AvgPool3D = "AvgPool3D";
-const AvgPool3DBackprop = "AvgPool3DBackprop";
-const BatchMatMul = "BatchMatMul";
-const BatchToSpaceND = "BatchToSpaceND";
-const BroadcastTo = "BroadcastTo";
-const Cast = "Cast";
-const Ceil = "Ceil";
-const ClipByValue = "ClipByValue";
-const Complex = "Complex";
-const Concat = "Concat";
-const Conv2D = "Conv2D";
-const Conv2DBackpropFilter = "Conv2DBackpropFilter";
-const Conv2DBackpropInput = "Conv2DBackpropInput";
-const Conv3D = "Conv3D";
-const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
-const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
-const Cos = "Cos";
-const Cosh = "Cosh";
-const Cumsum = "Cumsum";
-const CropAndResize = "CropAndResize";
-const DepthToSpace = "DepthToSpace";
-const DepthwiseConv2dNative = "DepthwiseConv2dNative";
-const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
-const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
-const Diag = "Diag";
-const Dilation2D = "Dilation2D";
-const Dilation2DBackpropInput = "Dilation2DBackpropInput";
-const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
-const Div = "Div";
-const Elu = "Elu";
-const EluGrad = "EluGrad";
-const Erf = "Erf";
-const Equal = "Equal";
-const Exp = "Exp";
-const Expm1 = "Expm1";
-const FFT = "FFT";
-const Fill = "Fill";
-const FlipLeftRight = "FlipLeftRight";
-const Floor = "Floor";
-const FloorDiv = "FloorDiv";
-const FusedBatchNorm = "FusedBatchNorm";
-const GatherV2 = "GatherV2";
-const GatherNd = "GatherNd";
-const Greater = "Greater";
-const GreaterEqual = "GreaterEqual";
-const Identity = "Identity";
-const IFFT = "IFFT";
-const Imag = "Imag";
-const IsFinite = "IsFinite";
-const IsInf = "IsInf";
-const IsNan = "IsNan";
-const Less = "Less";
-const LessEqual = "LessEqual";
-const LinSpace = "LinSpace";
-const Log = "Log";
-const Log1p = "Log1p";
-const LogicalAnd = "LogicalAnd";
-const LogicalNot = "LogicalNot";
-const LogicalOr = "LogicalOr";
-const LogSoftmax = "LogSoftmax";
-const LRN = "LRN";
-const LRNBackprop = "LRNBackprop";
-const Max = "Max";
-const Maximum = "Maximum";
-const MaxPool = "MaxPool";
-const MaxPoolBackprop = "MaxPoolBackprop";
-const MaxPool3D = "MaxPool3D";
-const MaxPool3DBackprop = "MaxPool3DBackprop";
-const MaxPoolWithArgmax = "MaxPoolWithArgmax";
-const Mean = "Mean";
-const Min = "Min";
-const Minimum = "Minimum";
-const Mod = "Mod";
-const Multiply = "Multiply";
-const Negate = "Negate";
-const NotEqual = "NotEqual";
-const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
-const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
-const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
-const OnesLike = "OnesLike";
-const OneHot = "OneHot";
-const PadV2 = "PadV2";
-const Pool = "Pool";
-const Pow = "Pow";
-const Prelu = "Prelu";
-const Prod = "Prod";
-const Range = "Range";
-const Real = "Real";
-const Reciprocal = "Reciprocal";
-const Relu = "Relu";
-const Reshape = "Reshape";
-const ResizeNearestNeighbor = "ResizeNearestNeighbor";
-const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
-const ResizeBilinear = "ResizeBilinear";
-const ResizeBilinearGrad = "ResizeBilinearGrad";
-const Relu6 = "Relu6";
-const Reverse = "Reverse";
-const Round = "Round";
-const Rsqrt = "Rsqrt";
-const ScatterNd = "ScatterNd";
-const SelectV2 = "SelectV2";
-const Selu = "Selu";
-const Slice = "Slice";
-const Sin = "Sin";
-const Sinh = "Sinh";
-const Sign = "Sign";
-const Sigmoid = "Sigmoid";
-const Softplus = "Softplus";
-const Sqrt = "Sqrt";
-const Sum = "Sum";
-const SpaceToBatchND = "SpaceToBatchND";
-const SplitV = "SplitV";
-const Softmax = "Softmax";
-const SquaredDifference = "SquaredDifference";
-const Square = "Square";
-const Sub = "Sub";
-const SparseToDense = "SparseToDense";
-const StridedSlice = "StridedSlice";
-const Tan = "Tan";
-const Tanh = "Tanh";
-const Tile = "Tile";
-const TopK = "TopK";
-const Transpose = "Transpose";
-const Unpack = "Unpack";
-const UnsortedSegmentSum = "UnsortedSegmentSum";
-const ZerosLike = "ZerosLike";
-const Step = "Step";
-const FromPixels = "FromPixels";
-const RotateWithOffset = "RotateWithOffset";
-const _FusedMatMul = "_FusedMatMul";
-const FusedConv2D = "FusedConv2D";
-const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const kernelRegistry = getGlobal("kernelRegistry", () => new Map());
-const gradRegistry = getGlobal("gradRegistry", () => new Map());
-function getKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  return kernelRegistry.get(key);
-}
-function getGradient(kernelName) {
-  return gradRegistry.get(kernelName);
-}
-function getKernelsForBackend(backendName) {
-  const it = kernelRegistry.entries();
-  const result = [];
-  while (true) {
-    const {done, value} = it.next();
-    if (done) {
-      break;
-    }
-    const [key, config2] = value;
-    const [backend2] = key.split("_");
-    if (backend2 === backendName) {
-      result.push(config2);
-    }
-  }
-  return result;
-}
-function registerKernel(config2) {
-  const {kernelName, backendName} = config2;
-  const key = makeKey(kernelName, backendName);
-  if (kernelRegistry.has(key)) {
-    console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
-  }
-  kernelRegistry.set(key, config2);
-}
-function registerGradient(config2) {
-  const {kernelName} = config2;
-  if (gradRegistry.has(kernelName)) {
-    if (env().getBool("DEBUG")) {
-      console.warn(`Overriding the gradient for '${kernelName}'`);
-    }
-  }
-  gradRegistry.set(kernelName, config2);
-}
-function unregisterKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  if (!kernelRegistry.has(key)) {
-    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
-  }
-  kernelRegistry.delete(key);
-}
-function unregisterGradient(kernelName) {
-  if (!gradRegistry.has(kernelName)) {
-    throw new Error(`The gradient '${kernelName}' for backend is not registered`);
-  }
-  gradRegistry.delete(kernelName);
-}
-function makeKey(kernelName, backendName) {
-  return `${backendName}_${kernelName}`;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/util.js
-const util_exports = {};
-__export(util_exports, {
-  arraysEqual: () => arraysEqual,
-  assert: () => assert,
-  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
-  assertNonNull: () => assertNonNull,
-  assertShapesMatch: () => assertShapesMatch,
-  bytesFromStringArray: () => bytesFromStringArray,
-  bytesPerElement: () => bytesPerElement,
-  checkConversionForErrors: () => checkConversionForErrors,
-  clamp: () => clamp,
-  computeStrides: () => computeStrides,
-  createShuffledIndices: () => createShuffledIndices,
-  decodeString: () => decodeString,
-  distSquared: () => distSquared,
-  encodeString: () => encodeString,
-  fetch: () => fetch2,
-  flatten: () => flatten,
-  getArrayFromDType: () => getArrayFromDType,
-  getTypedArrayFromDType: () => getTypedArrayFromDType,
-  hasEncodingLoss: () => hasEncodingLoss,
-  indexToLoc: () => indexToLoc,
-  inferDtype: () => inferDtype,
-  inferFromImplicitShape: () => inferFromImplicitShape,
-  isBoolean: () => isBoolean,
-  isFunction: () => isFunction,
-  isInt: () => isInt,
-  isNumber: () => isNumber,
-  isScalarShape: () => isScalarShape,
-  isString: () => isString,
-  isTypedArray: () => isTypedArray,
-  isValidDtype: () => isValidDtype,
-  locToIndex: () => locToIndex,
-  makeOnesTypedArray: () => makeOnesTypedArray,
-  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
-  makeZerosTypedArray: () => makeZerosTypedArray,
-  nearestDivisor: () => nearestDivisor,
-  nearestLargerEven: () => nearestLargerEven,
-  now: () => now,
-  parseAxisParam: () => parseAxisParam,
-  randUniform: () => randUniform,
-  repeatedTry: () => repeatedTry,
-  rightPad: () => rightPad,
-  shuffle: () => shuffle,
-  sizeFromShape: () => sizeFromShape,
-  sizeToSquarishShape: () => sizeToSquarishShape,
-  squeezeShape: () => squeezeShape,
-  sum: () => sum,
-  tanh: () => tanh,
-  toNestedArray: () => toNestedArray,
-  toTypedArray: () => toTypedArray
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function shuffle(array) {
-  let counter = array.length;
-  let temp = 0;
-  let index = 0;
-  while (counter > 0) {
-    index = Math.random() * counter | 0;
-    counter--;
-    temp = array[counter];
-    array[counter] = array[index];
-    array[index] = temp;
-  }
-}
-function clamp(min4, x, max6) {
-  return Math.max(min4, Math.min(x, max6));
-}
-function nearestLargerEven(val) {
-  return val % 2 === 0 ? val : val + 1;
-}
-function sum(arr) {
-  let sum26 = 0;
-  for (let i = 0; i < arr.length; i++) {
-    sum26 += arr[i];
-  }
-  return sum26;
-}
-function randUniform(a, b) {
-  const r = Math.random();
-  return b * r + (1 - r) * a;
-}
-function distSquared(a, b) {
-  let result = 0;
-  for (let i = 0; i < a.length; i++) {
-    const diff = Number(a[i]) - Number(b[i]);
-    result += diff * diff;
-  }
-  return result;
-}
-function assert(expr, msg) {
-  if (!expr) {
-    throw new Error(typeof msg === "string" ? msg : msg());
-  }
-}
-function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
-  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-}
-function assertNonNull(a) {
-  assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
-}
-function flatten(arr, result = [], skipTypedArray = false) {
-  if (result == null) {
-    result = [];
-  }
-  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-    for (let i = 0; i < arr.length; ++i) {
-      flatten(arr[i], result, skipTypedArray);
-    }
-  } else {
-    result.push(arr);
-  }
-  return result;
-}
-function sizeFromShape(shape) {
-  if (shape.length === 0) {
-    return 1;
-  }
-  let size = shape[0];
-  for (let i = 1; i < shape.length; i++) {
-    size *= shape[i];
-  }
-  return size;
-}
-function isScalarShape(shape) {
-  return shape.length === 0;
-}
-function arraysEqual(n1, n2) {
-  if (n1 === n2) {
-    return true;
-  }
-  if (n1 == null || n2 == null) {
-    return false;
-  }
-  if (n1.length !== n2.length) {
-    return false;
-  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function isInt(a) {
-  return a % 1 === 0;
-}
-function tanh(x) {
-  if (Math.tanh != null) {
-    return Math.tanh(x);
-  }
-  if (x === Infinity) {
-    return 1;
-  } else if (x === -Infinity) {
-    return -1;
-  } else {
-    const e2x = Math.exp(2 * x);
-    return (e2x - 1) / (e2x + 1);
-  }
-}
-function sizeToSquarishShape(size) {
-  const width = Math.ceil(Math.sqrt(size));
-  return [width, Math.ceil(size / width)];
-}
-function createShuffledIndices(n) {
-  const shuffledIndices = new Uint32Array(n);
-  for (let i = 0; i < n; ++i) {
-    shuffledIndices[i] = i;
-  }
-  shuffle(shuffledIndices);
-  return shuffledIndices;
-}
-function rightPad(a, size) {
-  if (size <= a.length) {
-    return a;
-  }
-  return a + " ".repeat(size - a.length);
-}
-function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
-  return new Promise((resolve, reject) => {
-    let tryCount = 0;
-    const tryFn = () => {
-      if (checkFn()) {
-        resolve();
-        return;
-      }
-      tryCount++;
-      const nextBackoff = delayFn(tryCount);
-      if (maxCounter != null && tryCount >= maxCounter) {
-        reject();
-        return;
-      }
-      setTimeout(tryFn, nextBackoff);
-    };
-    tryFn();
-  });
-}
-function inferFromImplicitShape(shape, size) {
-  let shapeProd = 1;
-  let implicitIdx = -1;
-  for (let i = 0; i < shape.length; ++i) {
-    if (shape[i] >= 0) {
-      shapeProd *= shape[i];
-    } else if (shape[i] === -1) {
-      if (implicitIdx !== -1) {
-        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
-      }
-      implicitIdx = i;
-    } else if (shape[i] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
-    }
-  }
-  if (implicitIdx === -1) {
-    if (size > 0 && size !== shapeProd) {
-      throw Error(`Size(${size}) must match the product of shape ${shape}`);
-    }
-    return shape;
-  }
-  if (shapeProd === 0) {
-    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
-  }
-  if (size % shapeProd !== 0) {
-    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
-  }
-  const newShape = shape.slice();
-  newShape[implicitIdx] = size / shapeProd;
-  return newShape;
-}
-function parseAxisParam(axis, shape) {
-  const rank = shape.length;
-  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
-  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
-  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
-  return axis.map((a) => a < 0 ? rank + a : a);
-}
-function squeezeShape(shape, axis) {
-  const newShape = [];
-  const keptDims = [];
-  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
-  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
-  let j = 0;
-  for (let i = 0; i < shape.length; ++i) {
-    if (axes != null) {
-      if (axes[j] === i && shape[i] !== 1) {
-        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
-      }
-      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
-        newShape.push(shape[i]);
-        keptDims.push(i);
-      }
-      if (axes[j] <= i) {
-        j++;
-      }
-    }
-    if (shape[i] !== 1) {
-      newShape.push(shape[i]);
-      keptDims.push(i);
-    }
-  }
-  return {newShape, keptDims};
-}
-function getTypedArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function getArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else if (dtype === "string") {
-    values = new Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function checkConversionForErrors(vals, dtype) {
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
-    }
-  }
-}
-function isValidDtype(dtype) {
-  return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
-}
-function hasEncodingLoss(oldType, newType) {
-  if (newType === "complex64") {
-    return false;
-  }
-  if (newType === "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "bool" && oldType === "bool") {
-    return false;
-  }
-  return true;
-}
-function isTypedArray(a) {
-  return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
-}
-function bytesPerElement(dtype) {
-  if (dtype === "float32" || dtype === "int32") {
-    return 4;
-  } else if (dtype === "complex64") {
-    return 8;
-  } else if (dtype === "bool") {
-    return 1;
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-function bytesFromStringArray(arr) {
-  if (arr == null) {
-    return 0;
-  }
-  let bytes = 0;
-  arr.forEach((x) => bytes += x.length);
-  return bytes;
-}
-function isString(value) {
-  return typeof value === "string" || value instanceof String;
-}
-function isBoolean(value) {
-  return typeof value === "boolean";
-}
-function isNumber(value) {
-  return typeof value === "number";
-}
-function inferDtype(values) {
-  if (Array.isArray(values)) {
-    return inferDtype(values[0]);
-  }
-  if (values instanceof Float32Array) {
-    return "float32";
-  } else if (values instanceof Int32Array || values instanceof Uint8Array) {
-    return "int32";
-  } else if (isNumber(values)) {
-    return "float32";
-  } else if (isString(values)) {
-    return "string";
-  } else if (isBoolean(values)) {
-    return "bool";
-  }
-  return "float32";
-}
-function isFunction(f) {
-  return !!(f && f.constructor && f.call && f.apply);
-}
-function nearestDivisor(size, start) {
-  for (let i = start; i < size; ++i) {
-    if (size % i === 0) {
-      return i;
-    }
-  }
-  return size;
-}
-function computeStrides(shape) {
-  const rank = shape.length;
-  if (rank < 2) {
-    return [];
-  }
-  const strides = new Array(rank - 1);
-  strides[rank - 2] = shape[rank - 1];
-  for (let i = rank - 3; i >= 0; --i) {
-    strides[i] = strides[i + 1] * shape[i + 1];
-  }
-  return strides;
-}
-function toTypedArray(a, dtype) {
-  if (dtype === "string") {
-    throw new Error("Cannot convert a string[] to a TypedArray");
-  }
-  if (Array.isArray(a)) {
-    a = flatten(a);
-  }
-  if (env().getBool("DEBUG")) {
-    checkConversionForErrors(a, dtype);
-  }
-  if (noConversionNeeded(a, dtype)) {
-    return a;
-  }
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(a);
-  } else if (dtype === "int32") {
-    return new Int32Array(a);
-  } else if (dtype === "bool") {
-    const bool = new Uint8Array(a.length);
-    for (let i = 0; i < bool.length; ++i) {
-      if (Math.round(a[i]) !== 0) {
-        bool[i] = 1;
-      }
-    }
-    return bool;
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function createNestedArray(offset, shape, a) {
-  const ret = new Array();
-  if (shape.length === 1) {
-    const d = shape[0];
-    for (let i = 0; i < d; i++) {
-      ret[i] = a[offset + i];
-    }
-  } else {
-    const d = shape[0];
-    const rest = shape.slice(1);
-    const len = rest.reduce((acc, c) => acc * c);
-    for (let i = 0; i < d; i++) {
-      ret[i] = createNestedArray(offset + i * len, rest, a);
-    }
-  }
-  return ret;
-}
-function toNestedArray(shape, a) {
-  if (shape.length === 0) {
-    return a[0];
-  }
-  const size = shape.reduce((acc, c) => acc * c);
-  if (size === 0) {
-    return [];
-  }
-  if (size !== a.length) {
-    throw new Error(`[${shape}] does not match the input size ${a.length}.`);
-  }
-  return createNestedArray(0, shape, a);
-}
-function noConversionNeeded(a, dtype) {
-  return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
-}
-function makeOnesTypedArray(size, dtype) {
-  const array = makeZerosTypedArray(size, dtype);
-  for (let i = 0; i < array.length; i++) {
-    array[i] = 1;
-  }
-  return array;
-}
-function makeZerosTypedArray(size, dtype) {
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(size);
-  } else if (dtype === "int32") {
-    return new Int32Array(size);
-  } else if (dtype === "bool") {
-    return new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function makeZerosNestedTypedArray(shape, dtype) {
-  const size = shape.reduce((prev, curr) => prev * curr, 1);
-  if (dtype == null || dtype === "float32") {
-    return toNestedArray(shape, new Float32Array(size));
-  } else if (dtype === "int32") {
-    return toNestedArray(shape, new Int32Array(size));
-  } else if (dtype === "bool") {
-    return toNestedArray(shape, new Uint8Array(size));
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function now() {
-  return env().platform.now();
-}
-function assertNonNegativeIntegerDimensions(shape) {
-  shape.forEach((dimSize) => {
-    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
-  });
-}
-function fetch2(path, requestInits) {
-  return env().platform.fetch(path, requestInits);
-}
-function encodeString(s, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.encode(s, encoding);
-}
-function decodeString(bytes, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.decode(bytes, encoding);
-}
-function locToIndex(locs, rank, strides) {
-  if (rank === 0) {
-    return 0;
-  } else if (rank === 1) {
-    return locs[0];
-  }
-  let index = locs[locs.length - 1];
-  for (let i = 0; i < locs.length - 1; ++i) {
-    index += strides[i] * locs[i];
-  }
-  return index;
-}
-function indexToLoc(index, rank, strides) {
-  if (rank === 0) {
-    return [];
-  } else if (rank === 1) {
-    return [index];
-  }
-  const locs = new Array(rank);
-  for (let i = 0; i < locs.length - 1; ++i) {
-    locs[i] = Math.floor(index / strides[i]);
-    index -= locs[i] * strides[i];
-  }
-  locs[locs.length - 1] = index;
-  return locs;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/profiler.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Profiler {
-  constructor(backendTimer, logger) {
-    this.backendTimer = backendTimer;
-    this.logger = logger;
-    if (logger == null) {
-      this.logger = new Logger();
-    }
-  }
-  profileKernel(kernelName, inputs, f) {
-    let outputs;
-    const holdResultWrapperFn = () => {
-      outputs = f();
-    };
-    const timer = this.backendTimer.time(holdResultWrapperFn);
-    for (let i = 0; i < outputs.length; i++) {
-      const output = outputs[i];
-      output.data().then((tensorVals) => {
-        checkComputationForErrors(tensorVals, output.dtype, kernelName);
-      });
-    }
-    const kernelProfile = {
-      kernelName,
-      outputs,
-      inputs,
-      timeMs: timer.then((timing) => timing.kernelMs),
-      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
-    };
-    return kernelProfile;
-  }
-  logKernelProfile(kernelProfile) {
-    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
-    outputs.forEach((result) => {
-      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
-        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
-      });
-    });
-  }
-}
-function checkComputationForErrors(vals, dtype, kernelName) {
-  if (dtype !== "float32") {
-    return false;
-  }
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      console.warn(`Found ${num} in the result of '${kernelName}'`);
-      return true;
-    }
-  }
-  return false;
-}
-class Logger {
-  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
-    const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
-    const paddedName = rightPad(name, 25);
-    const rank = result.rank;
-    const size = result.size;
-    const shape = rightPad(result.shape.toString(), 14);
-    let inputShapesDescription = "";
-    for (const name2 in inputs) {
-      const input = inputs[name2];
-      if (input != null) {
-        const inputShape = input.shape || result.shape;
-        const inputRank = inputShape.length;
-        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
-      }
-    }
-    console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tape.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFilteredNodesXToY(tape2, xs, y) {
-  const tensorsFromX = {};
-  const nodesFromX = {};
-  for (let i = 0; i < xs.length; i++) {
-    tensorsFromX[xs[i].id] = true;
-  }
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (const inputName in nodeInputs) {
-      const input = nodeInputs[inputName];
-      let anyInputFromX = false;
-      for (let j = 0; j < xs.length; j++) {
-        if (tensorsFromX[input.id]) {
-          node2.outputs.forEach((output) => tensorsFromX[output.id] = true);
-          anyInputFromX = true;
-          nodesFromX[node2.id] = true;
-          break;
-        }
-      }
-      if (anyInputFromX) {
-        break;
-      }
-    }
-  }
-  const tensorsLeadToY = {};
-  tensorsLeadToY[y.id] = true;
-  const nodesToY = {};
-  for (let i = tape2.length - 1; i >= 0; i--) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (let j = 0; j < node2.outputs.length; j++) {
-      if (tensorsLeadToY[node2.outputs[j].id]) {
-        for (const inputName in nodeInputs) {
-          tensorsLeadToY[nodeInputs[inputName].id] = true;
-          nodesToY[node2.id] = true;
-        }
-        break;
-      }
-    }
-  }
-  const filteredTape = [];
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    if (nodesFromX[node2.id] && nodesToY[node2.id]) {
-      const prunedInputs = {};
-      for (const inputName in node2.inputs) {
-        const nodeInput = node2.inputs[inputName];
-        if (tensorsFromX[nodeInput.id]) {
-          prunedInputs[inputName] = nodeInput;
-        }
-      }
-      const prunedNode = Object.assign({}, node2);
-      prunedNode.inputs = prunedInputs;
-      prunedNode.outputs = node2.outputs;
-      filteredTape.push(prunedNode);
-    }
-  }
-  return filteredTape;
-}
-function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add29) {
-  for (let i = filteredTape.length - 1; i >= 0; i--) {
-    const node2 = filteredTape[i];
-    const dys = [];
-    node2.outputs.forEach((o) => {
-      const gradTensor = tensorAccumulatedGradientMap[o.id];
-      if (gradTensor != null) {
-        dys.push(gradTensor);
-      } else {
-        dys.push(null);
-      }
-    });
-    if (node2.gradient == null) {
-      throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);
-    }
-    const inputGradients = node2.gradient(dys);
-    for (const inputName in node2.inputs) {
-      if (!(inputName in inputGradients)) {
-        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
-      }
-      const dx = tidy2(() => inputGradients[inputName]());
-      if (dx.dtype !== "float32") {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
-      }
-      const x = node2.inputs[inputName];
-      if (!arraysEqual(dx.shape, x.shape)) {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
-      }
-      if (tensorAccumulatedGradientMap[x.id] == null) {
-        tensorAccumulatedGradientMap[x.id] = dx;
-      } else {
-        const curGradient = tensorAccumulatedGradientMap[x.id];
-        tensorAccumulatedGradientMap[x.id] = add29(curGradient, dx);
-        curGradient.dispose();
-      }
-    }
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const FORMAT_LIMIT_NUM_VALS = 20;
-const FORMAT_NUM_FIRST_LAST_VALS = 3;
-const FORMAT_NUM_SIG_DIGITS = 7;
-function tensorToString(vals, shape, dtype, verbose) {
-  const strides = computeStrides(shape);
-  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
-  const rank = shape.length;
-  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
-  const lines = ["Tensor"];
-  if (verbose) {
-    lines.push(`  dtype: ${dtype}`);
-    lines.push(`  rank: ${rank}`);
-    lines.push(`  shape: [${shape}]`);
-    lines.push(`  values:`);
-  }
-  lines.push(valsLines.map((l) => "    " + l).join("\n"));
-  return lines.join("\n");
-}
-function computeMaxSizePerColumn(vals, shape, dtype, strides) {
-  const n = sizeFromShape(shape);
-  const numCols = strides[strides.length - 1];
-  const padPerCol = new Array(numCols).fill(0);
-  const rank = shape.length;
-  const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
-  if (rank > 1) {
-    for (let row = 0; row < n / numCols; row++) {
-      const offset = row * numCols;
-      for (let j = 0; j < numCols; j++) {
-        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
-      }
-    }
-  }
-  return padPerCol;
-}
-function valToString(val, pad8, dtype) {
-  let valStr;
-  if (Array.isArray(val)) {
-    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
-  } else if (isString(val)) {
-    valStr = `'${val}'`;
-  } else if (dtype === "bool") {
-    valStr = boolNumToString(val);
-  } else {
-    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
-  }
-  return rightPad(valStr, pad8);
-}
-function boolNumToString(v) {
-  return v === 0 ? "false" : "true";
-}
-function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
-  const storagePerElement = dtype === "complex64" ? 2 : 1;
-  const size = shape[0];
-  const rank = shape.length;
-  if (rank === 0) {
-    if (dtype === "complex64") {
-      const complexTuple = createComplexTuples(vals);
-      return [valToString(complexTuple[0], 0, dtype)];
-    }
-    if (dtype === "bool") {
-      return [boolNumToString(vals[0])];
-    }
-    return [vals[0].toString()];
-  }
-  if (rank === 1) {
-    if (size > FORMAT_LIMIT_NUM_VALS) {
-      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
-      let firstVals = Array.from(vals.slice(0, firstValsSize));
-      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
-      if (dtype === "complex64") {
-        firstVals = createComplexTuples(firstVals);
-        lastVals = createComplexTuples(lastVals);
-      }
-      return [
-        "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
-      ];
-    }
-    const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
-    return [
-      "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
-    ];
-  }
-  const subshape = shape.slice(1);
-  const substrides = strides.slice(1);
-  const stride = strides[0] * storagePerElement;
-  const lines = [];
-  if (size > FORMAT_LIMIT_NUM_VALS) {
-    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
-    }
-    lines.push("...");
-    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  } else {
-    for (let i = 0; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  }
-  const sep = rank === 2 ? "," : "";
-  lines[0] = "[" + lines[0] + sep;
-  for (let i = 1; i < lines.length - 1; i++) {
-    lines[i] = " " + lines[i] + sep;
-  }
-  let newLineSep = ",\n";
-  for (let i = 2; i < rank; i++) {
-    newLineSep += "\n";
-  }
-  lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
-  return lines;
-}
-function createComplexTuples(vals) {
-  const complexTuples = [];
-  for (let i = 0; i < vals.length; i += 2) {
-    complexTuples.push([vals[i], vals[i + 1]]);
-  }
-  return complexTuples;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class TensorBuffer {
-  constructor(shape, dtype, values) {
-    this.dtype = dtype;
-    this.shape = shape.slice();
-    this.size = sizeFromShape(shape);
-    if (values != null) {
-      const n = values.length;
-      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
-    }
-    if (dtype === "complex64") {
-      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
-    }
-    this.values = values || getArrayFromDType(dtype, this.size);
-    this.strides = computeStrides(shape);
-  }
-  set(value, ...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
-    const index = this.locToIndex(locs);
-    this.values[index] = value;
-  }
-  get(...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    let i = 0;
-    for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i]) {
-        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
-        throw new Error(msg);
-      }
-      i++;
-    }
-    let index = locs[locs.length - 1];
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      index += this.strides[i2] * locs[i2];
-    }
-    return this.values[index];
-  }
-  locToIndex(locs) {
-    if (this.rank === 0) {
-      return 0;
-    } else if (this.rank === 1) {
-      return locs[0];
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += this.strides[i] * locs[i];
-    }
-    return index;
-  }
-  indexToLoc(index) {
-    if (this.rank === 0) {
-      return [];
-    } else if (this.rank === 1) {
-      return [index];
-    }
-    const locs = new Array(this.shape.length);
-    for (let i = 0; i < locs.length - 1; ++i) {
-      locs[i] = Math.floor(index / this.strides[i]);
-      index -= locs[i] * this.strides[i];
-    }
-    locs[locs.length - 1] = index;
-    return locs;
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  toTensor() {
-    return trackerFn().makeTensor(this.values, this.shape, this.dtype);
-  }
-}
-let trackerFn = null;
-let opHandler = null;
-let deprecationWarningFn = null;
-function setTensorTracker(fn) {
-  trackerFn = fn;
-}
-function setOpHandler(handler) {
-  opHandler = handler;
-}
-function setDeprecationWarningFn(fn) {
-  deprecationWarningFn = fn;
-}
-class Tensor {
-  constructor(shape, dtype, dataId, id) {
-    this.kept = false;
-    this.isDisposedInternal = false;
-    this.shape = shape.slice();
-    this.dtype = dtype || "float32";
-    this.size = sizeFromShape(shape);
-    this.strides = computeStrides(shape);
-    this.dataId = dataId;
-    this.id = id;
-    this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  async buffer() {
-    const vals = await this.data();
-    return opHandler.buffer(this.shape, this.dtype, vals);
-  }
-  bufferSync() {
-    return opHandler.buffer(this.shape, this.dtype, this.dataSync());
-  }
-  async array() {
-    const vals = await this.data();
-    return toNestedArray(this.shape, vals);
-  }
-  arraySync() {
-    return toNestedArray(this.shape, this.dataSync());
-  }
-  async data() {
-    this.throwIfDisposed();
-    const data = trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      const bytes = await data;
-      try {
-        return bytes.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  dataSync() {
-    this.throwIfDisposed();
-    const data = trackerFn().readSync(this.dataId);
-    if (this.dtype === "string") {
-      try {
-        return data.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  async bytes() {
-    this.throwIfDisposed();
-    const data = await trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      return data;
-    } else {
-      return new Uint8Array(data.buffer);
-    }
-  }
-  dispose() {
-    if (this.isDisposed) {
-      return;
-    }
-    trackerFn().disposeTensor(this);
-    this.isDisposedInternal = true;
-  }
-  get isDisposed() {
-    return this.isDisposedInternal;
-  }
-  throwIfDisposed() {
-    if (this.isDisposed) {
-      throw new Error(`Tensor is disposed.`);
-    }
-  }
-  print(verbose = false) {
-    return opHandler.print(this, verbose);
-  }
-  clone() {
-    this.throwIfDisposed();
-    return opHandler.clone(this);
-  }
-  toString(verbose = false) {
-    const vals = this.dataSync();
-    return tensorToString(vals, this.shape, this.dtype, verbose);
-  }
-  cast(dtype) {
-    this.throwIfDisposed();
-    return opHandler.cast(this, dtype);
-  }
-  variable(trainable = true, name, dtype) {
-    this.throwIfDisposed();
-    return trackerFn().makeVariable(this, trainable, name, dtype);
-  }
-}
-Object.defineProperty(Tensor, Symbol.hasInstance, {
-  value: (instance) => {
-    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
-  }
-});
-class Variable extends Tensor {
-  constructor(initialValue, trainable, name, tensorId) {
-    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
-    this.trainable = trainable;
-    this.name = name;
-  }
-  assign(newValue) {
-    if (newValue.dtype !== this.dtype) {
-      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
-    }
-    if (!arraysEqual(newValue.shape, this.shape)) {
-      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
-    }
-    trackerFn().disposeTensor(this);
-    this.dataId = newValue.dataId;
-    trackerFn().incRef(this, null);
-  }
-  dispose() {
-    trackerFn().disposeVariable(this);
-    this.isDisposedInternal = true;
-  }
-}
-Object.defineProperty(Variable, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/types.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Rank;
-(function(Rank2) {
-  Rank2["R0"] = "R0";
-  Rank2["R1"] = "R1";
-  Rank2["R2"] = "R2";
-  Rank2["R3"] = "R3";
-  Rank2["R4"] = "R4";
-  Rank2["R5"] = "R5";
-  Rank2["R6"] = "R6";
-})(Rank || (Rank = {}));
-var UpcastInt32AndMap;
-(function(UpcastInt32AndMap2) {
-  UpcastInt32AndMap2["float32"] = "float32";
-  UpcastInt32AndMap2["int32"] = "int32";
-  UpcastInt32AndMap2["bool"] = "int32";
-  UpcastInt32AndMap2["complex64"] = "complex64";
-})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
-var UpcastBoolAndMap;
-(function(UpcastBoolAndMap2) {
-  UpcastBoolAndMap2["float32"] = "float32";
-  UpcastBoolAndMap2["int32"] = "int32";
-  UpcastBoolAndMap2["bool"] = "bool";
-  UpcastBoolAndMap2["complex64"] = "complex64";
-})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
-var UpcastFloat32AndMap;
-(function(UpcastFloat32AndMap2) {
-  UpcastFloat32AndMap2["float32"] = "float32";
-  UpcastFloat32AndMap2["int32"] = "float32";
-  UpcastFloat32AndMap2["bool"] = "float32";
-  UpcastFloat32AndMap2["complex64"] = "complex64";
-})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
-var UpcastComplex64AndMap;
-(function(UpcastComplex64AndMap2) {
-  UpcastComplex64AndMap2["float32"] = "complex64";
-  UpcastComplex64AndMap2["int32"] = "complex64";
-  UpcastComplex64AndMap2["bool"] = "complex64";
-  UpcastComplex64AndMap2["complex64"] = "complex64";
-})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
-const upcastTypeMap = {
-  float32: UpcastFloat32AndMap,
-  int32: UpcastInt32AndMap,
-  bool: UpcastBoolAndMap,
-  complex64: UpcastComplex64AndMap
-};
-function upcastType(typeA, typeB) {
-  if (typeA === "string" || typeB === "string") {
-    if (typeA === "string" && typeB === "string") {
-      return "string";
-    }
-    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
-  }
-  return upcastTypeMap[typeA][typeB];
-}
-function sumOutType(type) {
-  return upcastType(type, "int32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
-const tensor_util_exports = {};
-__export(tensor_util_exports, {
-  assertTypesMatch: () => assertTypesMatch,
-  getTensorsInContainer: () => getTensorsInContainer,
-  isTensorInList: () => isTensorInList,
-  makeTypesMatch: () => makeTypesMatch
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTypesMatch(a, b) {
-  if (a.dtype === b.dtype) {
-    return [a, b];
-  }
-  const dtype = upcastType(a.dtype, b.dtype);
-  return [a.cast(dtype), b.cast(dtype)];
-}
-function assertTypesMatch(a, b) {
-  assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
-}
-function isTensorInList(tensor17, tensorList) {
-  return tensorList.some((x) => x.id === tensor17.id);
-}
-function getTensorsInContainer(result) {
-  const list = [];
-  const seen = new Set();
-  walkTensorContainer(result, list, seen);
-  return list;
-}
-function walkTensorContainer(container, list, seen) {
-  if (container == null) {
-    return;
-  }
-  if (container instanceof Tensor) {
-    list.push(container);
-    return;
-  }
-  if (!isIterable(container)) {
-    return;
-  }
-  const iterable = container;
-  for (const k in iterable) {
-    const val = iterable[k];
-    if (!seen.has(val)) {
-      seen.add(val);
-      walkTensorContainer(val, list, seen);
-    }
-  }
-}
-function isIterable(obj) {
-  return Array.isArray(obj) || typeof obj === "object";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/engine.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class EngineState {
-  constructor() {
-    this.registeredVariables = {};
-    this.nextTapeNodeId = 0;
-    this.numBytes = 0;
-    this.numTensors = 0;
-    this.numStringTensors = 0;
-    this.numDataBuffers = 0;
-    this.gradientDepth = 0;
-    this.kernelDepth = 0;
-    this.scopeStack = [];
-    this.numDataMovesStack = [];
-    this.nextScopeId = 0;
-    this.tensorInfo = new WeakMap();
-    this.profiling = false;
-    this.activeProfile = {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};
-  }
-  dispose() {
-    for (const variableName in this.registeredVariables) {
-      this.registeredVariables[variableName].dispose();
-    }
-  }
-}
-class Engine {
-  constructor(ENV3) {
-    this.ENV = ENV3;
-    this.registry = {};
-    this.registryFactory = {};
-    this.pendingBackendInitId = 0;
-    this.state = new EngineState();
-  }
-  async ready() {
-    if (this.pendingBackendInit != null) {
-      return this.pendingBackendInit.then(() => {
-      });
-    }
-    if (this.backendInstance != null) {
-      return;
-    }
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const success = await this.initializeBackend(backendName).success;
-      if (success) {
-        await this.setBackend(backendName);
-        return;
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  get backend() {
-    if (this.pendingBackendInit != null) {
-      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-    }
-    if (this.backendInstance == null) {
-      const {name, asyncInit} = this.initializeBackendsAndReturnBest();
-      if (asyncInit) {
-        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-      }
-      this.setBackend(name);
-    }
-    return this.backendInstance;
-  }
-  backendNames() {
-    return Object.keys(this.registryFactory);
-  }
-  findBackend(backendName) {
-    if (!(backendName in this.registry)) {
-      if (backendName in this.registryFactory) {
-        const {asyncInit} = this.initializeBackend(backendName);
-        if (asyncInit) {
-          return null;
-        }
-      } else {
-        return null;
-      }
-    }
-    return this.registry[backendName];
-  }
-  findBackendFactory(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      return null;
-    }
-    return this.registryFactory[backendName].factory;
-  }
-  registerBackend(backendName, factory, priority = 1) {
-    if (backendName in this.registryFactory) {
-      console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
-      return false;
-    }
-    this.registryFactory[backendName] = {factory, priority};
-    return true;
-  }
-  async setBackend(backendName) {
-    if (this.registryFactory[backendName] == null) {
-      throw new Error(`Backend name '${backendName}' not found in registry`);
-    }
-    this.backendName = backendName;
-    if (this.registry[backendName] == null) {
-      this.backendInstance = null;
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      const result = asyncInit ? await success : success;
-      if (!result) {
-        return false;
-      }
-    }
-    this.backendInstance = this.registry[backendName];
-    this.setupRegisteredKernels();
-    this.profiler = new Profiler(this.backendInstance);
-    return true;
-  }
-  setupRegisteredKernels() {
-    const kernels = getKernelsForBackend(this.backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.setupFunc != null) {
-        kernel.setupFunc(this.backendInstance);
-      }
-    });
-  }
-  disposeRegisteredKernels(backendName) {
-    const kernels = getKernelsForBackend(backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.disposeFunc != null) {
-        kernel.disposeFunc(this.registry[backendName]);
-      }
-    });
-  }
-  initializeBackend(backendName) {
-    const registryFactoryEntry = this.registryFactory[backendName];
-    if (registryFactoryEntry == null) {
-      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
-    }
-    try {
-      const backend2 = registryFactoryEntry.factory();
-      if (Promise.resolve(backend2) === backend2) {
-        const promiseId = ++this.pendingBackendInitId;
-        const success = backend2.then((backendInstance) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.registry[backendName] = backendInstance;
-          this.pendingBackendInit = null;
-          return true;
-        }).catch((err) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.pendingBackendInit = null;
-          console.warn(`Initialization of backend ${backendName} failed`);
-          console.warn(err.stack || err.message);
-          return false;
-        });
-        this.pendingBackendInit = success;
-        return {success, asyncInit: true};
-      } else {
-        this.registry[backendName] = backend2;
-        return {success: true, asyncInit: false};
-      }
-    } catch (err) {
-      console.warn(`Initialization of backend ${backendName} failed`);
-      console.warn(err.stack || err.message);
-      return {success: false, asyncInit: false};
-    }
-  }
-  removeBackend(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      throw new Error(`${backendName} backend not found in registry`);
-    }
-    if (this.backendName === backendName && this.pendingBackendInit != null) {
-      this.pendingBackendInitId++;
-    }
-    if (backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    delete this.registryFactory[backendName];
-    if (this.backendName === backendName) {
-      this.pendingBackendInit = null;
-      this.backendName = null;
-      this.backendInstance = null;
-    }
-  }
-  getSortedBackends() {
-    if (Object.keys(this.registryFactory).length === 0) {
-      throw new Error("No backend found in registry.");
-    }
-    return Object.keys(this.registryFactory).sort((a, b) => {
-      return this.registryFactory[b].priority - this.registryFactory[a].priority;
-    });
-  }
-  initializeBackendsAndReturnBest() {
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      if (asyncInit || success) {
-        return {name: backendName, asyncInit};
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  moveData(backend2, dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    const srcBackend = info.backend;
-    const values = this.readSync(dataId);
-    srcBackend.disposeData(dataId);
-    info.backend = backend2;
-    backend2.move(dataId, values, info.shape, info.dtype);
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
-    }
-  }
-  tidy(nameOrFn, fn) {
-    let name = null;
-    if (fn == null) {
-      if (typeof nameOrFn !== "function") {
-        throw new Error("Please provide a function to tidy()");
-      }
-      fn = nameOrFn;
-    } else {
-      if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
-        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
-      }
-      if (typeof fn !== "function") {
-        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
-      }
-      name = nameOrFn;
-    }
-    let result;
-    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
-      result = fn();
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      return result;
-    });
-  }
-  scopedRun(start, end, f) {
-    start();
-    try {
-      const res = f();
-      end();
-      return res;
-    } catch (ex) {
-      end();
-      throw ex;
-    }
-  }
-  nextTensorId() {
-    return Engine.nextTensorId++;
-  }
-  nextVariableId() {
-    return Engine.nextVariableId++;
-  }
-  clone(x) {
-    const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
-    const inputs = {x};
-    const grad2 = (dy) => ({
-      x: () => {
-        const dtype = "float32";
-        const gradInputs = {x: dy};
-        const attrs = {dtype};
-        return ENGINE.runKernelFunc((backend2) => backend2.cast(dy, dtype), gradInputs, null, Cast, attrs);
-      }
-    });
-    const saved = [];
-    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
-    return y;
-  }
-  runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {
-    const forwardFunc = null;
-    const backwardsFunc = null;
-    return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
-  }
-  shouldCheckForMemLeaks() {
-    return this.ENV.getBool("IS_TEST");
-  }
-  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
-    const numDataIdsAfter = this.backend.numDataIds();
-    let numOutputDataIds = 0;
-    outInfos.forEach((info) => {
-      numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
-    });
-    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
-    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
-    if (dataIdsLeaked > 0) {
-      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
-    }
-  }
-  runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
-    let outputs;
-    let saved = [];
-    const isTapeOn = this.isTapeOn();
-    if (kernelName == null) {
-      kernelName = this.state.activeScope != null ? this.state.activeScope.name : "";
-    }
-    const startingBytecount = this.state.numBytes;
-    const startingNumTensors = this.state.numTensors;
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack.push(0);
-    }
-    let kernelFunc;
-    const kernel = getKernel(kernelName, this.backendName);
-    let out;
-    if (kernel != null) {
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
-        const outInfos = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
-        }
-        const outTensors = outInfos.map(({dataId, shape, dtype}) => this.makeTensorFromDataId(dataId, shape, dtype));
-        if (isTapeOn) {
-          let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
-          if (tensorsToSave == null) {
-            if (outputsToSave == null) {
-              outputsToSave = [];
-            }
-            const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);
-            tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);
-          }
-          saved = this.saveTensorsForBackwardMode(tensorsToSave);
-        }
-        return outTensors;
-      };
-    } else {
-      const saveFunc = (tensors) => {
-        if (!isTapeOn) {
-          return;
-        }
-        saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-      };
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
-        const outs = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
-        }
-        return outs;
-      };
-    }
-    let kernelProfile;
-    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-      if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
-        outputs = kernelFunc();
-      } else {
-        kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());
-        if (this.ENV.getBool("DEBUG")) {
-          this.profiler.logKernelProfile(kernelProfile);
-        }
-        outputs = kernelProfile.outputs;
-      }
-    });
-    if (isTapeOn) {
-      this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);
-    }
-    if (this.state.profiling) {
-      this.state.activeProfile.kernels.push({
-        name: kernelName,
-        bytesAdded: this.state.numBytes - startingBytecount,
-        totalBytesSnapshot: this.state.numBytes,
-        tensorsAdded: this.state.numTensors - startingNumTensors,
-        totalTensorsSnapshot: this.state.numTensors,
-        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
-        outputShapes: outputs.map((item) => item.shape),
-        kernelTimeMs: kernelProfile.timeMs,
-        extraInfo: kernelProfile.extraInfo
-      });
-    }
-    return Array.isArray(out) ? outputs : outputs[0];
-  }
-  saveTensorsForBackwardMode(tensors) {
-    const saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-    return saved;
-  }
-  getTensorsForGradient(kernelName, inputs, outputs) {
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      const inputsToSave = gradConfig.inputsToSave || [];
-      const outputsToSave = gradConfig.outputsToSave || [];
-      let inputTensorsToSave;
-      if (gradConfig.saveAllInputs) {
-        assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
-        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
-      } else {
-        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
-      }
-      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
-      return inputTensorsToSave.concat(outputTensorsToSave);
-    }
-    return null;
-  }
-  makeTensor(values, shape, dtype, backend2) {
-    if (values == null) {
-      throw new Error("Values passed to engine.makeTensor() are null");
-    }
-    dtype = dtype || "float32";
-    backend2 = backend2 || this.backend;
-    let backendVals = values;
-    if (dtype === "string" && isString(values[0])) {
-      backendVals = values.map((d) => encodeString(d));
-    }
-    const dataId = backend2.write(backendVals, shape, dtype);
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    if (dtype === "string") {
-      const info = this.state.tensorInfo.get(dataId);
-      const newBytes = bytesFromStringArray(backendVals);
-      this.state.numBytes += newBytes - info.bytes;
-      info.bytes = newBytes;
-    }
-    return t;
-  }
-  makeTensorFromDataId(dataId, shape, dtype, backend2) {
-    dtype = dtype || "float32";
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    return t;
-  }
-  makeVariable(initialValue, trainable = true, name, dtype) {
-    name = name || this.nextVariableId().toString();
-    if (dtype != null && dtype !== initialValue.dtype) {
-      initialValue = initialValue.cast(dtype);
-    }
-    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
-    if (this.state.registeredVariables[v.name] != null) {
-      throw new Error(`Variable with name ${v.name} was already registered`);
-    }
-    this.state.registeredVariables[v.name] = v;
-    this.incRef(v, this.backend);
-    return v;
-  }
-  incRef(a, backend2) {
-    const refCount = this.state.tensorInfo.has(a.dataId) ? this.state.tensorInfo.get(a.dataId).refCount : 0;
-    this.state.numTensors++;
-    if (a.dtype === "string") {
-      this.state.numStringTensors++;
-    }
-    if (refCount === 0) {
-      this.state.numDataBuffers++;
-      let bytes = 0;
-      if (a.dtype !== "complex64" && a.dtype !== "string") {
-        bytes = a.size * bytesPerElement(a.dtype);
-      }
-      this.state.tensorInfo.set(a.dataId, {
-        backend: backend2 || this.backend,
-        dtype: a.dtype,
-        shape: a.shape,
-        bytes,
-        refCount: 0
-      });
-      this.state.numBytes += bytes;
-    }
-    this.state.tensorInfo.get(a.dataId).refCount++;
-    if (!(a instanceof Variable)) {
-      this.track(a);
-    }
-  }
-  disposeTensor(a) {
-    if (!this.state.tensorInfo.has(a.dataId)) {
-      return;
-    }
-    this.state.numTensors--;
-    if (a.dtype === "string") {
-      this.state.numStringTensors--;
-    }
-    const info = this.state.tensorInfo.get(a.dataId);
-    const refCount = info.refCount;
-    if (refCount <= 1) {
-      if (a.dtype !== "complex64") {
-        this.state.numBytes -= info.bytes;
-      }
-      this.state.numDataBuffers--;
-      info.backend.disposeData(a.dataId);
-      this.state.tensorInfo.delete(a.dataId);
-    } else {
-      this.state.tensorInfo.get(a.dataId).refCount--;
-    }
-  }
-  disposeVariables() {
-    for (const varName in this.state.registeredVariables) {
-      const v = this.state.registeredVariables[varName];
-      this.disposeVariable(v);
-    }
-  }
-  disposeVariable(v) {
-    this.disposeTensor(v);
-    if (this.state.registeredVariables[v.name] != null) {
-      delete this.state.registeredVariables[v.name];
-    }
-  }
-  memory() {
-    const info = this.backend.memory();
-    info.numTensors = this.state.numTensors;
-    info.numDataBuffers = this.state.numDataBuffers;
-    info.numBytes = this.state.numBytes;
-    if (this.state.numStringTensors > 0) {
-      info.unreliable = true;
-      if (info.reasons == null) {
-        info.reasons = [];
-      }
-      info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
-    }
-    return info;
-  }
-  async profile(query) {
-    this.state.profiling = true;
-    const startBytes = this.state.numBytes;
-    const startNumTensors = this.state.numTensors;
-    this.state.activeProfile.kernels = [];
-    this.state.activeProfile.result = await query();
-    this.state.profiling = false;
-    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
-    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
-    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
-    for (const kernel of this.state.activeProfile.kernels) {
-      kernel.kernelTimeMs = await kernel.kernelTimeMs;
-      kernel.extraInfo = await kernel.extraInfo;
-    }
-    return this.state.activeProfile;
-  }
-  isTapeOn() {
-    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
-  }
-  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
-    const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      gradientsFunc = gradConfig.gradFunc;
-    }
-    if (gradientsFunc != null) {
-      tapeNode.gradient = (dys) => {
-        dys = dys.map((dy, i) => {
-          if (dy == null) {
-            const output = outputs[i];
-            const vals = makeZerosTypedArray(output.size, output.dtype);
-            return this.makeTensor(vals, output.shape, output.dtype);
-          }
-          return dy;
-        });
-        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
-      };
-    }
-    this.state.activeTape.push(tapeNode);
-  }
-  keep(result) {
-    result.kept = true;
-    return result;
-  }
-  startTape() {
-    if (this.state.gradientDepth === 0) {
-      this.state.activeTape = [];
-    }
-    this.state.gradientDepth++;
-  }
-  endTape() {
-    this.state.gradientDepth--;
-  }
-  startScope(name) {
-    const scopeInfo = {
-      track: [],
-      name: "unnamed scope",
-      id: this.state.nextScopeId++
-    };
-    if (name) {
-      scopeInfo.name = name;
-    }
-    this.state.scopeStack.push(scopeInfo);
-    this.state.activeScope = scopeInfo;
-  }
-  endScope(result) {
-    const tensorsToTrackInParent = getTensorsInContainer(result);
-    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
-    for (let i = 0; i < this.state.activeScope.track.length; i++) {
-      const tensor17 = this.state.activeScope.track[i];
-      if (!tensor17.kept && !tensorsToTrackInParentSet.has(tensor17.id)) {
-        tensor17.dispose();
-      }
-    }
-    const oldScope = this.state.scopeStack.pop();
-    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
-    tensorsToTrackInParent.forEach((tensor17) => {
-      if (!tensor17.kept && tensor17.scopeId === oldScope.id) {
-        this.track(tensor17);
-      }
-    });
-  }
-  gradients(f, xs, dy, allowNoGradients = false) {
-    assert(xs.length > 0, () => "gradients() received an empty list of xs.");
-    if (dy != null && dy.dtype !== "float32") {
-      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
-    }
-    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
-    assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
-    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
-    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
-      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
-    }
-    return this.tidy("backward", () => {
-      const accumulatedGradientMap = {};
-      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
-      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
-      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
-      if (this.state.gradientDepth === 0) {
-        this.state.activeTape.forEach((node2) => {
-          for (const tensor17 of node2.saved) {
-            tensor17.dispose();
-          }
-        });
-        this.state.activeTape = null;
-      }
-      return {value: y, grads: grads2};
-    });
-  }
-  customGrad(f) {
-    assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
-    return (...inputs) => {
-      assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
-      let res;
-      const inputMap = {};
-      inputs.forEach((input, i) => {
-        inputMap[i] = input;
-      });
-      return this.runKernelFunc((_, save) => {
-        res = f(...[...inputs, save]);
-        assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
-        assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
-        return res.value;
-      }, inputMap, (dy, saved) => {
-        const gradRes = res.gradFunc(dy, saved);
-        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
-        assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
-        assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
-        const gradMap = {};
-        grads2.forEach((grad2, i) => {
-          gradMap[i] = () => grad2;
-        });
-        return gradMap;
-      });
-    };
-  }
-  readSync(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.readSync(dataId);
-  }
-  read(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.read(dataId);
-  }
-  async time(query) {
-    const start = now();
-    const timingInfo = await this.backend.time(query);
-    timingInfo.wallMs = now() - start;
-    return timingInfo;
-  }
-  track(result) {
-    if (this.state.activeScope != null) {
-      result.scopeId = this.state.activeScope.id;
-      this.state.activeScope.track.push(result);
-    }
-    return result;
-  }
-  get registeredVariables() {
-    return this.state.registeredVariables;
-  }
-  reset() {
-    this.pendingBackendInitId++;
-    this.state.dispose();
-    this.ENV.reset();
-    this.state = new EngineState();
-    for (const backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    this.backendName = null;
-    this.backendInstance = null;
-    this.pendingBackendInit = null;
-  }
-}
-Engine.nextTensorId = 0;
-Engine.nextVariableId = 0;
-function ones(shape) {
-  const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
-  return ENGINE.makeTensor(values, shape, "float32");
-}
-function getOrMakeEngine() {
-  const ns = getGlobalNamespace();
-  if (ns._tfengine == null) {
-    const environment12 = new Environment(ns);
-    ns._tfengine = new Engine(environment12);
-  }
-  setEnvironmentGlobal(ns._tfengine.ENV);
-  setTensorTracker(() => ns._tfengine);
-  return ns._tfengine;
-}
-const ENGINE = getOrMakeEngine();
-function add(a, b) {
-  const inputs = {a, b};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.add(a, b);
-    save([a, b]);
-    return res;
-  }, inputs, null, Add);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/device_util.js
-const device_util_exports = {};
-__export(device_util_exports, {
-  isBrowser: () => isBrowser,
-  isMobile: () => isMobile
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function _isNavigatorDefined() {
-  return typeof navigator !== "undefined" && navigator != null;
-}
-function isMobile() {
-  if (_isNavigatorDefined()) {
-    const a = navigator.userAgent || navigator.vendor || window.opera;
-    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
-  }
-  return false;
-}
-function isBrowser() {
-  return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/flags.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ENV2 = env();
-ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
-  if (debugValue) {
-    console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
-  }
-});
-ENV2.registerFlag("IS_BROWSER", () => isBrowser());
-ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
-ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-ENV2.registerFlag("PROD", () => false);
-ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
-ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-ENV2.registerFlag("IS_TEST", () => false);
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function inferShape(val, dtype) {
-  let firstElem = val;
-  if (isTypedArray(val)) {
-    return dtype === "string" ? [] : [val.length];
-  }
-  if (!Array.isArray(val)) {
-    return [];
-  }
-  const shape = [];
-  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
-    shape.push(firstElem.length);
-    firstElem = firstElem[0];
-  }
-  if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
-    deepAssertShapeConsistency(val, shape, []);
-  }
-  return shape;
-}
-function deepAssertShapeConsistency(val, shape, indices) {
-  indices = indices || [];
-  if (!Array.isArray(val) && !isTypedArray(val)) {
-    assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
-    return;
-  }
-  assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
-  assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
-  const subShape = shape.slice(1);
-  for (let i = 0; i < val.length; ++i) {
-    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
-  }
-}
-function assertDtype(expectedDtype, actualDType, argName, functionName) {
-  if (expectedDtype == null) {
-    return;
-  }
-  if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
-  }
-}
-function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
-  if (x instanceof Tensor) {
-    assertDtype(parseAsDtype, x.dtype, argName, functionName);
-    return x;
-  }
-  let inferredDtype = inferDtype(x);
-  if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
-    inferredDtype = parseAsDtype;
-  }
-  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
-  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
-    const type = x == null ? "null" : x.constructor.name;
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
-  }
-  const inferredShape = inferShape(x, inferredDtype);
-  if (!isTypedArray(x) && !Array.isArray(x)) {
-    x = [x];
-  }
-  const skipTypedArray = true;
-  const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
-  return ENGINE.makeTensor(values, inferredShape, inferredDtype);
-}
-function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
-  if (!Array.isArray(arg)) {
-    throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
-  }
-  const tensors = arg;
-  return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OP_SCOPE_SUFFIX = "__op";
-function op(f) {
-  const keys = Object.keys(f);
-  if (keys.length !== 1) {
-    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
-  }
-  let opName = keys[0];
-  const fn = f[opName];
-  if (opName.endsWith("_")) {
-    opName = opName.substring(0, opName.length - 1);
-  }
-  opName = opName + OP_SCOPE_SUFFIX;
-  const f2 = (...args) => {
-    ENGINE.startScope(opName);
-    try {
-      const result = fn(...args);
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      ENGINE.endScope(result);
-      return result;
-    } catch (ex) {
-      ENGINE.endScope(null);
-      throw ex;
-    }
-  };
-  Object.defineProperty(f2, "name", {value: opName, configurable: true});
-  return f2;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function complex_(real6, imag6) {
-  const $real = convertToTensor(real6, "real", "complex");
-  const $imag = convertToTensor(imag6, "imag", "complex");
-  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
-  const forward = (backend2) => {
-    return backend2.complex($real, $imag);
-  };
-  const inputs = {real: $real, imag: $imag};
-  return ENGINE.runKernelFunc(forward, inputs, null, Complex);
-}
-const complex = op({complex_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTensor(values, shape, inferredShape, dtype) {
-  if (dtype == null) {
-    dtype = inferDtype(values);
-  }
-  if (dtype === "complex64") {
-    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
-  }
-  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
-    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
-  }
-  if (shape != null) {
-    assertNonNegativeIntegerDimensions(shape);
-    const providedSize = sizeFromShape(shape);
-    const inferredSize = sizeFromShape(inferredShape);
-    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-    for (let i = 0; i < inferredShape.length; ++i) {
-      const inferred = inferredShape[i];
-      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
-      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
-    }
-  }
-  if (!isTypedArray(values) && !Array.isArray(values)) {
-    values = [values];
-  }
-  shape = shape || inferredShape;
-  values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4(values, shape, dtype) {
-  const inferredShape = inferShape(values, dtype);
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/types.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DTYPE_VALUE_SIZE_MAP = {
-  float32: 4,
-  float16: 2,
-  int32: 4,
-  uint16: 2,
-  uint8: 1,
-  bool: 1,
-  complex64: 8
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const NUM_BYTES_STRING_LENGTH = 4;
-async function encodeWeights(tensors, group) {
-  const specs = [];
-  const dataPromises = [];
-  const names = Array.isArray(tensors) ? tensors.map((tensor17) => tensor17.name) : Object.keys(tensors);
-  for (let i = 0; i < names.length; ++i) {
-    const name = names[i];
-    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
-    if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
-      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
-    }
-    const spec = {name, shape: t.shape, dtype: t.dtype};
-    if (t.dtype === "string") {
-      const utf8bytes = new Promise(async (resolve) => {
-        const vals = await t.bytes();
-        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
-        const bytes = new Uint8Array(totalNumBytes);
-        let offset = 0;
-        for (let i2 = 0; i2 < vals.length; i2++) {
-          const val = vals[i2];
-          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
-          bytes.set(bytesOfLength, offset);
-          offset += NUM_BYTES_STRING_LENGTH;
-          bytes.set(val, offset);
-          offset += val.length;
-        }
-        resolve(bytes);
-      });
-      dataPromises.push(utf8bytes);
-    } else {
-      dataPromises.push(t.data());
-    }
-    if (group != null) {
-      spec.group = group;
-    }
-    specs.push(spec);
-  }
-  const tensorValues = await Promise.all(dataPromises);
-  return {data: concatenateTypedArrays(tensorValues), specs};
-}
-function decodeWeights(buffer11, specs) {
-  const out = {};
-  let float16Decode;
-  let offset = 0;
-  for (const spec of specs) {
-    const name = spec.name;
-    const dtype = spec.dtype;
-    const shape = spec.shape;
-    const size = sizeFromShape(shape);
-    let values;
-    if ("quantization" in spec) {
-      const quantization = spec.quantization;
-      if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-        if (!("min" in quantization && "scale" in quantization)) {
-          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
-        }
-      } else if (quantization.dtype === "float16") {
-        if (dtype !== "float32") {
-          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
-        }
-      } else {
-        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      }
-      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * quantizationSizeFactor);
-      const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
-      if (dtype === "float32") {
-        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-          values = new Float32Array(quantizedArray.length);
-          for (let i = 0; i < quantizedArray.length; i++) {
-            const v = quantizedArray[i];
-            values[i] = v * quantization.scale + quantization.min;
-          }
-        } else if (quantization.dtype === "float16") {
-          if (float16Decode === void 0) {
-            float16Decode = getFloat16Decoder();
-          }
-          values = float16Decode(quantizedArray);
-        } else {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
-        }
-      } else if (dtype === "int32") {
-        if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
-        }
-        values = new Int32Array(quantizedArray.length);
-        for (let i = 0; i < quantizedArray.length; i++) {
-          const v = quantizedArray[i];
-          values[i] = Math.round(v * quantization.scale + quantization.min);
-        }
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * quantizationSizeFactor;
-    } else if (dtype === "string") {
-      const size2 = sizeFromShape(spec.shape);
-      values = [];
-      for (let i = 0; i < size2; i++) {
-        const byteLength = new Uint32Array(buffer11.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
-        offset += NUM_BYTES_STRING_LENGTH;
-        const bytes = new Uint8Array(buffer11.slice(offset, offset + byteLength));
-        values.push(bytes);
-        offset += byteLength;
-      }
-    } else {
-      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * dtypeFactor);
-      if (dtype === "float32") {
-        values = new Float32Array(byteBuffer);
-      } else if (dtype === "int32") {
-        values = new Int32Array(byteBuffer);
-      } else if (dtype === "bool") {
-        values = new Uint8Array(byteBuffer);
-      } else if (dtype === "complex64") {
-        values = new Float32Array(byteBuffer);
-        const real6 = new Float32Array(values.length / 2);
-        const image2 = new Float32Array(values.length / 2);
-        for (let i = 0; i < real6.length; i++) {
-          real6[i] = values[i * 2];
-          image2[i] = values[i * 2 + 1];
-        }
-        const realTensor = tensor4(real6, shape, "float32");
-        const imageTensor = tensor4(image2, shape, "float32");
-        out[name] = complex(realTensor, imageTensor);
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * dtypeFactor;
-    }
-    if (dtype !== "complex64") {
-      out[name] = tensor4(values, shape, dtype);
-    }
-  }
-  return out;
-}
-function concatenateTypedArrays(xs) {
-  if (xs === null) {
-    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
-  }
-  let totalByteLength = 0;
-  const normalizedXs = [];
-  xs.forEach((x) => {
-    totalByteLength += x.byteLength;
-    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
-    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
-      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
-    }
-  });
-  const y = new Uint8Array(totalByteLength);
-  let offset = 0;
-  normalizedXs.forEach((x) => {
-    y.set(new Uint8Array(x.buffer), offset);
-    offset += x.byteLength;
-  });
-  return y.buffer;
-}
-const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
-function stringByteLength(str) {
-  if (useNodeBuffer) {
-    return Buffer.byteLength(str);
-  }
-  return new Blob([str]).size;
-}
-function concatenateArrayBuffers(buffers) {
-  if (buffers.length === 1) {
-    return buffers[0];
-  }
-  let totalByteLength = 0;
-  buffers.forEach((buffer11) => {
-    totalByteLength += buffer11.byteLength;
-  });
-  const temp = new Uint8Array(totalByteLength);
-  let offset = 0;
-  buffers.forEach((buffer11) => {
-    temp.set(new Uint8Array(buffer11), offset);
-    offset += buffer11.byteLength;
-  });
-  return temp.buffer;
-}
-function basename(path) {
-  const SEPARATOR = "/";
-  path = path.trim();
-  while (path.endsWith(SEPARATOR)) {
-    path = path.slice(0, path.length - 1);
-  }
-  const items = path.split(SEPARATOR);
-  return items[items.length - 1];
-}
-function getModelArtifactsInfoForJSON(modelArtifacts) {
-  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-    throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  }
-  return {
-    dateSaved: new Date(),
-    modelTopologyType: "JSON",
-    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
-    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
-    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
-  };
-}
-function computeFloat16MantisaTable() {
-  const convertMantissa = (i) => {
-    let m = i << 13;
-    let e = 0;
-    while ((m & 8388608) === 0) {
-      e -= 8388608;
-      m <<= 1;
-    }
-    m &= ~8388608;
-    e += 947912704;
-    return m | e;
-  };
-  const mantisaTable = new Uint32Array(2048);
-  mantisaTable[0] = 0;
-  for (let i = 1; i < 1024; i++) {
-    mantisaTable[i] = convertMantissa(i);
-  }
-  for (let i = 1024; i < 2048; i++) {
-    mantisaTable[i] = 939524096 + (i - 1024 << 13);
-  }
-  return mantisaTable;
-}
-function computeFloat16ExponentTable() {
-  const exponentTable = new Uint32Array(64);
-  exponentTable[0] = 0;
-  exponentTable[31] = 1199570944;
-  exponentTable[32] = 2147483648;
-  exponentTable[63] = 3347054592;
-  for (let i = 1; i < 31; i++) {
-    exponentTable[i] = i << 23;
-  }
-  for (let i = 33; i < 63; i++) {
-    exponentTable[i] = 2147483648 + (i - 32 << 23);
-  }
-  return exponentTable;
-}
-function computeFloat16OffsetTable() {
-  const offsetTable = new Uint32Array(64);
-  for (let i = 0; i < 64; i++) {
-    offsetTable[i] = 1024;
-  }
-  offsetTable[0] = offsetTable[32] = 0;
-  return offsetTable;
-}
-function getFloat16Decoder() {
-  const mantisaTable = computeFloat16MantisaTable();
-  const exponentTable = computeFloat16ExponentTable();
-  const offsetTable = computeFloat16OffsetTable();
-  return (quantizedArray) => {
-    const buffer11 = new ArrayBuffer(4 * quantizedArray.length);
-    const bufferUint32View = new Uint32Array(buffer11);
-    for (let index = 0; index < quantizedArray.length; index++) {
-      const float16Bits = quantizedArray[index];
-      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
-      bufferUint32View[index] = float32Bits;
-    }
-    return new Float32Array(buffer11);
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class IORouterRegistry {
-  constructor() {
-    this.saveRouters = [];
-    this.loadRouters = [];
-  }
-  static getInstance() {
-    if (IORouterRegistry.instance == null) {
-      IORouterRegistry.instance = new IORouterRegistry();
-    }
-    return IORouterRegistry.instance;
-  }
-  static registerSaveRouter(saveRouter) {
-    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
-  }
-  static registerLoadRouter(loadRouter) {
-    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
-  }
-  static getSaveHandlers(url) {
-    return IORouterRegistry.getHandlers(url, "save");
-  }
-  static getLoadHandlers(url, loadOptions) {
-    return IORouterRegistry.getHandlers(url, "load", loadOptions);
-  }
-  static getHandlers(url, handlerType, loadOptions) {
-    const validHandlers = [];
-    const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
-    routers.forEach((router) => {
-      const handler = router(url, loadOptions);
-      if (handler !== null) {
-        validHandlers.push(handler);
-      }
-    });
-    return validHandlers;
-  }
-}
-const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
-const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
-const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
-const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const URL_SCHEME_SUFFIX = "://";
-class ModelStoreManagerRegistry {
-  constructor() {
-    this.managers = {};
-  }
-  static getInstance() {
-    if (ModelStoreManagerRegistry.instance == null) {
-      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
-    }
-    return ModelStoreManagerRegistry.instance;
-  }
-  static registerManager(scheme, manager) {
-    assert(scheme != null, () => "scheme must not be undefined or null.");
-    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
-      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
-    }
-    assert(scheme.length > 0, () => "scheme must not be an empty string.");
-    const registry = ModelStoreManagerRegistry.getInstance();
-    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
-    registry.managers[scheme] = manager;
-  }
-  static getManager(scheme) {
-    const manager = this.getInstance().managers[scheme];
-    if (manager == null) {
-      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
-    }
-    return manager;
-  }
-  static getSchemes() {
-    return Object.keys(this.getInstance().managers);
-  }
-}
-function parseURL(url) {
-  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
-    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
-  }
-  return {
-    scheme: url.split(URL_SCHEME_SUFFIX)[0],
-    path: url.split(URL_SCHEME_SUFFIX)[1]
-  };
-}
-async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
-  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
-  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
-  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
-  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
-  const loadHandler = loadHandlers[0];
-  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
-  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
-  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
-  const saveHandler = saveHandlers[0];
-  const sourceScheme = parseURL(sourceURL).scheme;
-  const sourcePath = parseURL(sourceURL).path;
-  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
-  const modelArtifacts = await loadHandler.load();
-  if (deleteSource && sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  const saveResult = await saveHandler.save(modelArtifacts);
-  if (deleteSource && !sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  return saveResult.modelArtifactsInfo;
-}
-async function listModels() {
-  const schemes = ModelStoreManagerRegistry.getSchemes();
-  const out = {};
-  for (const scheme of schemes) {
-    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
-    for (const path in schemeOut) {
-      const url = scheme + URL_SCHEME_SUFFIX + path;
-      out[url] = schemeOut[path];
-    }
-  }
-  return out;
-}
-async function removeModel(url) {
-  const schemeAndPath = parseURL(url);
-  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
-  return manager.removeModel(schemeAndPath.path);
-}
-async function copyModel(sourceURL, destURL) {
-  const deleteSource = false;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-async function moveModel(sourceURL, destURL) {
-  const deleteSource = true;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function buffer(shape, dtype = "float32", values) {
-  dtype = dtype || "float32";
-  assertNonNegativeIntegerDimensions(shape);
-  return new TensorBuffer(shape, dtype, values);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cast_(x, dtype) {
-  const $x = convertToTensor(x, "x", "cast");
-  if (!isValidDtype(dtype)) {
-    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
-  }
-  if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
-    throw new Error("Only strings can be casted to strings");
-  }
-  const inputs = {x: $x};
-  const attrs = {dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.cast($x, dtype), inputs, null, Cast, attrs);
-}
-const cast = op({cast_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clone_(x) {
-  const $x = convertToTensor(x, "x", "clone", null);
-  const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Identity);
-}
-const clone = op({clone_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/print.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function print(x, verbose = false) {
-  console.log(x.toString(verbose));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-getOrMakeEngine();
-const opHandler2 = {
-  buffer,
-  cast,
-  clone,
-  print
-};
-setOpHandler(opHandler2);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DEFAULT_FILE_NAME_PREFIX = "model";
-const DEFAULT_JSON_EXTENSION_NAME = ".json";
-const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
-function defer(f) {
-  return new Promise((resolve) => setTimeout(resolve)).then(f);
-}
-class BrowserDownloads {
-  constructor(fileNamePrefix) {
-    if (!env().getBool("IS_BROWSER")) {
-      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    }
-    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
-      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
-    }
-    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
-      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
-    }
-    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
-    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
-  }
-  async save(modelArtifacts) {
-    if (typeof document === "undefined") {
-      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
-    }
-    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
-    } else {
-      const weightsManifest = [{
-        paths: ["./" + this.weightDataFileName],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelTopologyAndWeightManifest = {
-        modelTopology: modelArtifacts.modelTopology,
-        format: modelArtifacts.format,
-        generatedBy: modelArtifacts.generatedBy,
-        convertedBy: modelArtifacts.convertedBy,
-        weightsManifest
-      };
-      const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
-      const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
-      jsonAnchor.download = this.modelTopologyFileName;
-      jsonAnchor.href = modelTopologyAndWeightManifestURL;
-      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
-      if (modelArtifacts.weightData != null) {
-        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        weightDataAnchor.download = this.weightDataFileName;
-        weightDataAnchor.href = weightsURL;
-        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
-      }
-      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
-    }
-  }
-}
-BrowserDownloads.URL_SCHEME = "downloads://";
-class BrowserFiles {
-  constructor(files) {
-    if (files == null || files.length < 1) {
-      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
-    }
-    this.files = files;
-  }
-  async load() {
-    const jsonFile = this.files[0];
-    const weightFiles = this.files.slice(1);
-    return new Promise((resolve, reject) => {
-      const jsonReader = new FileReader();
-      jsonReader.onload = (event) => {
-        const modelJSON = JSON.parse(event.target.result);
-        const modelTopology = modelJSON.modelTopology;
-        if (modelTopology == null) {
-          reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        if (weightFiles.length === 0) {
-          resolve({modelTopology});
-        }
-        const weightsManifest = modelJSON.weightsManifest;
-        if (weightsManifest == null) {
-          reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        let pathToFile;
-        try {
-          pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
-        } catch (err) {
-          reject(err);
-          return;
-        }
-        const weightSpecs = [];
-        const paths = [];
-        const perFileBuffers = [];
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            paths.push(path);
-            perFileBuffers.push(null);
-          });
-          weightSpecs.push(...weightsGroup.weights);
-        });
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            const weightFileReader = new FileReader();
-            weightFileReader.onload = (event2) => {
-              const weightData = event2.target.result;
-              const index = paths.indexOf(path);
-              perFileBuffers[index] = weightData;
-              if (perFileBuffers.indexOf(null) === -1) {
-                resolve({
-                  modelTopology,
-                  weightSpecs,
-                  weightData: concatenateArrayBuffers(perFileBuffers),
-                  format: modelJSON.format,
-                  generatedBy: modelJSON.generatedBy,
-                  convertedBy: modelJSON.convertedBy,
-                  userDefinedMetadata: modelJSON.userDefinedMetadata
-                });
-              }
-            };
-            weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
-            weightFileReader.readAsArrayBuffer(pathToFile[path]);
-          });
-        });
-      };
-      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
-      jsonReader.readAsText(jsonFile);
-    });
-  }
-  checkManifestAndWeightFiles(manifest, files) {
-    const basenames = [];
-    const fileNames = files.map((file) => basename(file.name));
-    const pathToFile = {};
-    for (const group of manifest) {
-      group.paths.forEach((path) => {
-        const pathBasename = basename(path);
-        if (basenames.indexOf(pathBasename) !== -1) {
-          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
-        }
-        basenames.push(pathBasename);
-        if (fileNames.indexOf(pathBasename) === -1) {
-          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
-        } else {
-          pathToFile[path] = files[fileNames.indexOf(pathBasename)];
-        }
-      });
-    }
-    if (basenames.length !== files.length) {
-      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
-    }
-    return pathToFile;
-  }
-}
-const browserDownloadsRouter = (url) => {
-  if (!env().getBool("IS_BROWSER")) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
-      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
-function browserDownloads(fileNamePrefix = "model") {
-  return new BrowserDownloads(fileNamePrefix);
-}
-function browserFiles(files) {
-  return new BrowserFiles(files);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/progress.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
-  checkPromises(promises);
-  startFraction = startFraction == null ? 0 : startFraction;
-  endFraction = endFraction == null ? 1 : endFraction;
-  checkFraction(startFraction, endFraction);
-  let resolvedPromise = 0;
-  const registerMonitor = (promise) => {
-    promise.then((value) => {
-      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
-      onProgress(fraction);
-      return value;
-    });
-    return promise;
-  };
-  function checkPromises(promises2) {
-    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
-  }
-  function checkFraction(startFraction2, endFraction2) {
-    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
-    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
-    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
-  }
-  return Promise.all(promises.map(registerMonitor));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
-  if (loadOptions == null) {
-    loadOptions = {};
-  }
-  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
-  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
-  const fetchStartFraction = 0;
-  const fetchEndFraction = 0.5;
-  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
-  const bufferPromises = responses.map((response) => response.arrayBuffer());
-  const bufferStartFraction = 0.5;
-  const bufferEndFraction = 1;
-  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
-  return buffers;
-}
-async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
-  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
-  const loadWeights2 = weightsLoaderFactory(fetchWeights);
-  return loadWeights2(manifest, filePathPrefix, weightNames);
-}
-function weightsLoaderFactory(fetchWeightsFunction) {
-  return async (manifest, filePathPrefix = "", weightNames) => {
-    const groupIndicesToFetchMap = manifest.map(() => false);
-    const groupWeightsToFetch = {};
-    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
-    const allManifestWeightNames = [];
-    manifest.forEach((manifestGroupConfig, groupIndex) => {
-      let groupOffset = 0;
-      manifestGroupConfig.weights.forEach((weightsEntry) => {
-        const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
-        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
-        const enqueueWeightsForFetchingFn = () => {
-          groupIndicesToFetchMap[groupIndex] = true;
-          if (groupWeightsToFetch[groupIndex] == null) {
-            groupWeightsToFetch[groupIndex] = [];
-          }
-          groupWeightsToFetch[groupIndex].push({
-            manifestEntry: weightsEntry,
-            groupOffset,
-            sizeBytes: weightsBytes
-          });
-        };
-        if (weightNames != null) {
-          weightNames.forEach((weightName, weightIndex) => {
-            if (weightName === weightsEntry.name) {
-              enqueueWeightsForFetchingFn();
-              weightsFound[weightIndex] = true;
-            }
-          });
-        } else {
-          enqueueWeightsForFetchingFn();
-        }
-        allManifestWeightNames.push(weightsEntry.name);
-        groupOffset += weightsBytes;
-      });
-    });
-    if (!weightsFound.every((found) => found)) {
-      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
-      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
-    }
-    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
-      if (shouldFetch) {
-        accumulator.push(i);
-      }
-      return accumulator;
-    }, []);
-    const fetchUrls = [];
-    groupIndicesToFetch.forEach((i) => {
-      manifest[i].paths.forEach((filepath) => {
-        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
-        fetchUrls.push(fetchUrl);
-      });
-    });
-    const buffers = await fetchWeightsFunction(fetchUrls);
-    const weightsTensorMap = {};
-    let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach((i) => {
-      const numBuffers = manifest[i].paths.length;
-      let groupBytes = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        groupBytes += buffers[bufferIndexOffset + i2].byteLength;
-      }
-      const groupBuffer = new ArrayBuffer(groupBytes);
-      const groupByteBuffer = new Uint8Array(groupBuffer);
-      let groupBufferOffset = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        const buffer11 = new Uint8Array(buffers[bufferIndexOffset + i2]);
-        groupByteBuffer.set(buffer11, groupBufferOffset);
-        groupBufferOffset += buffer11.byteLength;
-      }
-      const weightsEntries = groupWeightsToFetch[i];
-      weightsEntries.forEach((weightsEntry) => {
-        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
-        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
-        for (const name in nameToTensorMap) {
-          weightsTensorMap[name] = nameToTensorMap[name];
-        }
-      });
-      bufferIndexOffset += numBuffers;
-    });
-    return weightsTensorMap;
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/http.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
-const JSON_TYPE = "application/json";
-class HTTPRequest {
-  constructor(path, loadOptions) {
-    this.DEFAULT_METHOD = "POST";
-    if (loadOptions == null) {
-      loadOptions = {};
-    }
-    this.weightPathPrefix = loadOptions.weightPathPrefix;
-    this.onProgress = loadOptions.onProgress;
-    this.weightUrlConverter = loadOptions.weightUrlConverter;
-    if (loadOptions.fetchFunc != null) {
-      assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
-      this.fetch = loadOptions.fetchFunc;
-    } else {
-      this.fetch = env().platform.fetch;
-    }
-    assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
-    if (Array.isArray(path)) {
-      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
-    }
-    this.path = path;
-    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
-      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
-    }
-    this.requestInit = loadOptions.requestInit || {};
-  }
-  async save(modelArtifacts) {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
-    }
-    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
-    init.body = new FormData();
-    const weightsManifest = [{
-      paths: ["./model.weights.bin"],
-      weights: modelArtifacts.weightSpecs
-    }];
-    const modelTopologyAndWeightManifest = {
-      modelTopology: modelArtifacts.modelTopology,
-      format: modelArtifacts.format,
-      generatedBy: modelArtifacts.generatedBy,
-      convertedBy: modelArtifacts.convertedBy,
-      userDefinedMetadata: modelArtifacts.userDefinedMetadata,
-      weightsManifest
-    };
-    init.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
-    if (modelArtifacts.weightData != null) {
-      init.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
-    }
-    const response = await this.fetch(this.path, init);
-    if (response.ok) {
-      return {
-        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
-        responses: [response]
-      };
-    } else {
-      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
-    }
-  }
-  async load() {
-    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
-    if (!modelConfigRequest.ok) {
-      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
-    }
-    let modelConfig;
-    try {
-      modelConfig = await modelConfigRequest.json();
-    } catch (e) {
-      let message = `Failed to parse model JSON of response from ${this.path}.`;
-      if (this.path.endsWith(".pb")) {
-        message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
-      } else {
-        message += " Please make sure the server is serving valid JSON for this request.";
-      }
-      throw new Error(message);
-    }
-    const modelTopology = modelConfig.modelTopology;
-    const weightsManifest = modelConfig.weightsManifest;
-    const generatedBy = modelConfig.generatedBy;
-    const convertedBy = modelConfig.convertedBy;
-    const format = modelConfig.format;
-    const userDefinedMetadata = modelConfig.userDefinedMetadata;
-    if (modelTopology == null && weightsManifest == null) {
-      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    }
-    let weightSpecs;
-    let weightData;
-    if (weightsManifest != null) {
-      const results = await this.loadWeights(weightsManifest);
-      [weightSpecs, weightData] = results;
-    }
-    return {
-      modelTopology,
-      weightSpecs,
-      weightData,
-      userDefinedMetadata,
-      generatedBy,
-      convertedBy,
-      format
-    };
-  }
-  async loadWeights(weightsManifest) {
-    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
-    const [prefix, suffix] = parseUrl(weightPath);
-    const pathPrefix = this.weightPathPrefix || prefix;
-    const weightSpecs = [];
-    for (const entry of weightsManifest) {
-      weightSpecs.push(...entry.weights);
-    }
-    const fetchURLs = [];
-    const urlPromises = [];
-    for (const weightsGroup of weightsManifest) {
-      for (const path of weightsGroup.paths) {
-        if (this.weightUrlConverter != null) {
-          urlPromises.push(this.weightUrlConverter(path));
-        } else {
-          fetchURLs.push(pathPrefix + path + suffix);
-        }
-      }
-    }
-    if (this.weightUrlConverter) {
-      fetchURLs.push(...await Promise.all(urlPromises));
-    }
-    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
-      requestInit: this.requestInit,
-      fetchFunc: this.fetch,
-      onProgress: this.onProgress
-    });
-    return [weightSpecs, concatenateArrayBuffers(buffers)];
-  }
-}
-HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
-function parseUrl(url) {
-  const lastSlash = url.lastIndexOf("/");
-  const lastSearchParam = url.lastIndexOf("?");
-  const prefix = url.substring(0, lastSlash);
-  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
-  return [prefix + "/", suffix];
-}
-function isHTTPScheme(url) {
-  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
-}
-const httpRouter = (url, loadOptions) => {
-  if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
-    return null;
-  } else {
-    let isHTTP = true;
-    if (Array.isArray(url)) {
-      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
-    } else {
-      isHTTP = isHTTPScheme(url);
-    }
-    if (isHTTP) {
-      return http(url, loadOptions);
-    }
-  }
-  return null;
-};
-IORouterRegistry.registerSaveRouter(httpRouter);
-IORouterRegistry.registerLoadRouter(httpRouter);
-function http(path, loadOptions) {
-  return new HTTPRequest(path, loadOptions);
-}
-function browserHTTPRequest(path, loadOptions) {
-  return http(path, loadOptions);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class PassthroughLoader {
-  constructor(modelArtifacts) {
-    this.modelArtifacts = modelArtifacts;
-  }
-  async load() {
-    return this.modelArtifacts;
-  }
-}
-class PassthroughSaver {
-  constructor(saveHandler) {
-    this.saveHandler = saveHandler;
-  }
-  async save(modelArtifacts) {
-    return this.saveHandler(modelArtifacts);
-  }
-}
-function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
-  if (arguments.length === 1) {
-    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
-    if (isModelArtifacts) {
-      return new PassthroughLoader(modelArtifacts);
-    } else {
-      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-      return new PassthroughLoader({modelTopology: modelArtifacts});
-    }
-  } else {
-    console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-    return new PassthroughLoader({
-      modelTopology: modelArtifacts,
-      weightSpecs,
-      weightData,
-      trainingConfig
-    });
-  }
-}
-function withSaveHandler(saveHandler) {
-  return new PassthroughSaver(saveHandler);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io.js
-const io_exports = {};
-__export(io_exports, {
-  browserFiles: () => browserFiles,
-  browserHTTPRequest: () => browserHTTPRequest,
-  concatenateArrayBuffers: () => concatenateArrayBuffers,
-  copyModel: () => copyModel,
-  decodeWeights: () => decodeWeights,
-  encodeWeights: () => encodeWeights,
-  fromMemory: () => fromMemory,
-  getLoadHandlers: () => getLoadHandlers,
-  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
-  getSaveHandlers: () => getSaveHandlers,
-  http: () => http,
-  isHTTPScheme: () => isHTTPScheme,
-  listModels: () => listModels,
-  loadWeights: () => loadWeights,
-  moveModel: () => moveModel,
-  registerLoadRouter: () => registerLoadRouter,
-  registerSaveRouter: () => registerSaveRouter,
-  removeModel: () => removeModel,
-  weightsLoaderFactory: () => weightsLoaderFactory,
-  withSaveHandler: () => withSaveHandler
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reshape_(x, shape) {
-  const $x = convertToTensor(x, "x", "reshape", null);
-  shape = inferFromImplicitShape(shape, $x.size);
-  assert($x.size === sizeFromShape(shape), () => "new shape and old shape must have the same number of elements.");
-  const inputs = {x: $x};
-  const attrs = {shape};
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.reshape($x, shape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Reshape, attrs);
-}
-const reshape = op({reshape_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function matMul_(a, b, transposeA = false, transposeB = false) {
-  let $a = convertToTensor(a, "a", "matMul");
-  let $b = convertToTensor(b, "b", "matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  const forward = (backend2, save) => {
-    save([a3D, b3D]);
-    return backend2.batchMatMul(a3D, b3D, transposeA, transposeB);
-  };
-  const inputs = {a: a3D, b: b3D};
-  const attrs = {transposeA, transposeB};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, BatchMatMul, attrs);
-  return reshape(res, outShape);
-}
-const matMul = op({matMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function oneHot_(indices, depth, onValue = 1, offValue = 0) {
-  if (depth < 2) {
-    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
-  }
-  const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
-  const outShape = [...$indices.shape, depth];
-  const forward = (backend2, save) => {
-    save([$indices]);
-    return reshape(backend2.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);
-  };
-  const inputs = {indices: $indices};
-  const attrs = {depth, onValue, offValue};
-  return ENGINE.runKernelFunc(forward, inputs, null, OneHot, attrs);
-}
-const oneHot = op({oneHot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function transpose_(x, perm) {
-  const $x = convertToTensor(x, "x", "transpose");
-  if (perm == null) {
-    perm = $x.shape.map((s, i) => i).reverse();
-  }
-  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
-  perm.forEach((axis) => {
-    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
-  });
-  if ($x.rank <= 1) {
-    return $x.clone();
-  }
-  const inputs = {x: $x};
-  const attrs = {perm};
-  return ENGINE.runKernelFunc((backend2) => backend2.transpose($x, perm), inputs, null, Transpose, attrs);
-}
-const transpose = op({transpose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function confusionMatrix_(labels, predictions, numClasses) {
-  const $labels = convertToTensor(labels, "labels", "confusionMatrix");
-  const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
-  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
-  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
-  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
-  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
-  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
-  const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
-  const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
-  const oneHotLabelsT = transpose(oneHotLabels);
-  return cast(matMul(oneHotLabelsT, oneHotPredictions), "int32");
-}
-const confusionMatrix = op({confusionMatrix_});
-
-// node_modules/@tensorflow/tfjs-core/dist/math.js
-const math_exports = {};
-__export(math_exports, {
-  confusionMatrix: () => confusionMatrix
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor3d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 3) {
-    throw new Error("tensor3d() requires shape to have three numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
-    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
-const browser_exports = {};
-__export(browser_exports, {
-  fromPixels: () => fromPixels,
-  toPixels: () => toPixels
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let fromPixels2DContext;
-function fromPixels_(pixels, numChannels = 3) {
-  if (numChannels > 4) {
-    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
-  }
-  if (pixels == null) {
-    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-  }
-  let isPixelData = false;
-  let isImageData = false;
-  let isVideo = false;
-  let isImage = false;
-  let isCanvasLike = false;
-  if (pixels.data instanceof Uint8Array) {
-    isPixelData = true;
-  } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
-    isImageData = true;
-  } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
-    isVideo = true;
-  } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
-    isImage = true;
-  } else if (pixels.getContext != null) {
-    isCanvasLike = true;
-  } else {
-    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
-  }
-  if (isVideo) {
-    const HAVE_CURRENT_DATA_READY_STATE = 2;
-    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
-      throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-    }
-  }
-  const kernel = getKernel(FromPixels, ENGINE.backendName);
-  if (kernel != null) {
-    const inputs = {pixels};
-    const attrs = {numChannels};
-    return ENGINE.runKernel(FromPixels, inputs, attrs);
-  }
-  const [width, height] = isVideo ? [
-    pixels.videoWidth,
-    pixels.videoHeight
-  ] : [pixels.width, pixels.height];
-  let vals;
-  if (isCanvasLike) {
-    vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
-  } else if (isImageData || isPixelData) {
-    vals = pixels.data;
-  } else if (isImage || isVideo) {
-    if (fromPixels2DContext == null) {
-      fromPixels2DContext = document.createElement("canvas").getContext("2d");
-    }
-    fromPixels2DContext.canvas.width = width;
-    fromPixels2DContext.canvas.height = height;
-    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
-    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
-  }
-  let values;
-  if (numChannels === 4) {
-    values = new Int32Array(vals);
-  } else {
-    const numPixels = width * height;
-    values = new Int32Array(numPixels * numChannels);
-    for (let i = 0; i < numPixels; i++) {
-      for (let channel = 0; channel < numChannels; ++channel) {
-        values[i * numChannels + channel] = vals[i * 4 + channel];
-      }
-    }
-  }
-  const outShape = [height, width, numChannels];
-  return tensor3d(values, outShape, "int32");
-}
-async function toPixels(img, canvas) {
-  let $img = convertToTensor(img, "img", "toPixels");
-  if (!(img instanceof Tensor)) {
-    const originalImgTensor = $img;
-    $img = cast(originalImgTensor, "int32");
-    originalImgTensor.dispose();
-  }
-  if ($img.rank !== 2 && $img.rank !== 3) {
-    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
-  }
-  const [height, width] = $img.shape.slice(0, 2);
-  const depth = $img.rank === 2 ? 1 : $img.shape[2];
-  if (depth > 4 || depth === 2) {
-    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
-  }
-  if ($img.dtype !== "float32" && $img.dtype !== "int32") {
-    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
-  }
-  const data = await $img.data();
-  const multiplier = $img.dtype === "float32" ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255];
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-      if ($img.dtype === "float32") {
-        if (value < 0 || value > 1) {
-          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
-        }
-      } else if ($img.dtype === "int32") {
-        if (value < 0 || value > 255) {
-          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
-        }
-      }
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-  if (canvas != null) {
-    canvas.width = width;
-    canvas.height = height;
-    const ctx = canvas.getContext("2d");
-    const imageData = new ImageData(bytes, width, height);
-    ctx.putImageData(imageData, 0, 0);
-  }
-  if ($img !== img) {
-    $img.dispose();
-  }
-  return bytes;
-}
-const fromPixels = op({fromPixels_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
-const gather_nd_util_exports = {};
-__export(gather_nd_util_exports, {
-  prepareAndValidate: () => prepareAndValidate
-});
-function prepareAndValidate(tensor17, indices) {
-  if (tensor17.rank < 1) {
-    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor17.rank}.`);
-  }
-  if (indices.rank < 1) {
-    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
-  }
-  if (indices.shape[indices.rank - 1] > tensor17.rank) {
-    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank - 1]} vs. ${tensor17.rank}`);
-  }
-  if (tensor17.size === 0) {
-    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor17.shape}.`);
-  }
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-  let nResult = 1;
-  for (let i = 0; i < indicesShape.length - 1; ++i) {
-    nResult *= indicesShape[i];
-  }
-  const inputShape = tensor17.shape;
-  const resultShape = indicesShape.slice();
-  resultShape.pop();
-  let sliceSize = 1;
-  for (let i = sliceRank; i < tensor17.rank; ++i) {
-    sliceSize *= inputShape[i];
-    resultShape.push(inputShape[i]);
-  }
-  const strides = [
-    ...computeStrides(tensor17.shape).map((stride) => stride / sliceSize),
-    1
-  ].slice(0, sliceRank);
-  return [resultShape, nResult, sliceSize, strides];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
-const scatter_nd_util_exports = {};
-__export(scatter_nd_util_exports, {
-  calculateShapes: () => calculateShapes,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape
-});
-function validateUpdateShape(shape, indices, updates) {
-  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
-  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
-  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
-  if (updates.rank < batchDim) {
-    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
-  }
-  if (shape.length < sliceDim + (updates.rank - batchDim)) {
-    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
-  }
-  if (updates.rank !== batchDim + shape.length - sliceDim) {
-    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
-  }
-  for (let d = 0; d < batchDim; ++d) {
-    if (updates.shape[d] !== indices.shape[d]) {
-      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
-    }
-  }
-  for (let d = 0; d < updates.rank - batchDim; ++d) {
-    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
-      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
-    }
-  }
-}
-function validateInput(updates, indices, shape) {
-  if (indices.rank < 1) {
-    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (updates.rank < 1) {
-    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
-  }
-  if (shape.length < 1) {
-    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
-  }
-  if (shape.length === 0) {
-    if (indices.size === 0) {
-      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
-    }
-    if (updates.size === 0) {
-      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
-    }
-  }
-  validateUpdateShape(shape, indices, updates);
-}
-function calculateShapes(updates, indices, shape) {
-  const indicesRank = indices.shape.length;
-  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
-  const totalNd = shape.length;
-  let sliceSize = 1;
-  for (let i = sliceRank; i < totalNd; ++i) {
-    sliceSize *= shape[i];
-  }
-  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
-  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
-  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
-  const outputSize = sizeFromShape(shape);
-  return {sliceRank, numUpdates, sliceSize, strides, outputSize};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
-const slice_util_exports = {};
-__export(slice_util_exports, {
-  assertParamsValid: () => assertParamsValid,
-  computeFlatOffset: () => computeFlatOffset,
-  computeOutShape: () => computeOutShape,
-  getNormalizedAxes: () => getNormalizedAxes,
-  isSliceContinous: () => isSliceContinous,
-  maskToAxes: () => maskToAxes,
-  parseSliceParams: () => parseSliceParams,
-  startForAxis: () => startForAxis,
-  startIndicesWithElidedDims: () => startIndicesWithElidedDims,
-  stopForAxis: () => stopForAxis,
-  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
-  stridesForAxis: () => stridesForAxis,
-  stridesWithElidedDims: () => stridesWithElidedDims
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsValid(input, begin, size) {
-  assert(input.rank === begin.length, () => `Error in slice${input.rank}D: Length of begin ${begin} must match the rank of the array (${input.rank}).`);
-  assert(input.rank === size.length, () => `Error in slice${input.rank}D: Length of size ${size} must match the rank of the array (${input.rank}).`);
-  for (let i = 0; i < input.rank; ++i) {
-    assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${input.rank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);
-  }
-}
-function maskToAxes(mask) {
-  const axes = [];
-  let axis = 0;
-  while (mask > 0) {
-    if (mask & 1) {
-      axes.push(axis);
-    }
-    mask /= 2;
-    axis++;
-  }
-  return axes;
-}
-function computeOutShape(begin, end, strides) {
-  const size = [];
-  for (let axis = 0; axis < begin.length; axis++) {
-    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
-  }
-  return size;
-}
-function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
-  const newStrides = [...strides];
-  for (let i = newStrides.length; i < inputShape.length; i++) {
-    newStrides.push(1);
-  }
-  for (let i = 0; i < numElidedAxes; i++) {
-    if (i === 0) {
-      newStrides[ellipsisInsertionIndex] = 1;
-    } else {
-      newStrides.splice(ellipsisInsertionIndex, 0, 1);
-      newStrides.pop();
-    }
-  }
-  return newStrides;
-}
-function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
-  if (normalizedAxis <= ellipsisInsertionIndex) {
-    return normalizedAxis;
-  }
-  return normalizedAxis - (numElidedAxes - 1);
-}
-function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
-  const elidedAxes = [];
-  for (let i = 0; i < numElidedAxes; i++) {
-    elidedAxes.push(ellipsisInsertionIndex + i);
-  }
-  return elidedAxes;
-}
-function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
-  const inputRank = inputShape.length;
-  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
-  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
-    const fullIndex = ellipsisAxes[0];
-    const numElidedAxes = numInterpolatedAxes + 1;
-    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
-    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
-    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
-  } else {
-    for (let axis = 0; axis < inputRank; axis++) {
-      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
-      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
-      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
-    }
-  }
-  return {
-    begin: normalizedBegin,
-    end: normalizedEnd,
-    strides: normalizedStrides
-  };
-}
-function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = 0;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalBegin[originalAxis];
-      if (beginMask & 1 << originalAxis) {
-        originalValue = 0;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  return newIndices;
-}
-function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = Number.MAX_SAFE_INTEGER;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalEnd[originalAxis];
-      if (endMask & 1 << originalAxis) {
-        originalValue = Number.MAX_SAFE_INTEGER;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  for (let i = 0; i < newIndices.length; i++) {
-    const axisSize = inputShape[i];
-    if (newIndices[i] < 0) {
-      newIndices[i] += axisSize;
-    }
-    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
-  }
-  return newIndices;
-}
-function stridesForAxis(strides, axis, ellipsisMask) {
-  let stride = strides[axis];
-  if (ellipsisMask & 1 << axis || stride == null) {
-    stride = 1;
-  }
-  return stride;
-}
-function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
-  let start = startIndices[axis];
-  const stride = strides[axis] || 1;
-  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
-    if (stride > 0) {
-      start = Number.MIN_SAFE_INTEGER;
-    } else {
-      start = Number.MAX_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (start < 0) {
-    start += axisSize;
-  }
-  start = clamp(0, start, axisSize - 1);
-  return start;
-}
-function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
-  let stop = stopIndices[axis];
-  const stride = strides[axis] || 1;
-  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
-    if (stride > 0) {
-      stop = Number.MAX_SAFE_INTEGER;
-    } else {
-      stop = Number.MIN_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (stop < 0) {
-    stop += axisSize;
-  }
-  if (stride > 0) {
-    stop = clamp(0, stop, axisSize);
-  } else {
-    stop = clamp(-1, stop, axisSize - 1);
-  }
-  return stop;
-}
-function isSliceContinous(shape, begin, size) {
-  let firstNonOneAxis = size.length;
-  for (let i = 0; i < size.length; i++) {
-    if (size[i] > 1) {
-      firstNonOneAxis = i;
-      break;
-    }
-  }
-  for (let i = firstNonOneAxis + 1; i < size.length; i++) {
-    if (begin[i] > 0 || size[i] !== shape[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function computeFlatOffset(begin, strides) {
-  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i = 0; i < begin.length - 1; i++) {
-    flatOffset += begin[i] * strides[i];
-  }
-  return flatOffset;
-}
-function parseSliceParams(x, begin, size) {
-  let begin_;
-  if (typeof begin === "number") {
-    begin_ = [begin, ...new Array(x.rank - 1).fill(0)];
-  } else if (begin.length < x.rank) {
-    begin_ = begin.concat(new Array(x.rank - begin.length).fill(0));
-  } else {
-    begin_ = begin.slice();
-  }
-  begin_.forEach((d) => {
-    assert(d !== -1, () => "slice() does not support negative begin indexing.");
-  });
-  let size_;
-  if (size == null) {
-    size_ = new Array(x.rank).fill(-1);
-  } else if (typeof size === "number") {
-    size_ = [size, ...new Array(x.rank - 1).fill(-1)];
-  } else if (size.length < x.rank) {
-    size_ = size.concat(new Array(x.rank - size.length).fill(-1));
-  } else {
-    size_ = size;
-  }
-  size_ = size_.map((d, i) => {
-    if (d >= 0) {
-      return d;
-    } else {
-      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
-      return x.shape[i] - begin_[i];
-    }
-  });
-  return [begin_, size_];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/serialization.js
-const serialization_exports = {};
-__export(serialization_exports, {
-  Serializable: () => Serializable,
-  SerializationMap: () => SerializationMap,
-  registerClass: () => registerClass
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Serializable {
-  getClassName() {
-    return this.constructor.className;
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2);
-  }
-}
-class SerializationMap {
-  constructor() {
-    this.classNameMap = {};
-  }
-  static getMap() {
-    if (SerializationMap.instance == null) {
-      SerializationMap.instance = new SerializationMap();
-    }
-    return SerializationMap.instance;
-  }
-  static register(cls) {
-    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
-  }
-}
-function registerClass(cls) {
-  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
-  assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
-  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
-  SerializationMap.register(cls);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/test_util.js
-const test_util_exports = {};
-__export(test_util_exports, {
-  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
-  expectArrayBuffersEqual: () => expectArrayBuffersEqual,
-  expectArraysClose: () => expectArraysClose,
-  expectArraysEqual: () => expectArraysEqual,
-  expectNumbersClose: () => expectNumbersClose,
-  expectPromiseToFail: () => expectPromiseToFail,
-  expectValuesInRange: () => expectValuesInRange,
-  testEpsilon: () => testEpsilon
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TEST_EPSILON_FLOAT32 = 1e-3;
-const TEST_EPSILON_FLOAT16 = 0.1;
-function expectArraysClose(actual, expected, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon2));
-}
-function testEpsilon() {
-  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
-}
-function expectArraysPredicate(actual, expected, predicate) {
-  let checkClassType = true;
-  if (isTypedArray(actual) || isTypedArray(expected)) {
-    checkClassType = false;
-  }
-  if (isTypedArray(actual) && isTypedArray(expected)) {
-    checkClassType = true;
-  }
-  if (checkClassType) {
-    const aType = actual.constructor.name;
-    const bType = expected.constructor.name;
-    if (aType !== bType) {
-      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
-    }
-  }
-  if (Array.isArray(actual) && Array.isArray(expected)) {
-    const actualShape = inferShape(actual);
-    const expectedShape = inferShape(expected);
-    if (!arraysEqual(actualShape, expectedShape)) {
-      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
-    }
-  }
-  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
-  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
-  if (actualFlat.length !== expectedFlat.length) {
-    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-  }
-  for (let i = 0; i < expectedFlat.length; ++i) {
-    const a = actualFlat[i];
-    const e = expectedFlat[i];
-    if (!predicate(a, e)) {
-      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-    }
-  }
-}
-function expectPromiseToFail(fn, done) {
-  fn().then(() => done.fail(), () => done());
-}
-function expectArraysEqual(actual, expected) {
-  const exp11 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
-  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
-    return expectArraysPredicate(actual, exp11, (a, b) => a == b);
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
-}
-function expectNumbersClose(a, e, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  if (!areClose(a, e, epsilon2)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
-  }
-}
-function areClose(a, e, epsilon2) {
-  if (!isFinite(a) && !isFinite(e)) {
-    return true;
-  }
-  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon2) {
-    return false;
-  }
-  return true;
-}
-function expectValuesInRange(actual, low, high) {
-  for (let i = 0; i < actual.length; i++) {
-    if (actual[i] < low || actual[i] > high) {
-      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
-    }
-  }
-}
-function expectArrayBuffersEqual(actual, expected) {
-  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/version.js
-/** @license See the LICENSE file. */
-const version = "2.4.0";
-
-// node_modules/@tensorflow/tfjs-core/dist/globals.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enableProdMode() {
-  env().set("PROD", true);
-}
-function enableDebugMode() {
-  env().set("DEBUG", true);
-}
-function disableDeprecationWarnings() {
-  env().set("DEPRECATION_WARNINGS_ENABLED", false);
-  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
-}
-function deprecationWarn(msg) {
-  if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
-    console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
-  }
-}
-setDeprecationWarningFn(deprecationWarn);
-function disposeVariables() {
-  ENGINE.disposeVariables();
-}
-function engine15() {
-  return ENGINE;
-}
-function memory() {
-  return ENGINE.memory();
-}
-function profile(f) {
-  return ENGINE.profile(f);
-}
-function tidy(nameOrFn, fn) {
-  return ENGINE.tidy(nameOrFn, fn);
-}
-function dispose(container) {
-  const tensors = getTensorsInContainer(container);
-  tensors.forEach((tensor17) => tensor17.dispose());
-}
-function keep(result) {
-  return ENGINE.keep(result);
-}
-function time(f) {
-  return ENGINE.time(f);
-}
-function setBackend(backendName) {
-  return ENGINE.setBackend(backendName);
-}
-function ready() {
-  return ENGINE.ready();
-}
-function getBackend() {
-  return ENGINE.backendName;
-}
-function removeBackend(name) {
-  ENGINE.removeBackend(name);
-}
-function findBackend(name) {
-  return ENGINE.findBackend(name);
-}
-function findBackendFactory(name) {
-  return ENGINE.findBackendFactory(name);
-}
-function registerBackend(name, factory, priority = 1) {
-  return ENGINE.registerBackend(name, factory, priority);
-}
-function backend() {
-  return ENGINE.backend;
-}
-function setPlatform(platformName, platform) {
-  env().setPlatform(platformName, platform);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function add_(a, b) {
-  let $a = convertToTensor(a, "a", "add");
-  let $b = convertToTensor(b, "b", "add");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.add($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Add);
-}
-const add2 = op({add_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floorDiv_(a, b) {
-  let $a = convertToTensor(a, "a", "floorDiv");
-  let $b = convertToTensor(b, "b", "floorDiv");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.floorDiv($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, FloorDiv);
-}
-const floorDiv = op({floorDiv_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function div_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "int32" && $b.dtype === "int32") {
-    return floorDiv($a, $b);
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.realDivide($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, Div, attrs);
-}
-const div = op({div_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mul_(a, b) {
-  let $a = convertToTensor(a, "a", "mul");
-  let $b = convertToTensor(b, "b", "mul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.multiply($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Multiply);
-}
-const mul = op({mul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function abs_(x) {
-  const $x = convertToTensor(x, "x", "abs");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "complex64") {
-      return backend2.complexAbs($x);
-    }
-    return backend2.abs($x);
-  }, inputs, null, Abs);
-}
-const abs = op({abs_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acos_(x) {
-  const $x = convertToTensor(x, "x", "acos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acos);
-}
-const acos = op({acos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acosh_(x) {
-  const $x = convertToTensor(x, "x", "acosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acosh);
-}
-const acosh = op({acosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addN_(tensors) {
-  assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
-  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
-  const firstTensor = $tensors[0];
-  $tensors.forEach((t) => {
-    if (t.dtype !== firstTensor.dtype) {
-      throw new Error("All tensors passed to tf.addN() must have the same dtype");
-    }
-  });
-  $tensors.forEach((t) => {
-    if (!arraysEqual(t.shape, firstTensor.shape)) {
-      throw new Error("All tensors passed to tf.addN() must have the same shape");
-    }
-  });
-  const forward = (backend2, save) => {
-    const res = backend2.addN($tensors);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  return ENGINE.runKernelFunc(forward, inputs, null, AddN);
-}
-const addN = op({addN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function axesAreInnerMostDims(axes, rank) {
-  for (let i = 0; i < axes.length; ++i) {
-    if (axes[axes.length - i - 1] !== rank - 1 - i) {
-      return false;
-    }
-  }
-  return true;
-}
-function combineLocations(outputLoc, reduceLoc, axes) {
-  const rank = outputLoc.length + reduceLoc.length;
-  const loc = [];
-  let outIdx = 0;
-  let reduceIdx = 0;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      loc.push(outputLoc[outIdx++]);
-    } else {
-      loc.push(reduceLoc[reduceIdx++]);
-    }
-  }
-  return loc;
-}
-function computeOutAndReduceShapes(aShape, axes) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      outShape.push(aShape[dim]);
-    }
-  }
-  const reduceShape = axes.map((dim) => aShape[dim]);
-  return [outShape, reduceShape];
-}
-function expandShapeToKeepDim(shape, axes) {
-  const reduceSubShape = axes.map((x) => 1);
-  return combineLocations(shape, reduceSubShape, axes);
-}
-function assertAxesAreInnerMostDims(msg, axes, rank) {
-  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
-}
-function getAxesPermutation(axes, rank) {
-  if (axesAreInnerMostDims(axes, rank)) {
-    return null;
-  }
-  const result = [];
-  for (let i = 0; i < rank; ++i) {
-    if (axes.indexOf(i) === -1) {
-      result.push(i);
-    }
-  }
-  axes.forEach((axis) => result.push(axis));
-  return result;
-}
-function getUndoAxesPermutation(axes) {
-  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
-}
-function getInnerMostAxes(numAxes, rank) {
-  const res = [];
-  for (let i = rank - numAxes; i < rank; ++i) {
-    res.push(i);
-  }
-  return res;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/all.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function all_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "all", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.all($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, All, attrs);
-}
-const all = op({all_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/any.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function any_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "any", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.any($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Any, attrs);
-}
-const any = op({any_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMax_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMax");
-  const forward = (backend2, save) => {
-    save([$x]);
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMax($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMax, attrs);
-}
-const argMax = op({argMax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMin_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMin");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if (axis == null) {
-      axis = 0;
-    }
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMin($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMin, attrs);
-}
-const argMin = op({argMin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asin_(x) {
-  const $x = convertToTensor(x, "x", "asin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asin);
-}
-const asin = op({asin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asinh_(x) {
-  const $x = convertToTensor(x, "x", "asinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asinh);
-}
-const asinh = op({asinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan_(x) {
-  const $x = convertToTensor(x, "x", "atan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atan);
-}
-const atan = op({atan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan2_(a, b) {
-  let $a = convertToTensor(a, "a", "atan2");
-  let $b = convertToTensor(b, "b", "atan2");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.atan2($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Atan2);
-}
-const atan2 = op({atan2_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atanh_(x) {
-  const $x = convertToTensor(x, "x", "atanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atanh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atanh);
-}
-const atanh = op({atanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function computeDilation2DInfo(inputShape, filterShape, strides, pad8, dataFormat = "NHWC", dilations) {
-  const inputChannels = inputShape[3];
-  const $filterShape = [...filterShape, inputChannels];
-  const $dataFormat = convertConv2DDataFormat(dataFormat);
-  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad8, null, null, $dataFormat);
-}
-function computePool2DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "channelsLast") {
-  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
-  let filterShape;
-  if (dataFormat === "channelsLast") {
-    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
-  } else if (dataFormat === "channelsFirst") {
-    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, false, dataFormat);
-}
-function computePool3DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "NDHWC") {
-  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
-  let filterShape;
-  let $dataFormat;
-  if (dataFormat === "NDHWC") {
-    $dataFormat = "channelsLast";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
-  } else if (dataFormat === "NCDHW") {
-    $dataFormat = "channelsFirst";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, false, $dataFormat, roundingMode);
-}
-function computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, depthwise = false, dataFormat = "channelsLast") {
-  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideHeight, strideWidth] = parseTupleParam(strides);
-  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inHeight,
-    inWidth,
-    inChannels,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideHeight,
-    strideWidth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, depthwise = false, dataFormat = "channelsLast", roundingMode) {
-  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
-  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
-  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inDepth,
-    inHeight,
-    inWidth,
-    inChannels,
-    outDepth,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideDepth,
-    strideHeight,
-    strideWidth,
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterDepth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationDepth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputRows = inShape[0];
-  const inputCols = inShape[1];
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputRows, outputCols];
-}
-function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputDepth = inShape[0];
-  const inputRows = inShape[1];
-  const inputCols = inShape[2];
-  const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputDepths, outputRows, outputCols, outChannels];
-}
-function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
-  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
-  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
-}
-function parseTupleParam(param) {
-  if (typeof param === "number") {
-    return [param, param, param];
-  }
-  if (param.length === 2) {
-    return [param[0], param[1], 1];
-  }
-  return param;
-}
-function parse3TupleParam(param) {
-  return typeof param === "number" ? [param, param, param] : param;
-}
-function getEffectiveFilterSize(filterSize, dilation) {
-  if (dilation <= 1) {
-    return filterSize;
-  }
-  return filterSize + (filterSize - 1) * (dilation - 1);
-}
-function getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
-  let padInfo;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {top: pad8, bottom: pad8, left: pad8, right: pad8, type: padType};
-    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad8, roundingMode);
-    outHeight = outShape[0];
-    outWidth = outShape[1];
-  } else if (pad8 === "same") {
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
-    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else if (typeof pad8 === "object") {
-    const top = dataFormat === "channelsLast" ? pad8[1][0] : pad8[2][0];
-    const bottom = dataFormat === "channelsLast" ? pad8[1][1] : pad8[2][1];
-    const left = dataFormat === "channelsLast" ? pad8[2][0] : pad8[3][0];
-    const right = dataFormat === "channelsLast" ? pad8[2][1] : pad8[3][1];
-    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
-    padInfo = {top, bottom, left, right, type: padType};
-    outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
-    outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outHeight, outWidth};
-}
-function get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
-  let padInfo;
-  let outDepth;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {
-      top: pad8,
-      bottom: pad8,
-      left: pad8,
-      right: pad8,
-      front: pad8,
-      back: pad8,
-      type: padType
-    };
-    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad8, roundingMode);
-    outDepth = outShape[0];
-    outHeight = outShape[1];
-    outWidth = outShape[2];
-  } else if (pad8 === "same") {
-    outDepth = Math.ceil(inDepth / strideDepth);
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
-    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
-    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
-    const front = Math.floor(padAlongDepth / 2);
-    const back = padAlongDepth - front;
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, front, back, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {
-      top: 0,
-      bottom: 0,
-      left: 0,
-      right: 0,
-      front: 0,
-      back: 0,
-      type: "VALID"
-    };
-    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outDepth, outHeight, outWidth};
-}
-function conditionalRound(value, roundingMode) {
-  if (!roundingMode) {
-    return value;
-  }
-  switch (roundingMode) {
-    case "round":
-      return Math.round(value);
-    case "ceil":
-      return Math.ceil(value);
-    case "floor":
-      return Math.floor(value);
-    default:
-      throw new Error(`Unknown roundingMode ${roundingMode}`);
-  }
-}
-function tupleValuesAreOne(param) {
-  const [dimA, dimB, dimC] = parseTupleParam(param);
-  return dimA === 1 && dimB === 1 && dimC === 1;
-}
-function eitherStridesOrDilationsAreOne(strides, dilations) {
-  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
-}
-function convertConv2DDataFormat(dataFormat) {
-  if (dataFormat === "NHWC") {
-    return "channelsLast";
-  } else if (dataFormat === "NCHW") {
-    return "channelsFirst";
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "avgPool", "float32");
-  const dilations = 1;
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    save([x4D]);
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      return x4D.clone();
-    }
-    return backend2.avgPool(x4D, convInfo);
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool, attrs);
-  res = cast(res, $x.dtype);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPool = op({avgPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3d_(x, filterSize, strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "avgPool3d", "float32");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    save([x5D]);
-    return backend2.avgPool3d(x5D, convInfo);
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3D, attrs);
-  res = cast(res, x5D.dtype);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3d = op({avgPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsConsistent(shapes, axis) {
-  const rank = shapes[0].length;
-  shapes.forEach((shape, i) => {
-    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
-  });
-  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
-  const firstShape = shapes[0];
-  shapes.forEach((shape, i) => {
-    for (let r = 0; r < rank; r++) {
-      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
-    }
-  });
-}
-function computeOutShape2(shapes, axis) {
-  const outputShape = shapes[0].slice();
-  for (let i = 1; i < shapes.length; i++) {
-    outputShape[axis] += shapes[i][axis];
-  }
-  return outputShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function concat_(tensors, axis = 0) {
-  assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
-  let $tensors = convertToTensorArray(tensors, "tensors", "concat");
-  if ($tensors[0].dtype === "complex64") {
-    $tensors.forEach((tensor17) => {
-      if (tensor17.dtype !== "complex64") {
-        throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor17.dtype}. `);
-      }
-    });
-  }
-  const $axis = parseAxisParam(axis, $tensors[0].shape)[0];
-  const outShape = computeOutShape2($tensors.map((t) => t.shape), $axis);
-  if (sizeFromShape(outShape) === 0) {
-    return tensor4([], outShape);
-  }
-  $tensors = $tensors.filter((t) => t.size > 0);
-  if ($tensors.length === 1) {
-    return $tensors[0];
-  }
-  const shapes = $tensors.map((t) => t.shape);
-  assertParamsConsistent(shapes, $axis);
-  const forward = (backend2, save) => {
-    const res = backend2.concat($tensors, $axis);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  const attr = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Concat, attr);
-}
-const concat = op({concat_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoid_(x) {
-  const $x = convertToTensor(x, "x", "sigmoid");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sigmoid($x);
-    save([res]);
-    return res;
-  }, inputs, null, Sigmoid);
-}
-const sigmoid = op({sigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice");
-  if ($x.rank === 0) {
-    throw new Error("Slicing scalar is not possible");
-  }
-  const [begin_, size_] = parseSliceParams($x, begin, size);
-  assertParamsValid($x, begin_, size_);
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.slice($x, begin_, size_);
-  };
-  const inputs = {x: $x};
-  const attrs = {begin, size};
-  return ENGINE.runKernelFunc(forward, inputs, null, Slice, attrs);
-}
-const slice = op({slice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tanh_(x) {
-  const $x = convertToTensor(x, "x", "tanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.tanh($x);
-    save([y]);
-    return y;
-  }, inputs, null, Tanh);
-}
-const tanh2 = op({tanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
-  const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
-  const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
-  const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
-  const $data = convertToTensor(data, "data", "basicLSTMCell");
-  const $c = convertToTensor(c, "c", "basicLSTMCell");
-  const $h = convertToTensor(h, "h", "basicLSTMCell");
-  const combined = concat([$data, $h], 1);
-  const weighted = matMul(combined, $lstmKernel);
-  const res = add2(weighted, $lstmBias);
-  const batchSize = res.shape[0];
-  const sliceCols = res.shape[1] / 4;
-  const sliceSize = [batchSize, sliceCols];
-  const i = slice(res, [0, 0], sliceSize);
-  const j = slice(res, [0, sliceCols], sliceSize);
-  const f = slice(res, [0, sliceCols * 2], sliceSize);
-  const o = slice(res, [0, sliceCols * 3], sliceSize);
-  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
-  const newH = mul(tanh2(newC), sigmoid(o));
-  return [newC, newH];
-}
-const basicLSTMCell = op({basicLSTMCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchToSpaceND_(x, blockShape, crops) {
-  const $x = convertToTensor(x, "x", "batchToSpaceND");
-  const prod3 = blockShape.reduce((a, b) => a * b);
-  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
-  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
-  assert($x.shape[0] % prod3 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod3}`);
-  const forward = (backend2) => {
-    return backend2.batchToSpaceND($x, blockShape, crops);
-  };
-  const inputs = {x: $x};
-  const attrs = {blockShape, crops};
-  return ENGINE.runKernelFunc(forward, inputs, null, BatchToSpaceND, attrs);
-}
-const batchToSpaceND = op({batchToSpaceND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
-function xAs4D(x) {
-  let x4D;
-  if (x.rank === 0 || x.rank === 1) {
-    x4D = reshape(x, [1, 1, 1, x.size]);
-  } else if (x.rank === 2) {
-    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  } else {
-    x4D = x;
-  }
-  return x4D;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 1e-3;
-  }
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
-  assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
-  assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-  const x4D = xAs4D($x);
-  const forward = (backend2, save) => {
-    save([x4D, $mean, $variance, $scale]);
-    return backend2.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);
-  };
-  const inputs = {
-    x: x4D,
-    scale: $scale,
-    offset: $offset,
-    mean: $mean,
-    variance: $variance
-  };
-  const attrs = {varianceEpsilon};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, FusedBatchNorm, attrs);
-  return reshape(res, $x.shape);
-}
-function as1DOr4D(x) {
-  if (x == null) {
-    return null;
-  }
-  if (x.rank === 0) {
-    return reshape(x, [x.size]);
-  } else if (x.rank === 1) {
-    return x;
-  } else if (x.rank === 2) {
-    return reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  return x;
-}
-const batchNorm = op({batchNorm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
-function batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
-  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm2d = op({batchNorm2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
-function batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
-  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm3d = op({batchNorm3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
-function batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
-  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm4d = op({batchNorm4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function broadcastTo_(x, shape) {
-  let input = convertToTensor(x, "broadcastTo", "x");
-  const xShape = input.shape;
-  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
-    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
-  }
-  if (shape.length < input.rank) {
-    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);
-  }
-  if (shape.length > input.rank) {
-    const newShape = input.shape.slice();
-    while (newShape.length < shape.length) {
-      newShape.unshift(1);
-    }
-    input = reshape(input, newShape);
-  }
-  const inputShape = input.shape;
-  const reps = Array.from(shape);
-  for (let i = shape.length - 1; i >= 0; i--) {
-    if (inputShape[i] === shape[i]) {
-      reps[i] = 1;
-    } else if (input.shape[i] !== 1) {
-      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
-    }
-  }
-  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
-  if (axes.length === 0) {
-    return clone(input);
-  }
-  const forward = (backend2) => backend2.tile(input, reps);
-  const inputs = {x: input};
-  const attrs = {shape, inputShape};
-  return ENGINE.runKernelFunc(forward, inputs, null, BroadcastTo, attrs);
-}
-const broadcastTo = op({broadcastTo_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ceil_(x) {
-  const $x = convertToTensor(x, "x", "ceil");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.ceil($x), inputs, null, Ceil);
-}
-const ceil = op({ceil_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clipByValue_(x, clipValueMin, clipValueMax) {
-  const $x = convertToTensor(x, "x", "clipByValue");
-  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
-  const inputs = {x: $x};
-  const attrs = {clipValueMin, clipValueMax};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.clip($x, clipValueMin, clipValueMax);
-    save([$x]);
-    return res;
-  }, inputs, null, ClipByValue, attrs);
-}
-const clipByValue = op({clipByValue_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
-function concat1d_(tensors) {
-  return concat(tensors, 0);
-}
-const concat1d = op({concat1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
-function concat2d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat2d = op({concat2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
-function concat3d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat3d = op({concat3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
-function concat4d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat4d = op({concat4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const forward = (backend2, save) => {
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2d(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2D, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2d = op({conv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
-function conv1d_(x, filter, stride, pad8, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv1d");
-  const $filter = convertToTensor(filter, "filter", "conv1d");
-  let x3D = $x;
-  let reshapedTo3D = false;
-  if ($x.rank === 2) {
-    reshapedTo3D = true;
-    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
-  }
-  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
-  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
-  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
-  assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
-  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
-  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
-  const strides = [1, stride];
-  const dilations = [1, dilation];
-  const conv2dDataFormat = "NHWC";
-  const res = conv2d(input4D, filter4D, strides, pad8, conv2dDataFormat, dilations, dimRoundingMode);
-  if (reshapedTo3D) {
-    return reshape(res, [res.shape[2], res.shape[3]]);
-  }
-  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
-}
-const conv1d = op({conv1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropInput_(xShape, dy, filter, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape4D = xShape;
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
-  }
-  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
-  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
-  const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
-  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2dDerInput(dy4D, filter, convInfo);
-    save([dy4D, filter]);
-    return res2;
-  };
-  const inputs = {dy: dy4D, filter};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape: xShape4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropInput, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2DBackpropInput = op({conv2DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
-function conv2dTranspose_(x, filter, outputShape, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
-  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad8, "NHWC", dimRoundingMode);
-}
-const conv2dTranspose = op({conv2dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3d_(x, filter, strides, pad8, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
-  const $x = convertToTensor(x, "x", "conv3d");
-  const $filter = convertToTensor(filter, "filter", "conv3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
-  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
-  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
-  const forward = (backend2, save) => {
-    const convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad8);
-    const res2 = backend2.conv3d(x5D, $filter, convInfo);
-    save([x5D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x5D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3d = op({conv3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropInput_(xShape, dy, filter, strides, pad8) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape5D = xShape;
-  let dy5D = dy;
-  let reshapedTo5D = false;
-  if (dy.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
-  }
-  const inDepth = xShape5D[4];
-  const outDepth = dy5D.shape[4];
-  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
-  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
-  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
-  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad8);
-    return backend2.conv3dDerInput(dy5D, filter, convInfo);
-  };
-  const inputs = {dy: dy5D};
-  const attrs = {pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3DBackpropInput = op({conv3DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
-function conv3dTranspose_(x, filter, outputShape, strides, pad8) {
-  const $x = convertToTensor(x, "x", "conv3dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
-  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad8);
-}
-const conv3dTranspose = op({conv3dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cos_(x) {
-  const $x = convertToTensor(x, "x", "cos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cos);
-}
-const cos = op({cos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cosh_(x) {
-  const $x = convertToTensor(x, "x", "cosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cosh);
-}
-const cosh = op({cosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cumsum_(x, axis = 0, exclusive = false, reverse9 = false) {
-  const $x = convertToTensor(x, "x", "cumsum");
-  const forward = (backend2, save) => {
-    const permutation = getAxesPermutation([axis], $x.rank);
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-    }
-    const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
-    let value = backend2.cumsum(permutedX, permutedAxis, exclusive, reverse9);
-    save([$x]);
-    if (permutation != null) {
-      const reversePermutation = getUndoAxesPermutation(permutation);
-      value = transpose(value, reversePermutation);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, exclusive, reverse: reverse9};
-  return ENGINE.runKernelFunc(forward, inputs, null, Cumsum, attrs);
-}
-const cumsum = op({cumsum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "depthToSpace");
-  const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
-  const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
-  const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
-  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
-    ${$x.shape}`);
-  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`);
-  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
-  const forward = (backend2) => backend2.depthToSpace($x, blockSize, dataFormat);
-  const inputs = {x: $x};
-  const attrs = {blockSize, dataFormat};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthToSpace, attrs);
-}
-const depthToSpace = op({depthToSpace_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1];
-    }
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-    const res2 = backend2.depthwiseConv2D(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNative, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2d = op({depthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function diag_(x) {
-  const $x = convertToTensor(x, "x", "diag");
-  const forward = (backend2) => {
-    const flat = reshape($x, [$x.size]);
-    const result = backend2.diag(flat);
-    const outShape = [...x.shape, ...x.shape];
-    return reshape(result, outShape);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Diag);
-}
-const diag = op({diag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dilation2d_(x, filter, strides, pad8, dilations = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "dilation2d");
-  const $filter = convertToTensor(filter, "filter", "dilation2d");
-  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
-  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    reshapedTo4D = true;
-  }
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dilations};
-  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const dilation2d = op({dilation2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getBroadcastDims(inShape, outShape) {
-  const inRank = inShape.length;
-  const dims = [];
-  for (let i = 0; i < inRank; i++) {
-    const dim = inRank - 1 - i;
-    const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i] || 1;
-    if (b > 1 && a === 1) {
-      dims.unshift(dim);
-    }
-  }
-  return dims;
-}
-function getReductionAxes(inShape, outShape) {
-  const result = [];
-  for (let i = 0; i < outShape.length; i++) {
-    const inDim = inShape[inShape.length - i - 1];
-    const outAxis = outShape.length - i - 1;
-    const outDim = outShape[outAxis];
-    if (inDim == null || inDim === 1 && outDim > 1) {
-      result.unshift(outAxis);
-    }
-  }
-  return result;
-}
-function assertAndGetBroadcastShape(shapeA, shapeB) {
-  const result = [];
-  const l = Math.max(shapeA.length, shapeB.length);
-  for (let i = 0; i < l; i++) {
-    let a = shapeA[shapeA.length - i - 1];
-    if (a == null) {
-      a = 1;
-    }
-    let b = shapeB[shapeB.length - i - 1];
-    if (b == null) {
-      b = 1;
-    }
-    if (a === 1) {
-      result.unshift(b);
-    } else if (b === 1) {
-      result.unshift(a);
-    } else if (a !== b) {
-      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
-      throw Error(errMsg);
-    } else {
-      result.unshift(a);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function equal_(a, b) {
-  let $a = convertToTensor(a, "a", "equal");
-  let $b = convertToTensor(b, "b", "equal");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.equal($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Equal);
-}
-const equal = op({equal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function where_(condition, a, b) {
-  const $a = convertToTensor(a, "a", "where");
-  const $b = convertToTensor(b, "b", "where");
-  const $condition = convertToTensor(condition, "condition", "where", "bool");
-  const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
-  const $broadcastedA = broadcastTo($a, broadcastShape);
-  const $broadcastedB = broadcastTo($b, broadcastShape);
-  if ($condition.rank === 1) {
-    assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
-  }
-  if ($condition.rank !== 1) {
-    assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.select($condition, $broadcastedA, $broadcastedB);
-    save([$condition]);
-    return res;
-  };
-  const inputs = {
-    condition: $condition,
-    t: $broadcastedA,
-    e: $broadcastedB
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, SelectV2);
-}
-const where = op({where_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zerosLike_(x) {
-  const $x = convertToTensor(x, "x", "zerosLike");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.zerosLike($x), inputs, null, ZerosLike);
-}
-const zerosLike = op({zerosLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function divNoNan_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const divResult = div($a, $b);
-  const zeros9 = zerosLike(divResult);
-  const bEqualsZero = equal($b, zeros9);
-  return where(bEqualsZero, zeros9, divResult);
-}
-const divNoNan = op({divNoNan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dot_(t1, t2) {
-  const $t1 = convertToTensor(t1, "t1", "dot");
-  const $t2 = convertToTensor(t2, "t2", "dot");
-  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
-  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
-  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
-  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
-  if ($t1.rank === 1 && $t2.rank === 1) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, []);
-  } else if ($t1.rank === 1 && $t2.rank === 2) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else if ($t1.rank === 2 && $t2.rank === 1) {
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul($t1, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else {
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul($t1, t22D);
-    return t1t2;
-  }
-}
-const dot = op({dot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function elu_(x) {
-  const $x = convertToTensor(x, "x", "elu");
-  const forward = (backend2, save) => {
-    const y = backend2.elu($x);
-    save([y]);
-    return y;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Elu);
-}
-const elu = op({elu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function erf_(x) {
-  let $x = convertToTensor(x, "x", "erf");
-  assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
-  if ($x.dtype === "int32") {
-    $x = cast($x, "float32");
-  }
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.erf($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Erf);
-}
-const erf = op({erf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function exp_(x) {
-  const $x = convertToTensor(x, "x", "exp");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.exp($x);
-    save([res]);
-    return res;
-  }, inputs, null, Exp);
-}
-const exp = op({exp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expandDims_(x, axis = 0) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "expandDims", parseAs);
-  assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
-  const newShape = $x.shape.slice();
-  if (axis < 0) {
-    assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);
-    axis = $x.rank + axis + 1;
-  }
-  newShape.splice(axis, 0, 1);
-  return reshape($x, newShape);
-}
-const expandDims = op({expandDims_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expm1_(x) {
-  const $x = convertToTensor(x, "x", "expm1");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.expm1($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Expm1);
-}
-const expm1 = op({expm1_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile_(x, reps) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "tile", parseAs);
-  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
-  const forward = (backend2, save) => {
-    const res = backend2.tile($x, reps);
-    save([$x]);
-    return res;
-  };
-  const inputsToSave = [$x];
-  const inputs = {x: $x};
-  const attrs = {reps};
-  return ENGINE.runKernelFunc(forward, inputs, null, Tile, attrs, inputsToSave);
-}
-const tile = op({tile_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function eye_(numRows, numColumns, batchShape, dtype = "float32") {
-  if (numColumns == null) {
-    numColumns = numRows;
-  }
-  const buff = buffer([numRows, numColumns], dtype);
-  const n = numRows <= numColumns ? numRows : numColumns;
-  for (let i = 0; i < n; ++i) {
-    buff.set(1, i, i);
-  }
-  const out = reshape(buff.toTensor(), [numRows, numColumns]);
-  if (batchShape == null) {
-    return out;
-  } else {
-    if (batchShape.length === 1) {
-      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
-    } else if (batchShape.length === 2) {
-      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
-    } else if (batchShape.length === 3) {
-      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
-        batchShape[0],
-        batchShape[1],
-        batchShape[2],
-        1,
-        1
-      ]);
-    } else {
-      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
-    }
-  }
-}
-const eye = op({eye_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fill(shape, value, dtype) {
-  const attrs = {shape, value, dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.fill(shape, value, dtype), {}, null, Fill, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floor_(x) {
-  const $x = convertToTensor(x, "x", "floor");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.floor($x), inputs, null, Floor);
-}
-const floor = op({floor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const PARALLELIZE_THRESHOLD = 30;
-function computeOptimalWindowSize(inSize) {
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    return inSize;
-  }
-  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
-const segment_util_exports = {};
-__export(segment_util_exports, {
-  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
-  computeOutShape: () => computeOutShape3,
-  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function segOpComputeOptimalWindowSize(inSize, numSegments) {
-  let done = false;
-  let res;
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    res = inSize;
-    done = true;
-  } else {
-    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-  }
-  while (!done) {
-    if (res > numSegments || res === inSize) {
-      done = true;
-    } else {
-      res = nearestDivisor(inSize, res + 1);
-    }
-  }
-  return res;
-}
-function computeOutShape3(aShape, axis, numSegments) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (dim !== axis) {
-      outShape.push(aShape[dim]);
-    } else {
-      outShape.push(numSegments);
-    }
-  }
-  return outShape;
-}
-function collectGatherOpShapeInfo(x, indices, axis) {
-  const dimSize = x.shape[axis];
-  const outputShape = [];
-  let batchSize = 1;
-  let sliceSize = 1;
-  for (let i = 0; i < axis; i++) {
-    outputShape.push(x.shape[i]);
-    batchSize *= x.shape[i];
-  }
-  for (let i = 0; i < indices.rank; i++) {
-    outputShape.push(indices.shape[i]);
-  }
-  for (let i = axis + 1; i < x.rank; i++) {
-    outputShape.push(x.shape[i]);
-    sliceSize *= x.shape[i];
-  }
-  return {batchSize, sliceSize, dimSize, outputShape};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gather_(x, indices, axis = 0) {
-  const $x = convertToTensor(x, "x", "gather");
-  const $indices = convertToTensor(indices, "indices", "gather", "int32");
-  const inputs = {x: $x, indices: $indices};
-  const attrs = {axis};
-  const forward = (backend2, save) => {
-    const parsedAxis = parseAxisParam(axis, $x.shape)[0];
-    const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);
-    const res = backend2.gather($x, reshape($indices, [$indices.size]), parsedAxis);
-    save([$x, $indices]);
-    return reshape(res, shapeInfo.outputShape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherV2, attrs);
-}
-const gather = op({gather_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greater_(a, b) {
-  let $a = convertToTensor(a, "a", "greater");
-  let $b = convertToTensor(b, "b", "greater");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.greater($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Greater);
-}
-const greater = op({greater_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greaterEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "greaterEqual");
-  let $b = convertToTensor(b, "b", "greaterEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.greaterEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, GreaterEqual);
-}
-const greaterEqual = op({greaterEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function imag_(input) {
-  const $input = convertToTensor(input, "input", "imag");
-  const forward = (backend2) => {
-    return backend2.imag($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Imag);
-}
-const imag = op({imag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isFinite_(x) {
-  const $x = convertToTensor(x, "x", "isFinite");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isFinite($x), inputs, null, IsFinite);
-}
-const isFinite2 = op({isFinite_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isInf_(x) {
-  const $x = convertToTensor(x, "x", "isInf");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isInf($x), inputs, null, IsInf);
-}
-const isInf = op({isInf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isNaN_(x) {
-  const $x = convertToTensor(x, "x", "isNaN");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isNaN($x), inputs, null, IsNan);
-}
-const isNaN2 = op({isNaN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maximum_(a, b) {
-  let $a = convertToTensor(a, "a", "maximum");
-  let $b = convertToTensor(b, "b", "maximum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.maximum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Maximum);
-}
-const maximum = op({maximum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scalar(value, dtype) {
-  if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
-    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
-  }
-  if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
-    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-  }
-  const shape = [];
-  const inferredShape = [];
-  return makeTensor(value, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function leakyRelu_(x, alpha = 0.2) {
-  const $x = convertToTensor(x, "x", "leakyRelu");
-  return maximum(mul(scalar(alpha), $x), $x);
-}
-const leakyRelu = op({leakyRelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function less_(a, b) {
-  let $a = convertToTensor(a, "a", "less");
-  let $b = convertToTensor(b, "b", "less");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.less($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Less);
-}
-const less = op({less_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function lessEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "lessEqual");
-  let $b = convertToTensor(b, "b", "lessEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.lessEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, LessEqual);
-}
-const lessEqual = op({lessEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function linspace(start, stop, num) {
-  if (num <= 0) {
-    throw new Error("The number of values should be positive.");
-  }
-  const attrs = {start, stop, num};
-  return ENGINE.runKernelFunc((backend2) => backend2.linspace(start, stop, num), {}, null, LinSpace, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const $x = convertToTensor(x, "x", "localResponseNormalization");
-  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${$x.rank}.`);
-  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  const forward = (backend2, save) => {
-    const y = backend2.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {depthRadius, bias, alpha, beta};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, LRN, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  } else {
-    return res;
-  }
-}
-const localResponseNormalization = op({localResponseNormalization_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log_(x) {
-  const $x = convertToTensor(x, "x", "log");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log);
-}
-const log = op({log_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log1p_(x) {
-  const $x = convertToTensor(x, "x", "log1p");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log1p($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log1p);
-}
-const log1p = op({log1p_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function grad(f) {
-  assert(isFunction(f), () => "The f passed in grad(f) must be a function");
-  return (x, dy) => {
-    const $x = convertToTensor(x, "x", "tf.grad", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
-      }
-      checkGrads(grads2);
-      return grads2[0];
-    });
-  };
-}
-function grads(f) {
-  assert(isFunction(f), () => "The f passed in grads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    const $args = convertToTensorArray(args, "args", "tf.grads", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-      }
-      checkGrads(grads2);
-      return grads2;
-    });
-  };
-}
-function valueAndGrad(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
-  return (x, dy) => {
-    assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
-    const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
-    checkGrads(grads2);
-    return {grad: grads2[0], value};
-  };
-}
-function valueAndGrads(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
-    const res = ENGINE.gradients(() => f(...args), args, dy);
-    if (dy != null) {
-      assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-    }
-    checkGrads(res.grads);
-    return res;
-  };
-}
-function variableGrads(f, varList) {
-  assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
-  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
-  const specifiedVarList = varList != null;
-  if (!specifiedVarList) {
-    varList = [];
-    for (const varName in ENGINE.registeredVariables) {
-      varList.push(ENGINE.registeredVariables[varName]);
-    }
-  }
-  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable3) => !variable3.trainable) : null;
-  const originalVarCount = varList.length;
-  varList = varList.filter((variable3) => variable3.trainable);
-  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
-  const allowNoGradients = true;
-  const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
-  assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
-  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
-  const namedGrads = {};
-  varList.forEach((v, i) => {
-    if (grads2[i] != null) {
-      namedGrads[v.name] = grads2[i];
-    }
-  });
-  if (specifiedNonTrainable != null) {
-    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
-  }
-  return {value, grads: namedGrads};
-}
-function customGrad(f) {
-  return ENGINE.customGrad(f);
-}
-function checkGrads(grads2) {
-  const numNullGradients = grads2.filter((g) => g == null).length;
-  if (numNullGradients > 0) {
-    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function neg_(x) {
-  const $x = convertToTensor(x, "x", "neg");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.neg($x), inputs, null, Negate);
-}
-const neg = op({neg_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softplus_(x) {
-  const $x = convertToTensor(x, "x", "softplus");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.softplus($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Softplus);
-}
-const softplus = op({softplus_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSigmoid_(x) {
-  const $x = convertToTensor(x, "x", "logSigmoid");
-  const customOp = customGrad((x2) => {
-    const value = neg(softplus(neg(x2)));
-    const gradFunc = (dy) => {
-      const derX = mul(dy, sigmoid(neg(x2)));
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const logSigmoid = op({logSigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function max_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "max");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let maxInput = $x;
-    if (permutedAxes != null) {
-      maxInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, maxInput.rank);
-    }
-    const y = backend2.max(maxInput, axes);
-    if (permutedAxes != null) {
-      maxInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, parseAxisParam(axis, $x.shape));
-      res = reshape(res, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {reductionIndices: axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Max, attrs);
-}
-const max = op({max_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sub_(a, b) {
-  let $a = convertToTensor(a, "a", "sub");
-  let $b = convertToTensor(b, "b", "sub");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.subtract($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sub);
-}
-const sub = op({sub_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sum_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "sum");
-  if ($x.dtype === "bool") {
-    $x = cast($x, "int32");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.sum(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sum, attrs);
-}
-const sum2 = op({sum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSoftmax_(logits, axis = -1) {
-  const $logits = convertToTensor(logits, "logits", "logSoftmax");
-  if (axis === -1) {
-    axis = $logits.rank - 1;
-  }
-  if (axis !== $logits.rank - 1) {
-    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
-  }
-  const forward = (backend2, save) => {
-    const keepDims = true;
-    const xMax = max(logits, axis, true);
-    const shifted = sub(logits, xMax);
-    const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
-    save([value]);
-    return value;
-  };
-  const inputs = {logits: $logits};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, LogSoftmax, attrs);
-}
-const logSoftmax = op({logSoftmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSumExp_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "logSumExp");
-  const axes = parseAxisParam(axis, $x.shape);
-  const xMax = max($x, axes, true);
-  const a = sub($x, xMax);
-  const b = exp(a);
-  const c = sum2(b, axes);
-  const d = log(c);
-  const res = add2(reshape(xMax, d.shape), d);
-  if (keepDims) {
-    const newShape = expandShapeToKeepDim(res.shape, axes);
-    return reshape(res, newShape);
-  }
-  return res;
-}
-const logSumExp = op({logSumExp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalAnd_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalAnd", "bool");
-  const $b = convertToTensor(b, "b", "logicalAnd", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalAnd($a, $b), inputs, null, LogicalAnd);
-}
-const logicalAnd = op({logicalAnd_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalNot_(x) {
-  const $x = convertToTensor(x, "x", "logicalNot", "bool");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalNot($x), inputs, null, LogicalNot);
-}
-const logicalNot = op({logicalNot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalOr_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalOr", "bool");
-  const $b = convertToTensor(b, "b", "logicalOr", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalOr($a, $b), inputs, null, LogicalOr);
-}
-const logicalOr = op({logicalOr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalXor_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalXor", "bool");
-  const $b = convertToTensor(b, "b", "logicalXor", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
-}
-const logicalXor = op({logicalXor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "maxPool");
-  const dilations = 1;
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    let y;
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      y = x4D.clone();
-    } else {
-      y = backend2.maxPool(x4D, convInfo);
-    }
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const maxPool = op({maxPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "maxPool3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    const y = backend2.maxPool3d(x5D, convInfo);
-    save([x5D, y]);
-    return y;
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3d = op({maxPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolWithArgmax_(x, filterSize, strides, pad8, includeBatchInIndex = false) {
-  const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
-  const inputs = {x: $x};
-  const attrs = {filterSize, strides, pad: pad8, includeBatchInIndex};
-  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
-  return {result: result[0], indexes: result[1]};
-}
-const maxPoolWithArgmax = op({maxPoolWithArgmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zeros(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = zeros(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ones2(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = ones2(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mean_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "mean");
-  const axes = parseAxisParam(axis, $x.shape);
-  const shapes = computeOutAndReduceShapes($x.shape, axes);
-  const reduceShape = shapes[1];
-  const reduceSize = sizeFromShape(reduceShape);
-  const customOp = customGrad((x2) => {
-    const reduceSizeScalar = scalar(reduceSize);
-    const xReduce = reduceSizeScalar.dtype === x2.dtype ? x2 : cast(x2, reduceSizeScalar.dtype);
-    const res = div(xReduce, reduceSizeScalar);
-    const value = sum2(res, axis, keepDims);
-    const gradFunc = (dy) => {
-      const expandedDyShape = x2.shape.slice();
-      axes.forEach((axis2) => {
-        expandedDyShape[axis2] = 1;
-      });
-      const expandedDy = reshape(dy, expandedDyShape);
-      const derX = div(mul(expandedDy, ones2(x2.shape, "float32")), reduceSize);
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const mean = op({mean_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/min.js
-function min_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "min");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let minInput = $x;
-    if (permutedAxes != null) {
-      minInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const y = backend2.min(minInput, axes);
-    if (permutedAxes != null) {
-      minInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, origAxes);
-      res = reshape(y, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Min, attrs);
-}
-const min = op({min_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function minimum_(a, b) {
-  let $a = convertToTensor(a, "a", "minimum");
-  let $b = convertToTensor(b, "b", "minimum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.minimum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Minimum);
-}
-const minimum = op({minimum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mod_(a, b) {
-  let $a = convertToTensor(a, "a", "mod");
-  let $b = convertToTensor(b, "b", "mod");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.mod($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Mod);
-}
-const mod = op({mod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/square.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function square_(x) {
-  const $x = convertToTensor(x, "x", "square");
-  const attrs = {};
-  const inputsToSave = [$x];
-  const outputsToSave = [];
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    return backend2.square($x);
-  }, {x: $x}, null, "Square", attrs, inputsToSave, outputsToSave);
-}
-const square = op({square_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function moments_(x, axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "moments");
-  const axes = parseAxisParam(axis, x.shape);
-  const xMean = mean(x, axes, keepDims);
-  let keepDimsShape = xMean.shape;
-  if (!keepDims) {
-    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
-  }
-  const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
-  const variance = mean(devSquared, axes, keepDims);
-  return {mean: xMean, variance};
-}
-const moments = op({moments_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
-function multiRNNCell_(lstmCells, data, c, h) {
-  const $data = convertToTensor(data, "data", "multiRNNCell");
-  const $c = convertToTensorArray(c, "c", "multiRNNCell");
-  const $h = convertToTensorArray(h, "h", "multiRNNCell");
-  let input = $data;
-  const newStates = [];
-  for (let i = 0; i < lstmCells.length; i++) {
-    const output = lstmCells[i](input, $c[i], $h[i]);
-    newStates.push(output[0]);
-    newStates.push(output[1]);
-    input = output[1];
-  }
-  const newC = [];
-  const newH = [];
-  for (let i = 0; i < newStates.length; i += 2) {
-    newC.push(newStates[i]);
-    newH.push(newStates[i + 1]);
-  }
-  return [newC, newH];
-}
-const multiRNNCell = op({multiRNNCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function multinomial_(logits, numSamples, seed, normalized = false) {
-  const $logits = convertToTensor(logits, "logits", "multinomial");
-  const numOutcomes = $logits.size;
-  const origRank = $logits.rank;
-  if (numOutcomes < 2) {
-    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
-  }
-  if (origRank > 2) {
-    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
-  }
-  seed = seed || Math.random();
-  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
-  const res = ENGINE.runKernelFunc((backend2) => backend2.multinomial(logits2D, normalized, numSamples, seed), {logits2D});
-  return origRank === 1 ? reshape(res, [res.size]) : res;
-}
-const multinomial = op({multinomial_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "notEqual");
-  let $b = convertToTensor(b, "b", "notEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.notEqual($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, NotEqual);
-}
-const notEqual = op({notEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/real.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function real_(input) {
-  const $input = convertToTensor(input, "input", "real");
-  const forward = (backend2) => {
-    return backend2.real($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Real);
-}
-const real = op({real_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function onesLike_(x) {
-  const $x = convertToTensor(x, "x", "onesLike");
-  const forward = (backend2, save) => {
-    if ($x.dtype === "complex64") {
-      const r = onesLike(real($x));
-      const i = zerosLike(imag($x));
-      return complex(r, i);
-    }
-    return backend2.onesLike($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, OnesLike);
-}
-const onesLike = op({onesLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
-function outerProduct_(v1, v2) {
-  const $v1 = convertToTensor(v1, "v1", "outerProduct");
-  const $v2 = convertToTensor(v2, "v2", "outerProduct");
-  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
-  const v12D = reshape($v1, [-1, 1]);
-  const v22D = reshape($v2, [1, -1]);
-  return matMul(v12D, v22D);
-}
-const outerProduct = op({outerProduct_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pad_(x, paddings, constantValue = 0) {
-  const $x = convertToTensor(x, "x", "pad");
-  if ($x.rank === 0) {
-    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.pad($x, paddings, constantValue);
-  };
-  const attrs = {paddings, constantValue};
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, PadV2, attrs);
-}
-const pad = op({pad_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
-function pad1d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
-  return pad(x, [paddings], constantValue);
-}
-const pad1d = op({pad1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
-function pad2d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad2d = op({pad2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
-function pad3d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad3d = op({pad3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
-function pad4d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad4d = op({pad4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function spaceToBatchND_(x, blockShape, paddings) {
-  const $x = convertToTensor(x, "x", "spaceToBatchND");
-  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
-  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-  assert($x.shape.reduce((a, b, i) => {
-    if (i > 0 && i <= blockShape.length) {
-      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
-    }
-    return a;
-  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
-  const forward = (backend2) => backend2.spaceToBatchND($x, blockShape, paddings);
-  const inputs = {x: $x};
-  const attrs = {blockShape, paddings};
-  return ENGINE.runKernelFunc(forward, inputs, null, SpaceToBatchND, attrs);
-}
-const spaceToBatchND = op({spaceToBatchND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pool_(input, windowShape, poolingType, pad8, dilations, strides) {
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  if (strides == null) {
-    strides = 1;
-  }
-  if (pad8 === 0) {
-    pad8 = "valid";
-  }
-  const $x = convertToTensor(input, "x", "maxPool");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad8);
-  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
-  let basePadding;
-  if (pad8 === "same") {
-    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
-  } else {
-    basePadding = [[0, 0], [0, 0]];
-  }
-  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
-  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
-  const convertedPad = isDilationOne ? pad8 : "valid";
-  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
-  const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
-  const y = forwardOp();
-  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
-  const padStart = basePadding.map((b) => b[0]);
-  const origPadEnd = basePadding.map((b) => b[1]);
-  const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
-  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
-  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
-  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
-  return [paddings, crops];
-}
-function withSpaceToBatchBasePaddings(filterShape, dilation) {
-  const dilatedFilterShape = filterShape.map((s, i) => {
-    return s + (s - 1) * (dilation[i] - 1);
-  });
-  const padExtraShape = dilatedFilterShape.map((s) => s - 1);
-  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
-  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
-  return padExtraShape.map((_, i) => {
-    return [padExtraStart[i], padExtraEnd[i]];
-  });
-}
-const pool = op({pool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pow_(base, exp11) {
-  let $base = convertToTensor(base, "base", "pow");
-  let $exp = convertToTensor(exp11, "exp", "pow");
-  [$base, $exp] = makeTypesMatch($base, $exp);
-  const inputs = {a: $base, b: $exp};
-  const forward = (backend2, save) => {
-    const y = backend2.pow($base, $exp);
-    save([$base, $exp, y]);
-    return y;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Pow);
-}
-const pow = op({pow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prelu_(x, alpha) {
-  const $x = convertToTensor(x, "x", "prelu");
-  const $alpha = convertToTensor(alpha, "alpha", "prelu");
-  const forward = (backend2, save) => {
-    const res = backend2.prelu($x, $alpha);
-    save([$x, $alpha]);
-    return res;
-  };
-  const inputs = {x: $x, alpha: $alpha};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prelu);
-}
-const prelu = op({prelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prod_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "prod");
-  const forward = (backend2) => {
-    if ($x.dtype === "bool") {
-      $x = cast($x, "int32");
-    }
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.prod(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prod, attrs);
-}
-const prod = op({prod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rand_(shape, randFunction, dtype) {
-  const size = sizeFromShape(shape);
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  for (let i = 0; i < size; i++) {
-    values[i] = randFunction();
-  }
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-const rand = op({rand_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
-const seedrandom = __toModule(require_seedrandom2());
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MPRandGauss {
-  constructor(mean5, stdDeviation, dtype, truncated, seed) {
-    this.mean = mean5;
-    this.stdDev = stdDeviation;
-    this.dtype = dtype;
-    this.nextVal = NaN;
-    this.truncated = truncated;
-    if (this.truncated) {
-      this.upper = this.mean + this.stdDev * 2;
-      this.lower = this.mean - this.stdDev * 2;
-    }
-    const seedValue = seed ? seed : Math.random();
-    this.random = seedrandom.alea(seedValue.toString());
-  }
-  nextValue() {
-    if (!isNaN(this.nextVal)) {
-      const value = this.nextVal;
-      this.nextVal = NaN;
-      return value;
-    }
-    let resultX, resultY;
-    let isValid = false;
-    while (!isValid) {
-      let v1, v2, s;
-      do {
-        v1 = 2 * this.random() - 1;
-        v2 = 2 * this.random() - 1;
-        s = v1 * v1 + v2 * v2;
-      } while (s >= 1 || s === 0);
-      const mul62 = Math.sqrt(-2 * Math.log(s) / s);
-      resultX = this.mean + this.stdDev * v1 * mul62;
-      resultY = this.mean + this.stdDev * v2 * mul62;
-      if (!this.truncated || this.isValidTruncated(resultX)) {
-        isValid = true;
-      }
-    }
-    if (!this.truncated || this.isValidTruncated(resultY)) {
-      this.nextVal = this.convertValue(resultY);
-    }
-    return this.convertValue(resultX);
-  }
-  convertValue(value) {
-    if (this.dtype == null || this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-  isValidTruncated(value) {
-    return value <= this.upper && value >= this.lower;
-  }
-}
-class RandGamma {
-  constructor(alpha, beta, dtype, seed) {
-    this.alpha = alpha;
-    this.beta = 1 / beta;
-    this.dtype = dtype;
-    const seedValue = seed ? seed : Math.random();
-    this.randu = seedrandom.alea(seedValue.toString());
-    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
-    if (alpha < 1) {
-      this.d = alpha + 2 / 3;
-    } else {
-      this.d = alpha - 1 / 3;
-    }
-    this.c = 1 / Math.sqrt(9 * this.d);
-  }
-  nextValue() {
-    let x2, v0, v1, x, u, v;
-    while (true) {
-      do {
-        x = this.randn.nextValue();
-        v = 1 + this.c * x;
-      } while (v <= 0);
-      v *= v * v;
-      x2 = x * x;
-      v0 = 1 - 0.331 * x2 * x2;
-      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
-      u = this.randu();
-      if (u < v0 || Math.log(u) < v1) {
-        break;
-      }
-    }
-    v = 1 / this.beta * this.d * v;
-    if (this.alpha < 1) {
-      v *= Math.pow(this.randu(), 1 / this.alpha);
-    }
-    return this.convertValue(v);
-  }
-  convertValue(value) {
-    if (this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-}
-class UniformRandom {
-  constructor(min4 = 0, max6 = 1, dtype, seed) {
-    this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
-    this.min = min4;
-    this.range = max6 - min4;
-    this.dtype = dtype;
-    if (seed == null) {
-      seed = Math.random();
-    }
-    if (typeof seed === "number") {
-      seed = seed.toString();
-    }
-    if (!this.canReturnFloat() && this.range <= 1) {
-      throw new Error(`The difference between ${min4} - ${max6} <= 1 and dtype is not float`);
-    }
-    this.random = seedrandom.alea(seed);
-  }
-  convertValue(value) {
-    if (this.canReturnFloat()) {
-      return value;
-    }
-    return Math.round(value);
-  }
-  nextValue() {
-    return this.convertValue(this.min + this.range * this.random());
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
-  if (beta == null) {
-    beta = 1;
-  }
-  if (dtype == null) {
-    dtype = "float32";
-  }
-  if (dtype !== "float32" && dtype !== "int32") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const rgamma = new RandGamma(alpha, beta, dtype, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = rgamma.nextValue();
-  }
-  return res.toTensor();
-}
-const randomGamma = op({randomGamma_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const randomNormal = op({randomNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
-  const res = buffer(shape, dtype);
-  const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = random.nextValue();
-  }
-  return res.toTensor();
-}
-const randomUniform = op({randomUniform_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor1d(values, dtype) {
-  assertNonNull(values);
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 1) {
-    throw new Error("tensor1d() requires values to be a flat/TypedArray");
-  }
-  const shape = null;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/range.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function range(start, stop, step7 = 1, dtype = "float32") {
-  if (step7 === 0) {
-    throw new Error("Cannot have a step of zero");
-  }
-  const forward = () => {
-    const sameStartStop = start === stop;
-    const increasingRangeNegativeStep = start < stop && step7 < 0;
-    const decreasingRangePositiveStep = stop < start && step7 > 1;
-    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
-      return zeros([0], dtype);
-    }
-    const numElements = Math.abs(Math.ceil((stop - start) / step7));
-    const values = makeZerosTypedArray(numElements, dtype);
-    if (stop < start && step7 === 1) {
-      step7 = -1;
-    }
-    values[0] = start;
-    for (let i = 1; i < values.length; i++) {
-      values[i] = values[i - 1] + step7;
-    }
-    return tensor1d(values, dtype);
-  };
-  const attrs = {start, stop, step: step7, dtype};
-  return ENGINE.runKernelFunc(forward, {}, null, Range, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reciprocal_(x) {
-  const $x = convertToTensor(x, "x", "reciprocal");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.reciprocal($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Reciprocal);
-}
-const reciprocal = op({reciprocal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu_(x) {
-  const $x = convertToTensor(x, "x", "relu");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu);
-}
-const relu = op({relu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu6_(x) {
-  const $x = convertToTensor(x, "x", "relu6");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu6($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu6);
-}
-const relu6 = op({relu6_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  const forward = (backend2) => {
-    const axes = parseAxisParam(axis, $x.shape);
-    if ($x.rank === 0) {
-      return clone($x);
-    }
-    const res = backend2.reverse($x, axes);
-    return reshape(res, $x.shape);
-  };
-  const inputs = {x: $x};
-  const attrs = {dims: axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Reverse, attrs);
-}
-const reverse = op({reverse_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse1d_(x) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
-  return reverse($x, 0);
-}
-const reverse1d = op({reverse1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse2d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse2d = op({reverse2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse3d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse3d = op({reverse3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse4d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse4d = op({reverse4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/round.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function round_(x) {
-  const $x = convertToTensor(x, "x", "round");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.round($x), inputs, null, Round);
-}
-const round = op({round_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rsqrt_(x) {
-  const $x = convertToTensor(x, "x", "rsqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.rsqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Rsqrt);
-}
-const rsqrt = op({rsqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function selu_(x) {
-  const $x = convertToTensor(x, "x", "selu");
-  const forward = (backend2, save) => {
-    const res = backend2.selu($x);
-    save([$x]);
-    return res;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Selu);
-}
-const selu = op({selu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
-function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad8, dilation = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "separableConv2d");
-  const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
-  const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  if (dataFormat === "NCHW") {
-    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
-  }
-  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
-  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
-  const inChannels = $depthwiseFilter.shape[2];
-  const channelMultiplier = $depthwiseFilter.shape[3];
-  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
-  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad8, dataFormat, dilation);
-  const pointwiseStride = 1;
-  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const separableConv2d = op({separableConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function setdiff1dAsync_(x, y) {
-  const $x = convertToTensor(x, "x", "setdiff1d");
-  const $y = convertToTensor(y, "y", "setdiff1d");
-  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
-  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
-  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
-  const xVals = await $x.data();
-  const yVals = await $y.data();
-  const ySet = new Set(yVals);
-  let outputSize = 0;
-  for (let i = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      outputSize++;
-    }
-  }
-  const buffer11 = new TensorBuffer([outputSize], $x.dtype);
-  const indices = new TensorBuffer([outputSize], "int32");
-  for (let i = 0, p = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      buffer11.values[p] = xVals[i];
-      indices.values[p] = i;
-      p++;
-    }
-  }
-  return [buffer11.toTensor(), indices.toTensor()];
-}
-const setdiff1dAsync = setdiff1dAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sign_(x) {
-  const $x = convertToTensor(x, "x", "sign");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.sign($x), inputs, null, Sign);
-}
-const sign = op({sign_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sin_(x) {
-  const $x = convertToTensor(x, "x", "sin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sin);
-}
-const sin = op({sin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sinh_(x) {
-  const $x = convertToTensor(x, "x", "sinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sinh);
-}
-const sinh = op({sinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice1d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice1d");
-  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, [begin], [size]);
-}
-const slice1d = op({slice1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice2d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice2d");
-  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice2d = op({slice2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice3d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice3d");
-  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice3d = op({slice3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice4d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice4d");
-  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice4d = op({slice4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmax_(logits, dim = -1) {
-  const $logits = convertToTensor(logits, "logits", "softmax", "float32");
-  if (dim === -1) {
-    dim = $logits.rank - 1;
-  }
-  if (dim !== $logits.rank - 1) {
-    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
-  }
-  const inputs = {logits: $logits};
-  const attrs = {dim};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.softmax($logits, dim);
-    save([y]);
-    return y;
-  }, inputs, null, Softmax, attrs);
-}
-const softmax = op({softmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = input.as2D(batch, innerDimensionSize);
-    const result = backend2.fft(input2D);
-    return result.reshape(input.shape);
-  }, inputs, null, FFT);
-}
-const fft = op({fft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ifft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = reshape(input, [batch, innerDimensionSize]);
-    const result = backend2.ifft(input2D);
-    return reshape(result, input.shape);
-  }, inputs, null, IFFT);
-}
-const ifft = op({ifft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function irfft_(input) {
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let ret;
-  if (innerDimensionSize <= 2) {
-    const complexInput = reshape(input, [batch, innerDimensionSize]);
-    ret = ifft(complexInput);
-  } else {
-    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
-    const realInput = reshape(real(input), [batch, innerDimensionSize]);
-    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
-    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
-    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-    const r = concat([realInput, realConjugate], 1);
-    const i = concat([imagInput, imagConjugate], 1);
-    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
-    ret = ifft(complexInput);
-  }
-  ret = real(ret);
-  if (input.rank === 3 && input.shape[0] !== 0) {
-    const temp = ret;
-    const batch2 = input.shape[0];
-    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
-    temp.dispose();
-  }
-  return ret;
-}
-const irfft = op({irfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
-function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
-  let splitSizes = [];
-  if (typeof numOrSizeSplits === "number") {
-    assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
-    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
-  } else {
-    const numOfNegs = numOrSizeSplits.reduce((count, value) => {
-      if (value === -1) {
-        count += 1;
-      }
-      return count;
-    }, 0);
-    assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
-    const negIndex = numOrSizeSplits.indexOf(-1);
-    if (negIndex !== -1) {
-      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
-      numOrSizeSplits[negIndex] = x.shape[axis] - total;
-    }
-    assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
-    splitSizes = numOrSizeSplits;
-  }
-  return splitSizes;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split_(x, numOrSizeSplits, axis = 0) {
-  const $x = convertToTensor(x, "x", "split");
-  const forward = (backend2, _) => {
-    const $axis = parseAxisParam(axis, $x.shape)[0];
-    const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);
-    return backend2.split($x, splitSizes, $axis);
-  };
-  const inputs = {x: $x};
-  const attr = {numOrSizeSplits, axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, SplitV, attr);
-}
-const split = op({split_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rfft_(input, fftLength) {
-  assert(input.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input.dtype}`);
-  let innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let adjustedInput;
-  if (fftLength != null && fftLength < innerDimensionSize) {
-    const begin = input.shape.map((v) => 0);
-    const size = input.shape.map((v) => v);
-    size[input.shape.length - 1] = fftLength;
-    adjustedInput = slice(input, begin, size);
-    innerDimensionSize = fftLength;
-  } else if (fftLength != null && fftLength > innerDimensionSize) {
-    const zerosShape = input.shape.map((v) => v);
-    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
-    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
-    innerDimensionSize = fftLength;
-  } else {
-    adjustedInput = input;
-  }
-  const zerosInput = zerosLike(adjustedInput);
-  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
-  const ret = fft(complexInput);
-  const half = Math.floor(innerDimensionSize / 2) + 1;
-  const realValues = real(ret);
-  const imagValues = imag(ret);
-  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
-  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
-  const outputShape = adjustedInput.shape.slice();
-  outputShape[adjustedInput.shape.length - 1] = half;
-  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
-}
-const rfft = op({rfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sqrt_(x) {
-  const $x = convertToTensor(x, "x", "sqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sqrt);
-}
-const sqrt = op({sqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squaredDifference_(a, b) {
-  let $a = convertToTensor(a, "a", "squaredDifference");
-  let $b = convertToTensor(b, "b", "squaredDifference");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.squaredDifference($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, SquaredDifference, attrs);
-}
-const squaredDifference = op({squaredDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squeeze_(x, axis) {
-  const $x = convertToTensor(x, "x", "squeeze");
-  return reshape($x, squeezeShape($x.shape, axis).newShape);
-}
-const squeeze = op({squeeze_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stack_(tensors, axis = 0) {
-  const $tensors = convertToTensorArray(tensors, "tensors", "stack");
-  assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
-  if ($tensors.length === 1) {
-    return expandDims($tensors[0], axis);
-  }
-  const rank = $tensors[0].rank;
-  const shape = $tensors[0].shape;
-  const dtype = $tensors[0].dtype;
-  assert(axis <= rank, () => "Axis must be <= rank of the tensor");
-  $tensors.forEach((t) => {
-    assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
-    assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
-  });
-  const expandedTensors = $tensors.map((t) => expandDims(t, axis));
-  return concat(expandedTensors, axis);
-}
-const stack = op({stack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/step.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function step_(x, alpha = 0) {
-  const $x = convertToTensor(x, "x", "step");
-  const inputs = {x: $x};
-  const attrs = {alpha};
-  return ENGINE.runKernelFunc((backend2) => backend2.step($x, alpha), inputs, null, Step, attrs);
-}
-const step = op({step_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
-  let $x = convertToTensor(x, "x", "stridedSlice");
-  const forward = (backend2) => {
-    if (strides == null) {
-      strides = new Array(begin.length);
-    }
-    const ellipsisAxes = maskToAxes(ellipsisMask);
-    if (ellipsisAxes.length > 1) {
-      throw new Error("Multiple ellipses in slice is not allowed.");
-    }
-    if (ellipsisMask !== 0 && newAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
-    }
-    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
-    }
-    const numInterpolatedAxes = $x.rank - begin.length;
-    const expandAxes = maskToAxes(newAxisMask);
-    const newShape = $x.shape.slice();
-    expandAxes.forEach((axis) => {
-      begin[axis] = 0;
-      end[axis] = 1;
-      newShape.splice(axis, 0, 1);
-    });
-    $x = reshape($x, newShape);
-    const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
-    begin = normalizedBegin;
-    end = normalizedEnd;
-    strides = normalizedStrides;
-    const shrinkAxes = maskToAxes(shrinkAxisMask);
-    shrinkAxes.forEach((axis) => {
-      end[axis] = begin[axis] + 1;
-      strides[axis] = 1;
-    });
-    const size = computeOutShape(begin, end, strides);
-    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
-    const nonStrided = strides.every((v) => v === 1);
-    if (nonStrided) {
-      return reshape(slice($x, begin, size), outShape);
-    }
-    const res = backend2.stridedSlice($x, begin, end, strides);
-    return reshape(res, outShape);
-  };
-  const inputs = {x: $x};
-  const attrs = {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, StridedSlice, attrs);
-}
-const stridedSlice = op({stridedSlice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tan_(x) {
-  const $x = convertToTensor(x, "x", "tan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.tan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Tan);
-}
-const tan = op({tan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor2d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 2) {
-    throw new Error("tensor2d() requires shape to have two numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
-    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 4) {
-    throw new Error("tensor4d() requires shape to have four numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
-    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor5d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 5) {
-    throw new Error("tensor5d() requires shape to have five numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
-    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor6d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 6) {
-    throw new Error("tensor6d() requires shape to have six numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
-    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-  }
-  shape = shape || inferredShape;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topk_(x, k = 1, sorted = true) {
-  const $x = convertToTensor(x, "x", "topk");
-  if ($x.rank === 0) {
-    throw new Error("topk() expects the input to be of rank 1 or higher");
-  }
-  const lastDim = $x.shape[$x.shape.length - 1];
-  if (k > lastDim) {
-    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
-  }
-  const inputs = {x: $x};
-  const attrs = {k, sorted};
-  const [values, indices] = ENGINE.runKernelFunc((b) => b.topk($x, k, sorted), inputs, null, TopK, attrs);
-  return {values, indices};
-}
-const topk = op({topk_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type $ { dtype }`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const truncatedNormal = op({truncatedNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unsortedSegmentSum_(x, segmentIds, numSegments) {
-  const $x = convertToTensor(x, "x", "unsortedSegmentSum");
-  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
-  assert(isInt(numSegments), () => "numSegments must be of dtype int");
-  const inputs = {x: $x, segmentIds: $segmentIds};
-  const attrs = {numSegments};
-  const forward = (backend2, save) => {
-    const res = backend2.unsortedSegmentSum($x, $segmentIds, numSegments);
-    save([$segmentIds]);
-    return res;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, UnsortedSegmentSum, attrs);
-}
-const unsortedSegmentSum = op({unsortedSegmentSum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unstack_(x, axis = 0) {
-  const $x = convertToTensor(x, "x", "unstack");
-  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
-  if (axis < 0) {
-    axis += $x.shape.length;
-  }
-  const inputs = {value: $x};
-  const attrs = {axis};
-  const forward = (backend2) => backend2.unstack($x, axis);
-  return ENGINE.runKernelFunc(forward, inputs, null, Unpack, attrs);
-}
-const unstack = op({unstack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function variable(initialValue, trainable = true, name, dtype) {
-  return ENGINE.makeVariable(initialValue, trainable, name, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function whereImpl(condShape, condVals) {
-  const indices = [];
-  for (let i = 0; i < condVals.length; i++) {
-    if (condVals[i]) {
-      indices.push(i);
-    }
-  }
-  const inBuffer = buffer(condShape, "int32");
-  const out = buffer([indices.length, condShape.length], "int32");
-  for (let i = 0; i < indices.length; i++) {
-    const loc = inBuffer.indexToLoc(indices[i]);
-    const offset = i * condShape.length;
-    out.values.set(loc, offset);
-  }
-  return out.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function whereAsync_(condition) {
-  const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
-  const vals = await $condition.data();
-  const res = whereImpl($condition.shape, vals);
-  if (condition !== $condition) {
-    $condition.dispose();
-  }
-  return res;
-}
-const whereAsync = whereAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function booleanMaskAsync_(tensor17, mask, axis) {
-  const $tensor = convertToTensor(tensor17, "tensor", "boolMask");
-  const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
-  const axisFrom = axis == null ? 0 : axis;
-  const maskDim = $mask.rank;
-  const tensorShape = $tensor.shape;
-  assert(maskDim > 0, () => "mask cannot be scalar");
-  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
-  let leadingSize = 1;
-  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
-    leadingSize *= tensorShape[i];
-  }
-  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
-  const reshapedTensor = reshape($tensor, targetTensorShape);
-  const reshapedMask = reshape($mask, [-1]);
-  const positivePositions = await whereAsync(reshapedMask);
-  const indices = squeeze(positivePositions, [1]);
-  const res = gather(reshapedTensor, indices, axisFrom);
-  if (tensor17 !== $tensor) {
-    $tensor.dispose();
-  }
-  if (mask !== $mask) {
-    $mask.dispose();
-  }
-  indices.dispose();
-  reshapedTensor.dispose();
-  reshapedMask.dispose();
-  positivePositions.dispose();
-  return res;
-}
-const booleanMaskAsync = booleanMaskAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "notEqualStrict");
-  const $b = convertToTensor(b, "b", "notEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in notEqualStrict: ");
-  return notEqual($a, $b);
-}
-function lessStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessStrict");
-  const $b = convertToTensor(b, "b", "lessStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessStrict: ");
-  return less($a, $b);
-}
-function equalStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "equalStrict");
-  const $b = convertToTensor(b, "b", "equalStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in equalStrict: ");
-  return equal($a, $b);
-}
-function lessEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessEqualStrict");
-  const $b = convertToTensor(b, "b", "lessEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessEqualStrict: ");
-  return lessEqual($a, $b);
-}
-function greaterStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterStrict");
-  const $b = convertToTensor(b, "b", "greaterStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterStrict: ");
-  return greater($a, $b);
-}
-function greaterEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterEqualStrict");
-  const $b = convertToTensor(b, "b", "greaterEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterEqualStrict: ");
-  return greaterEqual($a, $b);
-}
-const equalStrict = op({equalStrict_});
-const greaterEqualStrict = op({greaterEqualStrict_});
-const greaterStrict = op({greaterStrict_});
-const lessEqualStrict = op({lessEqualStrict_});
-const lessStrict = op({lessStrict_});
-const notEqualStrict = op({notEqualStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "addStrict");
-  const $b = convertToTensor(b, "b", "addStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in addStrict: ");
-  return add2($a, $b);
-}
-function subStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "subStrict");
-  const $b = convertToTensor(b, "b", "subStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in subStrict: ");
-  return sub($a, $b);
-}
-function powStrict_(base, exp11) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  assertShapesMatch(base.shape, exp11.shape, "Error in powStrict: ");
-  return pow(base, exp11);
-}
-function mulStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "mul");
-  const $b = convertToTensor(b, "b", "mul");
-  assertShapesMatch($a.shape, $b.shape, "Error in multiplyStrict: ");
-  return mul($a, $b);
-}
-function divStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "div");
-  const $b = convertToTensor(b, "b", "div");
-  assertShapesMatch($a.shape, $b.shape, "Error in divideStrict: ");
-  return div($a, $b);
-}
-function modStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "modStrict");
-  const $b = convertToTensor(b, "b", "modStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in modStrict: ");
-  return mod($a, $b);
-}
-function minimumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "minimumStrict");
-  const $b = convertToTensor(b, "b", "minimumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in minimumStrict: ");
-  return minimum($a, $b);
-}
-function maximumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "maximumStrict");
-  const $b = convertToTensor(b, "b", "maximumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in maximumStrict: ");
-  return maximum($a, $b);
-}
-function squaredDifferenceStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "squaredDifferenceStrict");
-  const $b = convertToTensor(b, "b", "squaredDifferenceStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in squaredDifferenceStrict: ");
-  return squaredDifference($a, $b);
-}
-const addStrict = op({addStrict_});
-const divStrict = op({divStrict_});
-const maximumStrict = op({maximumStrict_});
-const minimumStrict = op({minimumStrict_});
-const modStrict = op({modStrict_});
-const mulStrict = op({mulStrict_});
-const powStrict = op({powStrict_});
-const squaredDifferenceStrict = op({squaredDifferenceStrict_});
-const subStrict = op({subStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "norm");
-  const norm4 = normImpl(x, ord, axis);
-  let keepDimsShape = norm4.shape;
-  if (keepDims) {
-    const axes = parseAxisParam(axis, x.shape);
-    keepDimsShape = expandShapeToKeepDim(norm4.shape, axes);
-  }
-  return reshape(norm4, keepDimsShape);
-}
-function normImpl(x, p, axis = null) {
-  if (x.rank === 0) {
-    return abs(x);
-  }
-  if (x.rank !== 1 && axis === null) {
-    return normImpl(reshape(x, [-1]), p, axis);
-  }
-  if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
-    if (p === 1) {
-      return sum2(abs(x), axis);
-    }
-    if (p === Infinity) {
-      return max(abs(x), axis);
-    }
-    if (p === -Infinity) {
-      return min(abs(x), axis);
-    }
-    if (p === "euclidean" || p === 2) {
-      return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  if (Array.isArray(axis) && axis.length === 2) {
-    if (p === 1) {
-      return max(sum2(abs(x), axis[0]), axis[1] - 1);
-    }
-    if (p === Infinity) {
-      return max(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === -Infinity) {
-      return min(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === "fro" || p === "euclidean") {
-      return sqrt(sum2(square(x), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  throw new Error(`Error in norm: invalid axis: ${axis}`);
-}
-const norm = op({norm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function movingAverage_(v, x, decay, step7, zeroDebias = true) {
-  const $v = convertToTensor(v, "v", "movingAverage");
-  const $x = convertToTensor(x, "x", "movingAverage");
-  const $decay = convertToTensor(decay, "decay", "movingAverage");
-  assertTypesMatch($v, $x);
-  assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
-  const one = scalar(1);
-  const oneMinusDecay = sub(one, $decay);
-  let update = mul(sub($x, $v), oneMinusDecay);
-  if (zeroDebias) {
-    assert(step7 != null, () => "When using zeroDebias: true, step is required.");
-    const $step = convertToTensor(step7, "step", "movingAverage");
-    update = div(update, sub(one, pow($decay, $step)));
-  }
-  return add2($v, update);
-}
-const movingAverage = op({movingAverage_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scatterND_(indices, updates, shape) {
-  const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
-  const $updates = convertToTensor(updates, "updates", "scatterND");
-  validateInput($updates, $indices, shape);
-  const forward = (backend2) => {
-    return backend2.scatterND($indices, $updates, shape);
-  };
-  const inputs = {indices: $indices, updates: $updates};
-  const attrs = {shape};
-  return ENGINE.runKernelFunc(forward, inputs, null, ScatterNd, attrs);
-}
-const scatterND = op({scatterND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
-function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
-  if (sparseIndices.dtype !== "int32") {
-    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
-  }
-  if (sparseIndices.rank > 2) {
-    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
-  }
-  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
-  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
-  if (outputShape.length !== numDims) {
-    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
-  }
-  const numValues = sparseValues.size;
-  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
-    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
-  }
-  if (sparseValues.dtype !== defaultValues.dtype) {
-    throw new Error("sparseValues.dtype must match defaultValues.dtype");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
-  const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
-  const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
-  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
-  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
-  const inputs = {
-    sparseIndices: $sparseIndices,
-    sparseValues: $sparseValues,
-    defaultValue: $defaultValue
-  };
-  const attrs = {outputShape};
-  return ENGINE.runKernelFunc((backend2) => backend2.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null, SparseToDense, attrs);
-}
-const sparseToDense = op({sparseToDense_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gatherND_(x, indices) {
-  const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
-  const $x = convertToTensor(x, "x", "gatherND");
-  const forward = (backend2) => {
-    return backend2.gatherND($x, $indices);
-  };
-  const inputs = {params: $x, indices: $indices};
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherNd);
-}
-const gatherND = op({gatherND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getNoiseShape(x, noiseShape) {
-  if (noiseShape == null) {
-    return x.shape.slice();
-  }
-  if (arraysEqual(x.shape, noiseShape)) {
-    return noiseShape;
-  }
-  if (x.shape.length === noiseShape.length) {
-    const newDimension = [];
-    for (let i = 0; i < x.shape.length; i++) {
-      if (noiseShape[i] == null && x.shape[i] != null) {
-        newDimension.push(x.shape[i]);
-      } else {
-        newDimension.push(noiseShape[i]);
-      }
-    }
-    return newDimension;
-  }
-  return noiseShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dropout_(x, rate, noiseShape, seed) {
-  const $x = convertToTensor(x, "x", "dropout");
-  assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
-  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
-  if (rate === 0) {
-    return x instanceof Tensor ? $x.clone() : $x;
-  }
-  const $noiseShape = getNoiseShape($x, noiseShape);
-  const keepProb = 1 - rate;
-  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
-  return mul($x, multiplier);
-}
-const dropout = op({dropout_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enclosingPowerOfTwo(value) {
-  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
-}
-function cosineWindow(windowLength, a, b) {
-  const even = 1 - windowLength % 2;
-  const newValues = new Float32Array(windowLength);
-  for (let i = 0; i < windowLength; ++i) {
-    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
-    newValues[i] = a - b * Math.cos(cosArg);
-  }
-  return tensor1d(newValues, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function inTopKAsync_(predictions, targets, k = 1) {
-  const $predictions = convertToTensor(predictions, "predictions", "inTopK");
-  const $targets = convertToTensor(targets, "targets", "inTopK");
-  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
-  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
-  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
-  const lastDim = $predictions.shape[$predictions.shape.length - 1];
-  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
-  const predictionsVals = await $predictions.data();
-  const targetsVals = await $targets.data();
-  const [batch, size] = [predictionsVals.length / lastDim, lastDim];
-  const precision = getTypedArrayFromDType("bool", batch);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = predictionsVals.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    precision[b] = 0;
-    for (let i = 0; i < k; i++) {
-      if (valAndInd[i].index === targetsVals[b]) {
-        precision[b] = 1;
-        break;
-      }
-    }
-  }
-  if (predictions !== $predictions) {
-    $predictions.dispose();
-  }
-  if (targets !== $targets) {
-    $targets.dispose();
-  }
-  return tensor4(precision, $targets.shape, "bool");
-}
-const inTopKAsync = inTopKAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropFilter_(x, dy, filterShape, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
-  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
-  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    return backend2.conv2dDerFilter(x4D, dy4D, convInfo);
-  };
-  const inputs = {x: x4D, dy: dy4D};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);
-}
-const conv2DBackpropFilter = op({conv2DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFusedDyActivation(dy, y, activation) {
-  if (activation == null || activation === "linear") {
-    return dy;
-  }
-  if (activation === "relu") {
-    return mul(dy, step(y));
-  }
-  throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
-}
-function getFusedBiasGradient(bias, dyActivation) {
-  let res = dyActivation;
-  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
-  if (reduceAxes.length > 0) {
-    res = sum2(res, reduceAxes);
-  }
-  return reshape(res, bias.shape);
-}
-function applyActivation(x, activation, preluActivationWeights) {
-  if (activation === "linear") {
-    return x;
-  } else if (activation === "relu") {
-    return relu(x);
-  } else if (activation === "elu") {
-    return elu(x);
-  } else if (activation === "relu6") {
-    return relu6(x);
-  } else if (activation === "prelu") {
-    return prelu(x, preluActivationWeights);
-  }
-  throw new Error(`Unknown fused activation ${activation}.`);
-}
-const shouldFuse = (gradientDepth, activation) => {
-  const gradientMode = gradientDepth > 0;
-  return !gradientMode || activation === "linear";
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  activation = activation || "linear";
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = conv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
-  }
-  const grad2 = (dy, saved) => {
-    const [$filter2, x4D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8);
-    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8);
-    const der = [xDer, filterDer];
-    if ($bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      der.push(biasDer);
-    }
-    return der;
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedConv2d({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const conv2d5 = op({fusedConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);
-  const inputs = {x: x4D, dy: dy4D};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);
-}
-const depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerInput(dy4D, filter, convInfo);
-  const inputs = {dy: dy4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedDepthwiseConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = depthwiseConv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
-  }
-  const grad2 = (dy, saved) => {
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
-    const [$filter2, x4D2, y, bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, convInfo);
-    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, convInfo);
-    if (bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias, dyActivation);
-      return [xDer, filterDer, biasDer];
-    }
-    return [xDer, filterDer];
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedDepthwiseConv2D({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = matMul(a, b, transposeA, transposeB);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  let $a = convertToTensor(a, "a", "fused matMul");
-  let $b = convertToTensor(b, "b", "fused matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused matMul");
-    [$bias] = makeTypesMatch($bias, $a);
-    assertAndGetBroadcastShape(outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
-  }
-  const grad2 = (dy, saved) => {
-    const [a3D2, b3D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
-    let aDer;
-    let bDer;
-    if (!transposeA && !transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, true);
-      bDer = matMul(a3D2, dyActivation, true, false);
-    } else if (!transposeA && transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, false);
-      bDer = matMul(dyActivation, a3D2, true, false);
-    } else if (transposeA && !transposeB) {
-      aDer = matMul(b3D2, dyActivation, false, true);
-      bDer = matMul(a3D2, dyActivation, false, false);
-    } else {
-      aDer = matMul(b3D2, dyActivation, true, true);
-      bDer = matMul(dyActivation, a3D2, true, true);
-    }
-    if (bias != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      return [aDer, bDer, biasDer];
-    } else {
-      return [aDer, bDer];
-    }
-  };
-  const forward = (backend2) => {
-    const y = backend2.fusedBatchMatMul({
-      a: a3D,
-      b: b3D,
-      transposeA,
-      transposeB,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return y;
-  };
-  const inputs = {
-    a: a3D,
-    b: b3D,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {transposeA, transposeB, activation};
-  if (bias == null) {
-    const customOp = customGrad((a3D2, b3D2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOp(a3D, b3D);
-  } else {
-    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res, $bias2]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOpWithBias(a3D, b3D, $bias);
-  }
-}
-const matMul2 = op({fusedMatMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
-const fused_ops_exports = {};
-__export(fused_ops_exports, {
-  conv2d: () => conv2d5,
-  depthwiseConv2d: () => depthwiseConv2d2,
-  matMul: () => matMul2
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hammingWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.54, 0.46);
-}
-const hammingWindow = op({hammingWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hannWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.5, 0.5);
-}
-const hannWindow = op({hannWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
-  let start = 0;
-  const output = [];
-  while (start + frameLength <= signal2.size) {
-    output.push(slice(signal2, start, frameLength));
-    start += frameStep;
-  }
-  if (padEnd) {
-    while (start < signal2.size) {
-      const padLen = start + frameLength - signal2.size;
-      const pad8 = concat([
-        slice(signal2, start, frameLength - padLen),
-        fill([padLen], padValue)
-      ]);
-      output.push(pad8);
-      start += frameStep;
-    }
-  }
-  if (output.length === 0) {
-    return tensor2d([], [0, frameLength]);
-  }
-  return reshape(concat(output), [output.length, frameLength]);
-}
-const frame = op({frame_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
-  if (fftLength == null) {
-    fftLength = enclosingPowerOfTwo(frameLength);
-  }
-  const framedSignal = frame(signal2, frameLength, frameStep);
-  const windowedSignal = mul(framedSignal, windowFn(frameLength));
-  const output = [];
-  for (let i = 0; i < framedSignal.shape[0]; i++) {
-    output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
-  }
-  return concat(output);
-}
-const stft = op({stft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cropAndResize_(image2, boxes, boxInd, cropSize, method, extrapolationValue) {
-  const $image = convertToTensor(image2, "image", "cropAndResize");
-  const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
-  const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
-  method = method || "bilinear";
-  extrapolationValue = extrapolationValue || 0;
-  const numBoxes = $boxes.shape[0];
-  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
-  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
-  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
-  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
-  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-  assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
-  const forward = (backend2) => backend2.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
-  const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
-  const attrs = {method, extrapolationValue, cropSize};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, CropAndResize, attrs);
-  return res;
-}
-const cropAndResize = op({cropAndResize_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function flipLeftRight_(image2) {
-  const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
-  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
-  return res;
-}
-const flipLeftRight = op({flipLeftRight_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
-  const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
-  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const attrs = {radians, fillValue, center};
-  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
-  return res;
-}
-const rotateWithOffset = op({rotateWithOffset_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  if (iouThreshold == null) {
-    iouThreshold = 0.5;
-  }
-  if (scoreThreshold == null) {
-    scoreThreshold = Number.NEGATIVE_INFINITY;
-  }
-  if (softNmsSigma == null) {
-    softNmsSigma = 0;
-  }
-  const numBoxes = boxes.shape[0];
-  maxOutputSize = Math.min(maxOutputSize, numBoxes);
-  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
-  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
-  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
-  assert(scores.rank === 1, () => "scores must be a 1D tensor");
-  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
-  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
-  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
-  return ENGINE.runKernelFunc((b) => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), {boxes: $boxes, scores: $scores}, null, NonMaxSuppressionV3, attrs);
-}
-const nonMaxSuppression = op({nonMaxSuppression_});
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/array_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function binaryInsert(arr, element, comparator) {
-  const index = binarySearch(arr, element, comparator);
-  const insertionPoint = index < 0 ? -(index + 1) : index;
-  arr.splice(insertionPoint, 0, element);
-}
-function binarySearch(arr, target, comparator) {
-  return binarySearch_(arr, target, comparator || defaultComparator);
-}
-function defaultComparator(a, b) {
-  return a > b ? 1 : a < b ? -1 : 0;
-}
-function binarySearch_(arr, target, comparator) {
-  let left = 0;
-  let right = arr.length;
-  let middle = 0;
-  let found = false;
-  while (left < right) {
-    middle = left + (right - left >>> 1);
-    const compareResult = comparator(target, arr[middle]);
-    if (compareResult > 0) {
-      left = middle + 1;
-    } else {
-      right = middle;
-      found = !compareResult;
-    }
-  }
-  return found ? left : -left - 1;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0).selectedIndices;
-}
-function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
-}
-function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
-}
-function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
-  const candidates = [];
-  for (let i = 0; i < scores.length; i++) {
-    if (scores[i] > scoreThreshold) {
-      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
-    }
-  }
-  candidates.sort(ascendingComparator);
-  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
-  const selectedIndices = [];
-  const selectedScores = [];
-  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
-    const candidate = candidates.pop();
-    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
-    if (originalScore < scoreThreshold) {
-      break;
-    }
-    let ignoreCandidate = false;
-    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
-      const iou3 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
-      if (iou3 >= iouThreshold) {
-        ignoreCandidate = true;
-        break;
-      }
-      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou3);
-      if (candidate.score <= scoreThreshold) {
-        break;
-      }
-    }
-    candidate.suppressBeginIndex = selectedIndices.length;
-    if (!ignoreCandidate) {
-      if (candidate.score === originalScore) {
-        selectedIndices.push(boxIndex);
-        selectedScores.push(candidate.score);
-      } else if (candidate.score > scoreThreshold) {
-        binaryInsert(candidates, candidate, ascendingComparator);
-      }
-    }
-  }
-  const validOutputs = selectedIndices.length;
-  const elemsToPad = maxOutputSize - validOutputs;
-  if (padToMaxOutputSize && elemsToPad > 0) {
-    selectedIndices.push(...new Array(elemsToPad).fill(0));
-    selectedScores.push(...new Array(elemsToPad).fill(0));
-  }
-  const result = {selectedIndices: tensor1d(selectedIndices, "int32")};
-  if (returnScoresTensor) {
-    result["selectedScores"] = tensor1d(selectedScores, "float32");
-  }
-  if (returnValidOutputs) {
-    result["validOutputs"] = scalar(validOutputs, "int32");
-  }
-  return result;
-}
-function intersectionOverUnion(boxes, i, j) {
-  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
-  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
-  const yminI = Math.min(iCoord[0], iCoord[2]);
-  const xminI = Math.min(iCoord[1], iCoord[3]);
-  const ymaxI = Math.max(iCoord[0], iCoord[2]);
-  const xmaxI = Math.max(iCoord[1], iCoord[3]);
-  const yminJ = Math.min(jCoord[0], jCoord[2]);
-  const xminJ = Math.min(jCoord[1], jCoord[3]);
-  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
-  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
-  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
-  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-  if (areaI <= 0 || areaJ <= 0) {
-    return 0;
-  }
-  const intersectionYmin = Math.max(yminI, yminJ);
-  const intersectionXmin = Math.max(xminI, xminJ);
-  const intersectionYmax = Math.min(ymaxI, ymaxJ);
-  const intersectionXmax = Math.min(xmaxI, xmaxJ);
-  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
-  return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-function suppressWeight(iouThreshold, scale2, iou3) {
-  const weight = Math.exp(scale2 * iou3 * iou3);
-  return iou3 <= iouThreshold ? weight : 0;
-}
-function ascendingComparator(c1, c2) {
-  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
-  return {selectedIndices: result[0], selectedScores: result[1]};
-}
-const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {
-    maxOutputSize: $maxOutputSize,
-    iouThreshold: $iouThreshold,
-    scoreThreshold: $scoreThreshold,
-    padToMaxOutputSize
-  };
-  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
-  return {selectedIndices: result[0], validOutputs: result[1]};
-}
-const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
-  const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeBilinear_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeBilinear");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeBilinear, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeBilinear = op({resizeBilinear_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeNearestNeighbor_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
-  assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeNearestNeighbor, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeNearestNeighbor = op({resizeNearestNeighbor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function bandPart_(a, numLower, numUpper) {
-  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
-  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
-  const $a = convertToTensor(a, "a", "bandPart");
-  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
-  const shape = $a.shape;
-  const [M, N] = $a.shape.slice(-2);
-  if (!(numLower <= M)) {
-    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
-  }
-  if (!(numUpper <= N)) {
-    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
-  }
-  if (numLower < 0) {
-    numLower = M;
-  }
-  if (numUpper < 0) {
-    numUpper = N;
-  }
-  const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
-  const j = range(0, N, 1, "int32");
-  const ij = sub(i, j);
-  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
-  const zero = zeros([M, N], $a.dtype);
-  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
-}
-const bandPart = op({bandPart_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gramSchmidt_(xs) {
-  let inputIsTensor2D;
-  if (Array.isArray(xs)) {
-    inputIsTensor2D = false;
-    assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
-    const dim = xs[0].shape[0];
-    for (let i = 1; i < xs.length; ++i) {
-      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
-    }
-  } else {
-    inputIsTensor2D = true;
-    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
-  }
-  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
-  const ys = [];
-  const xs1d = xs;
-  for (let i = 0; i < xs.length; ++i) {
-    ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i];
-      if (i > 0) {
-        for (let j = 0; j < i; ++j) {
-          const proj = mul(sum2(mul(ys[j], x)), ys[j]);
-          x = sub(x, proj);
-        }
-      }
-      return div(x, norm(x, "euclidean"));
-    }));
-  }
-  if (inputIsTensor2D) {
-    return stack(ys, 0);
-  } else {
-    return ys;
-  }
-}
-const gramSchmidt = op({gramSchmidt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function qr_(x, fullMatrices = false) {
-  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
-  if (x.rank === 2) {
-    return qr2d(x, fullMatrices);
-  } else {
-    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
-    const x2ds = unstack(reshape(x, [
-      outerDimsProd,
-      x.shape[x.shape.length - 2],
-      x.shape[x.shape.length - 1]
-    ]), 0);
-    const q2ds = [];
-    const r2ds = [];
-    x2ds.forEach((x2d) => {
-      const [q2d, r2d] = qr2d(x2d, fullMatrices);
-      q2ds.push(q2d);
-      r2ds.push(r2d);
-    });
-    const q = reshape(stack(q2ds, 0), x.shape);
-    const r = reshape(stack(r2ds, 0), x.shape);
-    return [q, r];
-  }
-}
-function qr2d(x, fullMatrices = false) {
-  return ENGINE.tidy(() => {
-    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
-    const m = x.shape[0];
-    const n = x.shape[1];
-    let q = eye(m);
-    let r = clone(x);
-    const one2D = tensor2d([[1]], [1, 1]);
-    let w = clone(one2D);
-    const iters = m >= n ? n : m;
-    for (let j = 0; j < iters; ++j) {
-      const rTemp = r;
-      const wTemp = w;
-      const qTemp = q;
-      [w, r, q] = ENGINE.tidy(() => {
-        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
-        const normX = norm(rjEnd1);
-        const rjj = slice(r, [j, j], [1, 1]);
-        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-        const u1 = sub(rjj, mul(s, normX));
-        const wPre = div(rjEnd1, u1);
-        if (wPre.shape[0] === 1) {
-          w = clone(one2D);
-        } else {
-          w = concat([
-            one2D,
-            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
-          ], 0);
-        }
-        const tau = neg(div(matMul(s, u1), normX));
-        const rjEndAll = slice(r, [j, 0], [m - j, n]);
-        const tauTimesW = mul(tau, w);
-        const wT = transpose(w);
-        if (j === 0) {
-          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-        } else {
-          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
-        }
-        const tawTimesWT = transpose(tauTimesW);
-        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
-        if (j === 0) {
-          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-        } else {
-          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
-        }
-        return [w, r, q];
-      });
-      dispose([rTemp, wTemp, qTemp]);
-    }
-    if (!fullMatrices && m > n) {
-      q = slice(q, [0, 0], [m, n]);
-      r = slice(r, [0, 0], [n, n]);
-    }
-    return [q, r];
-  });
-}
-const qr = op({qr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Reduction;
-(function(Reduction2) {
-  Reduction2[Reduction2["NONE"] = 0] = "NONE";
-  Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
-  Reduction2[Reduction2["SUM"] = 2] = "SUM";
-  Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
-})(Reduction || (Reduction = {}));
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
-function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
-  }
-  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
-  if (reduction === Reduction.NONE) {
-    return weightedLoss;
-  }
-  if (reduction === Reduction.SUM) {
-    return sum2(weightedLoss);
-  }
-  if (reduction === Reduction.MEAN) {
-    if ($weights == null) {
-      return mean(weightedLoss);
-    } else {
-      const broadcastFactor = $losses.size / $weights.size;
-      const result = div(sum2(weightedLoss), sum2($weights));
-      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
-    }
-  }
-  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    if ($weights == null) {
-      return div(sum2(weightedLoss), scalar($losses.size));
-    } else {
-      const broadcastedWeights = mul($weights, ones2($losses.shape));
-      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
-      return div(sum2(weightedLoss), numNonZeros);
-    }
-  }
-  throw Error(`Unknown reduction: ${reduction}`);
-}
-const computeWeightedLoss = op({computeWeightedLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "absoluteDifference");
-  const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "absoluteDifference");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
-  const losses2 = abs(sub($labels, $predictions));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const absoluteDifference = op({absoluteDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
-function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "cosineDistance");
-  const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "cosineDistance");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
-  const one = scalar(1);
-  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const cosineDistance = op({cosineDistance_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
-function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $labels = convertToTensor(labels, "labels", "hingeLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "hingeLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
-  const one = scalar(1);
-  $labels = sub(mul(scalar(2), $labels), one);
-  const losses2 = relu(sub(one, mul($labels, $predictions)));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const hingeLoss = op({hingeLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "huberLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "huberLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
-  const deltaScalar = scalar(delta);
-  const error = abs(sub($predictions, $labels));
-  const quadratic = minimum(error, deltaScalar);
-  const linear = sub(error, quadratic);
-  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const huberLoss = op({huberLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logLoss_(labels, predictions, weights, epsilon2 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "logLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "logLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "logLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
-  const one = scalar(1);
-  const epsilonScalar = scalar(epsilon2);
-  const l1 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
-  const l2 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
-  const losses2 = sub(l1, l2);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const logLoss = op({logLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "meanSquaredError");
-  const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "meanSquaredError");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
-  const losses2 = squaredDifference($labels, $predictions);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const meanSquaredError = op({meanSquaredError_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoidCrossEntropyWithLogits_(labels, logits) {
-  const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
-  assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  const maxOutput = relu($logits);
-  const outputXTarget = mul($logits, $labels);
-  const sigmoidOutput = log1p(exp(neg(abs($logits))));
-  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
-}
-function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
-  }
-  assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const half = scalar(0.5);
-    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
-  }
-  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
-  if (dim === -1) {
-    dim = logits.rank - 1;
-  }
-  if (dim !== logits.rank - 1) {
-    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
-  }
-  const customOp = customGrad((labels2, logits2, save) => {
-    const keepDims = true;
-    const lse = logSumExp(logits2, [dim], keepDims);
-    const logResult = sub(cast(logits2, "float32"), lse);
-    save([labels2, logResult]);
-    const costVector = neg(mul(logResult, labels2));
-    const value = sum2(costVector, [dim]);
-    const gradFunc = (dy, saved) => {
-      const [labels3, logResult2] = saved;
-      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
-      return [
-        mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
-        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
-      ];
-    };
-    return {value, gradFunc};
-  });
-  return customOp(labels, logits);
-}
-function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
-  }
-  assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const numClasses = scalar($onehotLabels.shape[1]);
-    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
-  }
-  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const softmaxCrossEntropy = op({softmaxCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spectral = {
-  fft,
-  ifft,
-  rfft,
-  irfft
-};
-const signal = {
-  hammingWindow,
-  hannWindow,
-  frame,
-  stft
-};
-const image = {
-  flipLeftRight,
-  resizeNearestNeighbor,
-  resizeBilinear,
-  rotateWithOffset,
-  cropAndResize,
-  nonMaxSuppression,
-  nonMaxSuppressionAsync,
-  nonMaxSuppressionWithScore,
-  nonMaxSuppressionWithScoreAsync,
-  nonMaxSuppressionPadded,
-  nonMaxSuppressionPaddedAsync
-};
-const linalg = {
-  bandPart,
-  gramSchmidt,
-  qr
-};
-const losses = {
-  absoluteDifference,
-  computeWeightedLoss,
-  cosineDistance,
-  hingeLoss,
-  huberLoss,
-  logLoss,
-  meanSquaredError,
-  sigmoidCrossEntropy,
-  softmaxCrossEntropy
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Optimizer extends Serializable {
-  minimize(f, returnCost = false, varList) {
-    const {value, grads: grads2} = this.computeGradients(f, varList);
-    if (varList != null) {
-      const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
-      this.applyGradients(gradArray);
-    } else {
-      this.applyGradients(grads2);
-    }
-    dispose(grads2);
-    if (returnCost) {
-      return value;
-    } else {
-      value.dispose();
-      return null;
-    }
-  }
-  get iterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return this.iterations_;
-  }
-  incrementIterations() {
-    this.iterations_ = this.iterations + 1;
-  }
-  computeGradients(f, varList) {
-    return variableGrads(f, varList);
-  }
-  dispose() {
-    if (this.iterations_ != null) {
-      dispose(this.iterations_);
-    }
-  }
-  async saveIterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return {
-      name: "iter",
-      tensor: scalar(this.iterations_, "int32")
-    };
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for this optimizer yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
-  }
-  async extractIterations(weightValues) {
-    this.iterations_ = (await weightValues[0].tensor.data())[0];
-    return weightValues.slice(1);
-  }
-}
-Object.defineProperty(Optimizer, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdadeltaOptimizer extends Optimizer {
-  constructor(learningRate, rho, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.rho = rho;
-    this.epsilon = epsilon2;
-    this.accumulatedGrads = [];
-    this.accumulatedUpdates = [];
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedGrads[i] == null) {
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accum_grad`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedUpdates[i] == null) {
-        this.accumulatedUpdates[i] = {
-          originalName: `${name}/accum_var`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
-        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
-        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        accumulatedUpdate.assign(newAccumulatedUpdate);
-        const newValue = add2(mul(updates, -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedUpdates != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-      dispose(this.accumulatedUpdates.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      rho: this.rho,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["rho"], config2["epsilon"]);
-  }
-}
-AdadeltaOptimizer.className = "Adadelta";
-registerClass(AdadeltaOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdagradOptimizer extends Optimizer {
-  constructor(learningRate, initialAccumulatorValue = 0.1) {
-    super();
-    this.learningRate = learningRate;
-    this.initialAccumulatorValue = initialAccumulatorValue;
-    this.accumulatedGrads = [];
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i] == null) {
-        const trainable = false;
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accumulator`,
-          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedGrads != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      initialAccumulatorValue: this.initialAccumulatorValue
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["initialAccumulatorValue"]);
-  }
-}
-AdagradOptimizer.className = "Adagrad";
-registerClass(AdagradOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedSecondMoment = [];
-    tidy(() => {
-      this.accBeta1 = scalar(beta1).variable();
-      this.accBeta2 = scalar(beta2).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const oneMinusAccBeta2 = sub(1, this.accBeta2);
-      varNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedSecondMoment[i] == null) {
-          this.accumulatedSecondMoment[i] = {
-            originalName: `${name}/v`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const secondMoment = this.accumulatedSecondMoment[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
-        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
-        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
-        firstMoment.assign(newFirstMoment);
-        secondMoment.assign(newSecondMoment);
-        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
-        value.assign(newValue);
-      });
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.accBeta2.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedSecondMoment != null) {
-      dispose(this.accumulatedSecondMoment.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    tidy(() => {
-      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
-      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
-    });
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"]);
-  }
-}
-AdamOptimizer.className = "Adam";
-registerClass(AdamOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamaxOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null, decay = 0) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.decay = decay;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedWeightedInfNorm = [];
-    tidy(() => {
-      this.iteration = scalar(0).variable();
-      this.accBeta1 = scalar(beta1).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        if (this.accumulatedWeightedInfNorm[i] == null) {
-          this.accumulatedWeightedInfNorm[i] = {
-            originalName: `${name}/v`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const ut0 = mul(weightedInfNorm, this.beta2);
-        const ut1 = abs(gradient);
-        const newWeightedInfNorm = maximum(ut0, ut1);
-        firstMoment.assign(newFirstMoment);
-        weightedInfNorm.assign(newWeightedInfNorm);
-        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
-        value.assign(newValue);
-      });
-      this.iteration.assign(add2(this.iteration, 1));
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.iteration.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedWeightedInfNorm != null) {
-      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for Adamax yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error("setWeights() is not implemented for Adamax yet.");
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon,
-      decay: this.decay
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"], config2["decay"]);
-  }
-}
-AdamaxOptimizer.className = "Adamax";
-registerClass(AdamaxOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class SGDOptimizer extends Optimizer {
-  constructor(learningRate) {
-    super();
-    this.learningRate = learningRate;
-    this.setLearningRate(learningRate);
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    varNames.forEach((name, i) => {
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const value = ENGINE.registeredVariables[name];
-      tidy(() => {
-        const newValue = add2(mul(this.c, gradient), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  setLearningRate(learningRate) {
-    this.learningRate = learningRate;
-    if (this.c != null) {
-      this.c.dispose();
-    }
-    this.c = keep(scalar(-learningRate));
-  }
-  dispose() {
-    this.c.dispose();
-  }
-  async getWeights() {
-    return [await this.saveIterations()];
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    if (weightValues.length !== 0) {
-      throw new Error("SGD optimizer does not have settable weights.");
-    }
-  }
-  getConfig() {
-    return {learningRate: this.learningRate};
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"]);
-  }
-}
-SGDOptimizer.className = "SGD";
-registerClass(SGDOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MomentumOptimizer extends SGDOptimizer {
-  constructor(learningRate, momentum, useNesterov = false) {
-    super(learningRate);
-    this.learningRate = learningRate;
-    this.momentum = momentum;
-    this.useNesterov = useNesterov;
-    this.accumulations = [];
-    this.m = scalar(this.momentum);
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i] == null) {
-        const trainable = false;
-        this.accumulations[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const accumulation = this.accumulations[i].variable;
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      tidy(() => {
-        let newValue;
-        const newAccumulation = add2(mul(this.m, accumulation), gradient);
-        if (this.useNesterov) {
-          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
-        } else {
-          newValue = add2(mul(this.c, newAccumulation), value);
-        }
-        accumulation.assign(newAccumulation);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.m.dispose();
-    if (this.accumulations != null) {
-      dispose(this.accumulations.map((v) => v.variable));
-    }
-  }
-  setMomentum(momentum) {
-    this.momentum = momentum;
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      momentum: this.momentum,
-      useNesterov: this.useNesterov
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["momentum"], config2["useNesterov"]);
-  }
-}
-MomentumOptimizer.className = "Momentum";
-registerClass(MomentumOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class RMSPropOptimizer extends Optimizer {
-  constructor(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    super();
-    this.learningRate = learningRate;
-    this.decay = decay;
-    this.momentum = momentum;
-    this.epsilon = epsilon2;
-    this.accumulatedMeanSquares = [];
-    this.accumulatedMoments = [];
-    this.accumulatedMeanGrads = [];
-    this.centered = centered;
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-    if (learningRate == null) {
-      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedMeanSquares[i] == null) {
-        this.accumulatedMeanSquares[i] = {
-          originalName: `${name}/rms`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMoments[i] == null) {
-        this.accumulatedMoments[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMeanGrads[i] == null && this.centered) {
-        this.accumulatedMeanGrads[i] = {
-          originalName: `${name}/mg`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
-      const accumulatedMoments = this.accumulatedMoments[i].variable;
-      tidy(() => {
-        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-        if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
-          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
-          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        } else {
-          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        }
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedMeanSquares != null) {
-      dispose(this.accumulatedMeanSquares.map((v) => v.variable));
-    }
-    if (this.accumulatedMeanGrads != null && this.centered) {
-      dispose(this.accumulatedMeanGrads.map((v) => v.variable));
-    }
-    if (this.accumulatedMoments != null) {
-      dispose(this.accumulatedMoments.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
-    if (this.centered) {
-      variables.push(...this.accumulatedMeanGrads);
-    }
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    if (this.centered) {
-      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-    }
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      decay: this.decay,
-      momentum: this.momentum,
-      epsilon: this.epsilon,
-      centered: this.centered
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["decay"], config2["momentum"], config2["epsilon"], config2["centered"]);
-  }
-}
-RMSPropOptimizer.className = "RMSProp";
-registerClass(RMSPropOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class OptimizerConstructors {
-  static sgd(learningRate) {
-    return new SGDOptimizer(learningRate);
-  }
-  static momentum(learningRate, momentum, useNesterov = false) {
-    return new MomentumOptimizer(learningRate, momentum, useNesterov);
-  }
-  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon2, centered);
-  }
-  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null) {
-    return new AdamOptimizer(learningRate, beta1, beta2, epsilon2);
-  }
-  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon2 = null) {
-    return new AdadeltaOptimizer(learningRate, rho, epsilon2);
-  }
-  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null, decay = 0) {
-    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon2, decay);
-  }
-  static adagrad(learningRate, initialAccumulatorValue = 0.1) {
-    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/train.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-[
-  MomentumOptimizer,
-  SGDOptimizer,
-  AdadeltaOptimizer,
-  AdagradOptimizer,
-  RMSPropOptimizer,
-  AdamaxOptimizer,
-  AdamOptimizer
-];
-const train = {
-  sgd: OptimizerConstructors.sgd,
-  momentum: OptimizerConstructors.momentum,
-  adadelta: OptimizerConstructors.adadelta,
-  adagrad: OptimizerConstructors.adagrad,
-  rmsprop: OptimizerConstructors.rmsprop,
-  adamax: OptimizerConstructors.adamax,
-  adam: OptimizerConstructors.adam
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/browser_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const delayCallback = (() => {
-  if (typeof requestAnimationFrame !== "undefined") {
-    return requestAnimationFrame;
-  } else if (typeof setImmediate !== "undefined") {
-    return setImmediate;
-  }
-  return (f) => f();
-})();
-function nextFrame() {
-  return new Promise((resolve) => delayCallback(() => resolve()));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getImageCenter(center, imageHeight, imageWidth) {
-  const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
-  const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
-  return [centerX, centerY];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getReshaped(inputShape, blockShape, prod3, batchToSpace = true) {
-  let reshaped = [];
-  if (batchToSpace) {
-    reshaped = reshaped.concat(blockShape.slice(0));
-    reshaped.push(inputShape[0] / prod3);
-    reshaped = reshaped.concat(inputShape.slice(1));
-  } else {
-    reshaped = reshaped.concat(inputShape[0]);
-    const spatialLength = blockShape.length;
-    for (let i = 0; i < spatialLength; ++i) {
-      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
-    }
-    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
-  }
-  return reshaped;
-}
-function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
-  const permuted = [];
-  if (batchToSpace) {
-    permuted.push(blockShapeRank);
-    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
-      if (i <= 2 * blockShapeRank) {
-        permuted.push(i);
-        permuted.push(i - (blockShapeRank + 1));
-      } else {
-        permuted.push(i);
-      }
-    }
-  } else {
-    const permutedBeforeBatch = [];
-    const permutedAfterBatch = [];
-    for (let i = 1; i < reshapedRank; ++i) {
-      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
-        permutedAfterBatch.push(i);
-      } else {
-        permutedBeforeBatch.push(i);
-      }
-    }
-    permuted.push(...permutedBeforeBatch);
-    permuted.push(0);
-    permuted.push(...permutedAfterBatch);
-  }
-  return permuted;
-}
-function getReshapedPermuted(inputShape, blockShape, prod3, batchToSpace = true) {
-  const reshapedPermuted = [];
-  if (batchToSpace) {
-    reshapedPermuted.push(inputShape[0] / prod3);
-  } else {
-    reshapedPermuted.push(inputShape[0] * prod3);
-  }
-  for (let i = 1; i < inputShape.length; ++i) {
-    if (i <= blockShape.length) {
-      if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
-      } else {
-        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
-      }
-    } else {
-      reshapedPermuted.push(inputShape[i]);
-    }
-  }
-  return reshapedPermuted;
-}
-function getSliceBeginCoords(crops, blockShape) {
-  const sliceBeginCoords = [0];
-  for (let i = 0; i < blockShape; ++i) {
-    sliceBeginCoords.push(crops[i][0]);
-  }
-  return sliceBeginCoords;
-}
-function getSliceSize(uncroppedShape, crops, blockShape) {
-  const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i = 0; i < blockShape; ++i) {
-    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
-  }
-  return sliceSize;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const SELU_SCALEALPHA = 1.7580993408473768;
-const SELU_SCALE = 1.0507009873554805;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ERF_P = 0.3275911;
-const ERF_A1 = 0.254829592;
-const ERF_A2 = -0.284496736;
-const ERF_A3 = 1.421413741;
-const ERF_A4 = -1.453152027;
-const ERF_A5 = 1.061405429;
-
-// node_modules/@tensorflow/tfjs-core/dist/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function warn(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.warn(...msg);
-  }
-}
-function log6(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.log(...msg);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mergeRealAndImagArrays(real6, imag6) {
-  if (real6.length !== imag6.length) {
-    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real6.length}, imag: ${imag6.length}.`);
-  }
-  const result = new Float32Array(real6.length * 2);
-  for (let i = 0; i < result.length; i += 2) {
-    result[i] = real6[i / 2];
-    result[i + 1] = imag6[i / 2];
-  }
-  return result;
-}
-function splitRealAndImagArrays(complex9) {
-  const real6 = new Float32Array(complex9.length / 2);
-  const imag6 = new Float32Array(complex9.length / 2);
-  for (let i = 0; i < complex9.length; i += 2) {
-    real6[i / 2] = complex9[i];
-    imag6[i / 2] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithEvenIndex(complex9) {
-  const len = Math.ceil(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 0; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithOddIndex(complex9) {
-  const len = Math.floor(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 2; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function getComplexWithIndex(complex9, index) {
-  const real6 = complex9[index * 2];
-  const imag6 = complex9[index * 2 + 1];
-  return {real: real6, imag: imag6};
-}
-function assignToTypedArray(data, real6, imag6, index) {
-  data[index * 2] = real6;
-  data[index * 2 + 1] = imag6;
-}
-function exponents(n, inverse) {
-  const real6 = new Float32Array(n / 2);
-  const imag6 = new Float32Array(n / 2);
-  for (let i = 0; i < Math.ceil(n / 2); i++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
-    real6[i] = Math.cos(x);
-    imag6[i] = Math.sin(x);
-  }
-  return {real: real6, imag: imag6};
-}
-function exponent(k, n, inverse) {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
-  const real6 = Math.cos(x);
-  const imag6 = Math.sin(x);
-  return {real: real6, imag: imag6};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
-const backend_util_exports = {};
-__export(backend_util_exports, {
-  ERF_A1: () => ERF_A1,
-  ERF_A2: () => ERF_A2,
-  ERF_A3: () => ERF_A3,
-  ERF_A4: () => ERF_A4,
-  ERF_A5: () => ERF_A5,
-  ERF_P: () => ERF_P,
-  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
-  SELU_SCALE: () => SELU_SCALE,
-  SELU_SCALEALPHA: () => SELU_SCALEALPHA,
-  applyActivation: () => applyActivation,
-  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
-  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
-  assertParamsConsistent: () => assertParamsConsistent,
-  assignToTypedArray: () => assignToTypedArray,
-  axesAreInnerMostDims: () => axesAreInnerMostDims,
-  calculateShapes: () => calculateShapes,
-  castTensor: () => castTensor,
-  combineLocations: () => combineLocations,
-  complexWithEvenIndex: () => complexWithEvenIndex,
-  complexWithOddIndex: () => complexWithOddIndex,
-  computeConv2DInfo: () => computeConv2DInfo,
-  computeConv3DInfo: () => computeConv3DInfo,
-  computeDefaultPad: () => computeDefaultPad,
-  computeDilation2DInfo: () => computeDilation2DInfo,
-  computeOptimalWindowSize: () => computeOptimalWindowSize,
-  computeOutAndReduceShapes: () => computeOutAndReduceShapes,
-  computeOutShape: () => computeOutShape2,
-  computePool2DInfo: () => computePool2DInfo,
-  computePool3DInfo: () => computePool3DInfo,
-  convertConv2DDataFormat: () => convertConv2DDataFormat,
-  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
-  expandShapeToKeepDim: () => expandShapeToKeepDim,
-  exponent: () => exponent,
-  exponents: () => exponents,
-  getAxesPermutation: () => getAxesPermutation,
-  getBroadcastDims: () => getBroadcastDims,
-  getComplexWithIndex: () => getComplexWithIndex,
-  getFusedBiasGradient: () => getFusedBiasGradient,
-  getFusedDyActivation: () => getFusedDyActivation,
-  getImageCenter: () => getImageCenter,
-  getInnerMostAxes: () => getInnerMostAxes,
-  getPermuted: () => getPermuted,
-  getReductionAxes: () => getReductionAxes,
-  getReshaped: () => getReshaped,
-  getReshapedPermuted: () => getReshapedPermuted,
-  getSliceBeginCoords: () => getSliceBeginCoords,
-  getSliceSize: () => getSliceSize,
-  getUndoAxesPermutation: () => getUndoAxesPermutation,
-  linspaceImpl: () => linspaceImpl,
-  log: () => log6,
-  mergeRealAndImagArrays: () => mergeRealAndImagArrays,
-  prepareAndValidate: () => prepareAndValidate,
-  prepareSplitSize: () => prepareSplitSize,
-  reshapeTensor: () => reshapeTensor,
-  segment_util: () => segment_util_exports,
-  shouldFuse: () => shouldFuse,
-  slice_util: () => slice_util_exports,
-  splitRealAndImagArrays: () => splitRealAndImagArrays,
-  tupleValuesAreOne: () => tupleValuesAreOne,
-  upcastType: () => upcastType,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape,
-  warn: () => warn
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function castTensor(x, dtype, backend2) {
-  if (dtype === "complex64") {
-    if (x.dtype === "complex64") {
-      return x.clone();
-    }
-    const zerosTensor = zeros(x.shape);
-    const floatX = cast(x, "float32");
-    const result = backend2.complex(floatX, zerosTensor);
-    zerosTensor.dispose();
-    floatX.dispose();
-    return result;
-  }
-  if (!hasEncodingLoss(x.dtype, dtype)) {
-    return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
-  }
-  if (x.dtype === "complex64") {
-    const real6 = backend2.real(x);
-    const result = cast(real6, dtype);
-    real6.dispose();
-    return result;
-  }
-  if (dtype === "int32") {
-    return backend2.int(x);
-  } else if (dtype === "bool") {
-    const zero = scalar(0, x.dtype);
-    const result = backend2.notEqual(x, zero);
-    zero.dispose();
-    return result;
-  } else {
-    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-  }
-}
-function reshapeTensor(x, shape) {
-  return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
-}
-function linspaceImpl(start, stop, num) {
-  const step7 = (stop - start) / (num - 1);
-  const values = makeZerosTypedArray(num, "float32");
-  values[0] = start;
-  for (let i = 1; i < values.length; i++) {
-    values[i] = values[i - 1] + step7;
-  }
-  return tensor1d(values, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/split_shared.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split5(x, sizeSplits, axis) {
-  const begin = new Array(x.rank).fill(0);
-  const size = x.shape.slice();
-  return sizeSplits.map((s) => {
-    const sliceSize = [...size];
-    sliceSize[axis] = s;
-    const sliceT = slice(x, begin, sliceSize);
-    begin[axis] += s;
-    return sliceT;
-  });
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/tile_impl.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile4(xBuf, reps) {
-  const newShape = new Array(xBuf.rank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = xBuf.shape[i] * reps[i];
-  }
-  const result = buffer(newShape, xBuf.dtype);
-  for (let i = 0; i < result.values.length; ++i) {
-    const newLoc = result.indexToLoc(i);
-    const originalLoc = new Array(xBuf.rank);
-    for (let j = 0; j < originalLoc.length; j++) {
-      originalLoc[j] = newLoc[j] % xBuf.shape[j];
-    }
-    const originalIndex = xBuf.locToIndex(originalLoc);
-    result.values[i] = xBuf.values[originalIndex];
-  }
-  return result.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/topk_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topkImpl(x, xShape, xDtype, k, sorted) {
-  const lastDim = xShape[xShape.length - 1];
-  const [batch, size] = [x.length / lastDim, lastDim];
-  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
-  const allTopKIndices = getTypedArrayFromDType("int32", batch * k);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = x.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    const outOffset = b * k;
-    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
-    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i = 0; i < k; i++) {
-      topKVals[i] = valAndInd[i].value;
-      topKIndices[i] = valAndInd[i].index;
-    }
-  }
-  const outputShape = xShape.slice();
-  outputShape[outputShape.length - 1] = k;
-  return [
-    tensor4(allTopKVals, outputShape, xDtype),
-    tensor4(allTopKIndices, outputShape, "int32")
-  ];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
-const kernel_impls_exports = {};
-__export(kernel_impls_exports, {
-  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
-  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
-  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
-  split: () => split5,
-  tile: () => tile4,
-  topkImpl: () => topkImpl,
-  whereImpl: () => whereImpl
-});
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const EPSILON_FLOAT32 = 1e-7;
-const EPSILON_FLOAT16 = 1e-4;
-class DataStorage {
-  constructor(backend2, dataMover) {
-    this.backend = backend2;
-    this.dataMover = dataMover;
-    this.data = new WeakMap();
-    this.dataIdsCount = 0;
-  }
-  get(dataId) {
-    if (!this.data.has(dataId)) {
-      this.dataMover.moveData(this.backend, dataId);
-    }
-    return this.data.get(dataId);
-  }
-  set(dataId, value) {
-    this.dataIdsCount++;
-    this.data.set(dataId, value);
-  }
-  has(dataId) {
-    return this.data.has(dataId);
-  }
-  delete(dataId) {
-    this.dataIdsCount--;
-    return this.data.delete(dataId);
-  }
-  numDataIds() {
-    return this.dataIdsCount;
-  }
-}
-class KernelBackend {
-  time(f) {
-    return notYetImplemented("time");
-  }
-  read(dataId) {
-    return notYetImplemented("read");
-  }
-  readSync(dataId) {
-    return notYetImplemented("readSync");
-  }
-  numDataIds() {
-    return notYetImplemented("numDataIds");
-  }
-  disposeData(dataId) {
-    return notYetImplemented("disposeData");
-  }
-  write(values, shape, dtype) {
-    return notYetImplemented("write");
-  }
-  move(dataId, values, shape, dtype) {
-    return notYetImplemented("move");
-  }
-  memory() {
-    return notYetImplemented("memory");
-  }
-  floatPrecision() {
-    return notYetImplemented("floatPrecision");
-  }
-  epsilon() {
-    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-  }
-  batchMatMul(a, b, transposeA, transposeB) {
-    return notYetImplemented("batchMatMul");
-  }
-  fusedBatchMatMul({a, b, transposeA, transposeB, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedBatchMatMul");
-  }
-  slice(x, begin, size) {
-    return notYetImplemented("slice");
-  }
-  stridedSlice(x, begin, end, strides) {
-    return notYetImplemented("stridedSlice");
-  }
-  unstack(x, axis) {
-    return notYetImplemented("unstack");
-  }
-  reverse(a, axis) {
-    return notYetImplemented("reverse");
-  }
-  concat(tensors, axis) {
-    return notYetImplemented("concat");
-  }
-  neg(a) {
-    return notYetImplemented("neg");
-  }
-  add(a, b) {
-    return notYetImplemented("add");
-  }
-  addN(tensors) {
-    return notYetImplemented("addN");
-  }
-  subtract(a, b) {
-    return notYetImplemented("subtract");
-  }
-  multiply(a, b) {
-    return notYetImplemented("multiply");
-  }
-  realDivide(a, b) {
-    return notYetImplemented("realDivide");
-  }
-  floorDiv(a, b) {
-    return notYetImplemented("floorDiv");
-  }
-  sum(x, axes) {
-    return notYetImplemented("sum");
-  }
-  prod(x, axes) {
-    return notYetImplemented("prod");
-  }
-  unsortedSegmentSum(x, segmentIds, numSegments) {
-    return notYetImplemented("unsortedSegmentSum");
-  }
-  argMin(x, axis) {
-    return notYetImplemented("argMin");
-  }
-  argMax(x, axis) {
-    return notYetImplemented("argMax");
-  }
-  equal(a, b) {
-    return notYetImplemented("equal");
-  }
-  notEqual(a, b) {
-    return notYetImplemented("notEqual");
-  }
-  less(a, b) {
-    return notYetImplemented("less");
-  }
-  lessEqual(a, b) {
-    return notYetImplemented("lessEqual");
-  }
-  greater(a, b) {
-    return notYetImplemented("greater");
-  }
-  greaterEqual(a, b) {
-    return notYetImplemented("greaterEqual");
-  }
-  logicalNot(a) {
-    return notYetImplemented("logicalNot");
-  }
-  logicalAnd(a, b) {
-    return notYetImplemented("logicalAnd");
-  }
-  logicalOr(a, b) {
-    return notYetImplemented("logicalOr");
-  }
-  where(condition) {
-    return notYetImplemented("where");
-  }
-  select(condition, a, b) {
-    return notYetImplemented("select");
-  }
-  topk(x, k, sorted) {
-    return notYetImplemented("topk");
-  }
-  min(x, axes) {
-    return notYetImplemented("min");
-  }
-  minimum(a, b) {
-    return notYetImplemented("minimum");
-  }
-  mod(a, b) {
-    return notYetImplemented("mod");
-  }
-  max(x, axes) {
-    return notYetImplemented("max");
-  }
-  maximum(a, b) {
-    return notYetImplemented("maximum");
-  }
-  all(x, axes) {
-    return notYetImplemented("all");
-  }
-  any(x, axes) {
-    return notYetImplemented("any");
-  }
-  squaredDifference(a, b) {
-    return notYetImplemented("squaredDifference");
-  }
-  ceil(x) {
-    return notYetImplemented("ceil");
-  }
-  floor(x) {
-    return notYetImplemented("floor");
-  }
-  round(x) {
-    return notYetImplemented("round");
-  }
-  sign(x) {
-    return notYetImplemented("sign");
-  }
-  isNaN(x) {
-    return notYetImplemented("isNaN");
-  }
-  isInf(x) {
-    return notYetImplemented("isInf");
-  }
-  isFinite(x) {
-    return notYetImplemented("isFinite");
-  }
-  pow(a, b) {
-    return notYetImplemented("pow");
-  }
-  exp(x) {
-    return notYetImplemented("exp");
-  }
-  expm1(x) {
-    return notYetImplemented("expm1");
-  }
-  softmax(x, dim) {
-    return notYetImplemented("softmax");
-  }
-  log(x) {
-    return notYetImplemented("log");
-  }
-  log1p(x) {
-    return notYetImplemented("log1p");
-  }
-  sqrt(x) {
-    return notYetImplemented("sqrt");
-  }
-  rsqrt(x) {
-    return notYetImplemented("rsqrt");
-  }
-  square(x) {
-    return notYetImplemented("square");
-  }
-  reciprocal(x) {
-    return notYetImplemented("reciprocal");
-  }
-  relu(x) {
-    return notYetImplemented("relu");
-  }
-  relu6(x) {
-    return notYetImplemented("relu6");
-  }
-  prelu(x, a) {
-    return notYetImplemented("prelu");
-  }
-  elu(x) {
-    return notYetImplemented("elu");
-  }
-  eluDer(dy, y) {
-    return notYetImplemented("eluDer");
-  }
-  selu(x) {
-    return notYetImplemented("selu");
-  }
-  int(x) {
-    return notYetImplemented("int");
-  }
-  clip(x, min4, max6) {
-    return notYetImplemented("clip");
-  }
-  abs(x) {
-    return notYetImplemented("abs");
-  }
-  complexAbs(x) {
-    return notYetImplemented("complexAbs");
-  }
-  sigmoid(x) {
-    return notYetImplemented("sigmoid");
-  }
-  softplus(x) {
-    return notYetImplemented("softplus");
-  }
-  sin(x) {
-    return notYetImplemented("sin");
-  }
-  cos(x) {
-    return notYetImplemented("cos");
-  }
-  tan(x) {
-    return notYetImplemented("tan");
-  }
-  asin(x) {
-    return notYetImplemented("asin");
-  }
-  acos(x) {
-    return notYetImplemented("acos");
-  }
-  atan(x) {
-    return notYetImplemented("atan");
-  }
-  atan2(a, b) {
-    return notYetImplemented("atan2");
-  }
-  sinh(x) {
-    return notYetImplemented("sinh");
-  }
-  cosh(x) {
-    return notYetImplemented("cosh");
-  }
-  tanh(x) {
-    return notYetImplemented("tanh");
-  }
-  asinh(x) {
-    return notYetImplemented("asinh");
-  }
-  acosh(x) {
-    return notYetImplemented("acosh");
-  }
-  atanh(x) {
-    return notYetImplemented("atanh");
-  }
-  erf(x) {
-    return notYetImplemented("erf");
-  }
-  step(x, alpha) {
-    return notYetImplemented("step");
-  }
-  fusedConv2d({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedConv2d");
-  }
-  conv2d(x, filter, convInfo) {
-    return notYetImplemented("conv2d");
-  }
-  conv2dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv2dDerInput");
-  }
-  conv2dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv2dDerFilter");
-  }
-  fusedDepthwiseConv2D({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedDepthwiseConv2D");
-  }
-  depthwiseConv2D(input, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2D");
-  }
-  depthwiseConv2DDerInput(dy, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerInput");
-  }
-  depthwiseConv2DDerFilter(x, dY, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerFilter");
-  }
-  conv3d(x, filter, convInfo) {
-    return notYetImplemented("conv3d");
-  }
-  conv3dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv3dDerInput");
-  }
-  conv3dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv3dDerFilter");
-  }
-  maxPool(x, convInfo) {
-    return notYetImplemented("maxPool");
-  }
-  maxPoolBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPoolBackprop");
-  }
-  avgPool(x, convInfo) {
-    return notYetImplemented("avgPool");
-  }
-  avgPoolBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPoolBackprop");
-  }
-  avgPool3d(x, convInfo) {
-    return notYetImplemented("avgPool3d");
-  }
-  avgPool3dBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPool3dBackprop");
-  }
-  maxPool3d(x, convInfo) {
-    return notYetImplemented("maxPool3d");
-  }
-  maxPool3dBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPool3dBackprop");
-  }
-  reshape(x, shape) {
-    return notYetImplemented("reshape");
-  }
-  cast(x, dtype) {
-    return notYetImplemented("cast");
-  }
-  tile(x, reps) {
-    return notYetImplemented("tile");
-  }
-  pad(x, paddings, constantValue) {
-    return notYetImplemented("pad");
-  }
-  transpose(x, perm) {
-    return notYetImplemented("transpose");
-  }
-  gather(x, indices, axis) {
-    return notYetImplemented("gather");
-  }
-  gatherND(x, indices) {
-    return notYetImplemented("gatherND");
-  }
-  scatterND(indices, updates, shape) {
-    return notYetImplemented("scatterND");
-  }
-  batchToSpaceND(x, blockShape, crops) {
-    return notYetImplemented("batchToSpaceND");
-  }
-  spaceToBatchND(x, blockShape, paddings) {
-    return notYetImplemented("spaceToBatchND");
-  }
-  resizeBilinear(x, newHeight, newWidth, alignCorners) {
-    return notYetImplemented("resizeBilinear");
-  }
-  resizeBilinearBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeBilinearBackprop");
-  }
-  resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {
-    return notYetImplemented("resizeNearestNeighbor");
-  }
-  resizeNearestNeighborBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeNearestNeighborBackprop");
-  }
-  batchNorm(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    return notYetImplemented("batchNorm");
-  }
-  localResponseNormalization4D(x, radius, bias, alpha, beta) {
-    return notYetImplemented("localResponseNormalization4D");
-  }
-  LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {
-    return notYetImplemented("LRNGrad");
-  }
-  multinomial(logits, normalized, numSamples, seed) {
-    return notYetImplemented("multinomial");
-  }
-  oneHot(indices, depth, onValue, offValue) {
-    return notYetImplemented("oneHot");
-  }
-  cumsum(x, axis, exclusive, reverse9) {
-    return notYetImplemented("cumsum");
-  }
-  nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-    return notYetImplemented("nonMaxSuppression");
-  }
-  fft(x) {
-    return notYetImplemented("fft");
-  }
-  ifft(x) {
-    return notYetImplemented("ifft");
-  }
-  complex(real6, imag6) {
-    return notYetImplemented("complex");
-  }
-  real(input) {
-    return notYetImplemented("real");
-  }
-  imag(input) {
-    return notYetImplemented("imag");
-  }
-  cropAndResize(image2, boxes, boxIndex, cropSize, method, extrapolationValue) {
-    return notYetImplemented("cropAndResize");
-  }
-  depthToSpace(x, blockSize, dataFormat) {
-    return notYetImplemented("depthToSpace");
-  }
-  split(value, sizeSplits, axis) {
-    return notYetImplemented("split");
-  }
-  sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {
-    return notYetImplemented("sparseToDense");
-  }
-  diag(x) {
-    return notYetImplemented("diag");
-  }
-  fill(shape, value, dtype) {
-    return notYetImplemented("fill");
-  }
-  onesLike(x) {
-    return notYetImplemented("onesLike");
-  }
-  zerosLike(x) {
-    return notYetImplemented("zerosLike");
-  }
-  linspace(start, stop, num) {
-    return notYetImplemented("linspace");
-  }
-  dispose() {
-    return notYetImplemented("dispose");
-  }
-}
-function notYetImplemented(kernelName) {
-  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. Did you forget to import the kernel?`);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const absGradConfig = {
-  kernelName: Abs,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, step(cast(x, "float32"), -1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acosGradConfig = {
-  kernelName: Acos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = square(cast(x, "float32"));
-        const b = sqrt(sub(scalar(1), a));
-        return neg(div(dy, b));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acoshGradConfig = {
-  kernelName: Acosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(sub(square(cast(x, "float32")), 1));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addGradConfig = {
-  kernelName: Add,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addNGradConfig = {
-  kernelName: AddN,
-  saveAllInputs: true,
-  gradFunc: (dy, saved) => {
-    const ders = {};
-    saved.forEach((_, i) => {
-      ders[i] = () => dy.clone();
-    });
-    return ders;
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMaxGradConfig = {
-  kernelName: ArgMax,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMinGradConfig = {
-  kernelName: ArgMin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinGradConfig = {
-  kernelName: Asin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32")))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinhGradConfig = {
-  kernelName: Asinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atan2GradConfig = {
-  kernelName: Atan2,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const d = add2(square(a), square(b));
-      let res = mul(dy, div(b, d));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      const d = add2(square(a), square(b));
-      let res = neg(mul(dy, div(a, d)));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanGradConfig = {
-  kernelName: Atan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(square(cast(x, "float32")), 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanhGradConfig = {
-  kernelName: Atanh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sub(scalar(1), square(cast(x, "float32"))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "avgPool3dBackprop");
-  const $input = convertToTensor(input, "input", "avgPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.avgPool3dBackprop(dy5D, input5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3dBackprop = op({avgPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPool3DGradConfig = {
-  kernelName: AvgPool3D,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPoolBackprop_(dy, input, filterSize, strides, pad8) {
-  const $dy = convertToTensor(dy, "dy", "avgPoolBackprop");
-  const $input = convertToTensor(input, "input", "avgPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  let input4D = $input;
-  let dy4D = $dy;
-  let reshapedTo4D = false;
-  if ($input.rank === 3) {
-    reshapedTo4D = true;
-    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
-    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
-  }
-  assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`);
-  assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo(input4D.shape, filterSize, strides, 1, pad8);
-    return backend2.avgPoolBackprop(dy4D, input4D, convInfo);
-  };
-  const inputs = {dy: dy4D, input: input4D};
-  const attrs = {filterSize, strides, pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPoolBackprop = op({avgPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPoolGradConfig = {
-  kernelName: AvgPool,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => avgPoolBackprop(dy, x, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchMatMulGradConfig = {
-  kernelName: BatchMatMul,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved, attrs) => {
-    const [a, b] = saved;
-    const {transposeA, transposeB} = attrs;
-    if (!transposeA && !transposeB) {
-      return {
-        a: () => matMul(dy, b, false, true),
-        b: () => matMul(a, dy, true, false)
-      };
-    } else if (!transposeA && transposeB) {
-      return {
-        a: () => matMul(dy, b, false, false),
-        b: () => matMul(dy, a, true, false)
-      };
-    } else if (transposeA && !transposeB) {
-      return {
-        a: () => matMul(b, dy, false, true),
-        b: () => matMul(a, dy, false, false)
-      };
-    } else {
-      return {
-        a: () => matMul(b, dy, true, true),
-        b: () => matMul(dy, a, true, true)
-      };
-    }
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchToSpaceNDGradConfig = {
-  kernelName: BatchToSpaceND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, crops} = attrs;
-    return {x: () => spaceToBatchND(dy, blockShape, crops)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const broadcastToGradConfig = {
-  kernelName: BroadcastTo,
-  gradFunc: (dy, saved, attrs) => {
-    const broadCastToAttrs = attrs;
-    const inputShape = broadCastToAttrs.inputShape;
-    const outputShape = broadCastToAttrs.shape;
-    const reps = Array.from(outputShape);
-    for (let i = inputShape.length - 1; i >= 0; i--) {
-      if (inputShape[i] === outputShape[i]) {
-        reps[i] = 1;
-      } else if (inputShape[i] !== 1) {
-        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
-      }
-    }
-    const axes = [];
-    for (let i = 0; i < reps.length; i++) {
-      if (reps[i] > 1) {
-        axes.push(i);
-      }
-    }
-    return {x: () => sum2(dy, axes, true)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const castGradConfig = {
-  kernelName: Cast,
-  gradFunc: (dy) => {
-    return {x: () => dy.clone()};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ceilGradConfig = {
-  kernelName: Ceil,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const clipByValueGradConfig = {
-  kernelName: ClipByValue,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {clipValueMin, clipValueMax} = attrs;
-    return {
-      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const concatGradConfig = {
-  kernelName: Concat,
-  saveAllInputs: true,
-  gradFunc: (dy, saved, attrs) => {
-    const shapes = saved.map((t) => t.shape);
-    const {axis} = attrs;
-    const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map((s) => s[$axis]);
-    const derTensors = split(dy, sizeSplits, $axis);
-    return derTensors.map((t) => () => t);
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DGradConfig = {
-  kernelName: Conv2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x4D, $filter] = saved;
-    const {dilations, strides, pad: pad8, dataFormat} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    return {
-      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad8, dataFormat),
-      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad8, dataFormat)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DBackpropInputGradConfig = {
-  kernelName: Conv2DBackpropInput,
-  inputsToSave: ["dy", "filter"],
-  gradFunc: (ddx, saved, attrs) => {
-    const [dy, filter] = saved;
-    const {strides, pad: pad8, dataFormat, dimRoundingMode} = attrs;
-    return {
-      dy: () => conv2d(ddx, filter, strides, pad8, dataFormat, 1, dimRoundingMode),
-      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad8, dataFormat, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropFilter_(x, dy, filterShape, strides, pad8) {
-  let x5D = x;
-  if (x.rank === 4) {
-    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
-  }
-  let dy5D = dy;
-  if (dy5D.rank === 4) {
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
-  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
-  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
-  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad8);
-    return backend2.conv3dDerFilter(x5D, dy5D, convInfo);
-  };
-  const inputs = {x: x5D, y: dy5D};
-  const attrs = {strides, pad: pad8};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);
-}
-const conv3DBackpropFilter = op({conv3DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv3DGradConfig = {
-  kernelName: Conv3D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const [x5D, $filter] = saved;
-    return {
-      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad8),
-      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cosGradConfig = {
-  kernelName: Cos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(neg(sin(cast(x, "float32"))), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const coshGradConfig = {
-  kernelName: Cosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(sinh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cumsumGradConfig = {
-  kernelName: Cumsum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {axis, exclusive, reverse: reverse9} = attrs;
-    return {
-      x: () => {
-        const permutation = getAxesPermutation([axis], x.rank);
-        let out = cumsum(dy, axis, exclusive, !reverse9);
-        if (permutation != null) {
-          out = transpose(out, permutation);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const depthwiseConv2dNativeGradConfig = {
-  kernelName: DepthwiseConv2dNative,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1] : dilations;
-    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
-    const [x, filter] = saved;
-    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
-    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
-    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const convInfo = computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad8, dimRoundingMode, true);
-    return {
-      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),
-      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const dilation2dGradConfig = {
-  kernelName: Dilation2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, filter] = saved;
-    const inputInputs = {x, filter, dy};
-    const filterInputs = {x, filter, dy};
-    return {
-      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
-      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Div_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const divGradConfig = {
-  kernelName: Div,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const eluGradConfig = {
-  kernelName: Elu,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    const backPropKernelFunc = (backend2) => {
-      return backend2.eluDer(dy, y);
-    };
-    const inputs = {dy, y};
-    return {
-      x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, EluGrad)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const erfGradConfig = {
-  kernelName: Erf,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
-    return {x: () => mul(dy, a)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expGradConfig = {
-  kernelName: Exp,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, y)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expm1GradConfig = {
-  kernelName: Expm1,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, exp(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorGradConfig = {
-  kernelName: Floor,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorDivGradConfig = {
-  kernelName: FloorDiv,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const fusedBatchNormGradConfig = {
-  kernelName: FusedBatchNorm,
-  inputsToSave: ["x", "mean", "variance", "scale"],
-  gradFunc: (dy, saved, attrs) => {
-    const {varianceEpsilon} = attrs;
-    const [x, mean5, variance, scale2] = saved;
-    const scaleValue = scale2 == null ? scalar(1) : scale2;
-    const reductionAxes = getReductionAxes(mean5.shape, x.shape);
-    const tileShape = [];
-    if (mean5.rank === 1) {
-      for (let i = 0; i < x.shape.length - 1; ++i) {
-        tileShape.push(x.shape[i]);
-      }
-      tileShape.push(1);
-    }
-    const xMinusMean = sub(x, mean5);
-    const dyTimesScaleValue = mul(dy, scaleValue);
-    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
-    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
-    const derX = () => {
-      if (mean5.rank === 1) {
-        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
-      } else {
-        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
-      }
-    };
-    const derMean = () => {
-      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        meanDer = sum2(meanDer, reductionAxes);
-      }
-      return reshape(meanDer, mean5.shape);
-    };
-    const derVariance = () => {
-      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        varianceDer = sum2(varianceDer, reductionAxes);
-      }
-      return reshape(varianceDer, mean5.shape);
-    };
-    const derScale = () => {
-      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
-      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
-      if (mean5.rank === 1) {
-        scaleDer = sum2(scaleDer, reductionAxes);
-      }
-      return reshape(scaleDer, mean5.shape);
-    };
-    const derOffset = () => {
-      let offsetDer = dy;
-      if (mean5.rank === 1) {
-        offsetDer = sum2(offsetDer, reductionAxes);
-      }
-      return reshape(offsetDer, mean5.shape);
-    };
-    return {
-      x: derX,
-      mean: derMean,
-      variance: derVariance,
-      scale: derScale,
-      offset: derOffset
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gatherGradConfig = {
-  kernelName: GatherV2,
-  inputsToSave: ["x", "indices"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, indices] = saved;
-    const {axis} = attrs;
-    const parsedAxis = parseAxisParam(axis, x.shape)[0];
-    const derX = () => {
-      const paramsShape = x.shape;
-      const indicesSize = indices.size;
-      const outerShape = paramsShape.slice(0, parsedAxis);
-      const outerDims = outerShape.length;
-      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
-      const innerDims = innerShape.length;
-      const outerAxesIndices = arrayRange(0, outerDims);
-      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
-      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
-      const values = reshape(dy, valuesShape);
-      const reshapedIndices = reshape(indices, [indicesSize]);
-      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
-      const valuesTranspose = transpose(values, transposeDims);
-      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
-      const invertTransposeDims = getUndoAxesPermutation(transposeDims);
-      paramsGrad = transpose(paramsGrad, invertTransposeDims);
-      return paramsGrad;
-    };
-    return {x: derX, indices: () => indices};
-  }
-};
-function arrayRange(start, stop) {
-  const result = [];
-  for (let i = start; i < stop; ++i) {
-    result.push(i);
-  }
-  return result;
-}
-function arrayConcat(arrays) {
-  const result = [];
-  for (let i = 0; i < arrays.length; ++i) {
-    for (let j = 0; j < arrays[i].length; ++j) {
-      result.push(arrays[i][j]);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const greaterEqualGradConfig = {
-  kernelName: GreaterEqual,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    return {a: () => zerosLike(a), b: () => zerosLike(b)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const identityGradConfig = {
-  kernelName: Identity,
-  gradFunc: (dy) => {
-    return {x: () => cast(dy, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isFiniteGradConfig = {
-  kernelName: IsFinite,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isInfGradConfig = {
-  kernelName: IsInf,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isNanGradConfig = {
-  kernelName: IsNan,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const log1pGradConfig = {
-  kernelName: Log1p,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(x, 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logGradConfig = {
-  kernelName: Log,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, cast(x, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logSoftmaxGradConfig = {
-  kernelName: LogSoftmax,
-  inputsToSave: [],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [value] = saved;
-    const {axis} = attrs;
-    return {
-      logits: () => {
-        const keepDims = true;
-        const softmax3 = exp(value);
-        return sub(dy, mul(sum2(dy, axis, keepDims), softmax3));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const forward = (backend2) => backend2.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);
-  const inputs = {x, y, dy};
-  const attrs = {depthRadius, bias, alpha, beta};
-  return ENGINE.runKernelFunc(forward, inputs, null, LRNBackprop, attrs);
-}
-const localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const lrnGradConfig = {
-  kernelName: LRN,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {depthRadius, bias, alpha, beta} = attrs;
-    return {
-      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
-  if (y.rank < xOrig.rank) {
-    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
-  }
-  if (dy.rank < xOrig.rank) {
-    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
-  }
-  return {
-    x: () => {
-      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
-      return permutedAxes == null ? dx : transpose(dx, permutedAxes);
-    }
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxGradConfig = {
-  kernelName: Max,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const maxAttrs = attrs;
-    const {reductionIndices} = maxAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(reductionIndices, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = maxGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maximumGradConfig = {
-  kernelName: Maximum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(less(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPool3dBackprop");
-  const $input = convertToTensor(input, "input", "maxPool3dBackprop");
-  const $output = convertToTensor(output, "output", "maxPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let output5D = $output;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-    output5D = reshape($output, [
-      1,
-      $output.shape[0],
-      $output.shape[1],
-      $output.shape[2],
-      $output.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D, output: output5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3dBackprop = op({maxPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPool3DGradConfig = {
-  kernelName: MaxPool3D,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolBackprop_(dy, input, output, filterSize, strides, pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPoolBackprop");
-  const $input = convertToTensor(input, "input", "maxPoolBackprop");
-  const $output = convertToTensor(output, "output", "maxPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`);
-  assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo($input.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    return backend2.maxPoolBackprop($dy, $input, $output, convInfo);
-  };
-  const inputs = {dy: $dy, input: $input, output: $output};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);
-}
-const maxPoolBackprop = op({maxPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPoolGradConfig = {
-  kernelName: MaxPool,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minGradConfig = {
-  kernelName: Min,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const minAttrs = attrs;
-    const {axis} = minAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(axis, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = minGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minimumGradConfig = {
-  kernelName: Minimum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(greater(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const modGradConfig = {
-  kernelName: Mod,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(dy, reduceAxes), a.shape);
-      }
-      return dy;
-    };
-    const derB = () => {
-      const res = mul(dy, neg(floor(div(a, b))));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const multiplyGradConfig = {
-  kernelName: Multiply,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = mul(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      const res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Negate_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const negateGradConfig = {
-  kernelName: Negate,
-  gradFunc: (dy) => {
-    return {x: () => neg(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const oneHotGradConfig = {
-  kernelName: OneHot,
-  inputsToSave: ["indices"],
-  gradFunc: (dy, saved) => {
-    const indices = saved[0];
-    return {indices: () => zeros(indices.shape, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const onesLikeGradConfig = {
-  kernelName: OnesLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const padV2GradConfig = {
-  kernelName: PadV2,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const x = saved[0];
-    const {paddings} = attrs;
-    const begin = paddings.map((p) => p[0]);
-    return {x: () => slice(dy, begin, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const powGradConfig = {
-  kernelName: Pow,
-  inputsToSave: ["a", "b"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [a, b, y] = saved;
-    const base = a;
-    const exp11 = b;
-    const outShape = assertAndGetBroadcastShape(base.shape, exp11.shape);
-    const derBase = () => {
-      const expFloat = cast(exp11, "float32");
-      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
-      const reduceAxes = getReductionAxes(base.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, base.shape);
-    };
-    const derExp = () => {
-      const condition = greater(base, 0);
-      const logBase = where(condition, log(base), zerosLike(base));
-      let res = mul(dy, mul(y, logBase));
-      const reduceAxes = getReductionAxes(exp11.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, exp11.shape);
-    };
-    return {a: derBase, b: derExp};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const preluGradConfig = {
-  kernelName: Prelu,
-  inputsToSave: ["x", "alpha"],
-  gradFunc: (dy, saved) => {
-    const [x, alpha] = saved;
-    const mask = greater(x, 0);
-    return {
-      x: () => where(mask, dy, mul(dy, alpha)),
-      alpha: () => {
-        let res = where(mask, zerosLike(dy), mul(dy, x));
-        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, alpha.shape);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reciprocalGradConfig = {
-  kernelName: Reciprocal,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, neg(square(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const relu6GradConfig = {
-  kernelName: Relu6,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const mask = mul(lessEqual(x, 6), step(x));
-    return {x: () => mul(dy, cast(mask, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reluGradConfig = {
-  kernelName: Relu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, cast(step(x), "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reshapeGradConfig = {
-  kernelName: Reshape,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => reshape(dy, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeBilinearGradConfig = {
-  kernelName: ResizeBilinear,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeBilinearBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeBilinearGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeNearestNeighborGradConfig = {
-  kernelName: ResizeNearestNeighbor,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeNearestNeighborBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeNearestNeighborGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reverseGradConfig = {
-  kernelName: Reverse,
-  gradFunc: (dy, saved, attrs) => {
-    const {dims} = attrs;
-    const axes = parseAxisParam(dims, dy.shape);
-    return {x: () => reverse(dy, axes)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const roundGradConfig = {
-  kernelName: Round,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const rsqrtGradConfig = {
-  kernelName: Rsqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SelectV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const selectV2PoolGradConfig = {
-  kernelName: SelectV2,
-  inputsToSave: ["condition"],
-  gradFunc: (dy, saved) => {
-    const [condition] = saved;
-    return {
-      condition: () => cast(zerosLike(condition), "float32"),
-      t: () => mul(dy, cast(condition, dy.dtype)),
-      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const seluGradConfig = {
-  kernelName: Selu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const mask = greater(x, scalar(0));
-        const scaleAlpha = scalar(SELU_SCALEALPHA);
-        const scale2 = scalar(SELU_SCALE);
-        const greaterThanZeroDer = mul(dy, scale2);
-        const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, "float32")));
-        return where(mask, greaterThanZeroDer, lessEqualZeroDer);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sigmoidGradConfig = {
-  kernelName: Sigmoid,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const signGradConfig = {
-  kernelName: Sign,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinGradConfig = {
-  kernelName: Sin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cos(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinhGradConfig = {
-  kernelName: Sinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cosh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sliceGradConfig = {
-  kernelName: Slice,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {begin, size} = attrs;
-    const inputShape = x.shape;
-    const [begin_, size_] = parseSliceParams(x, begin, size);
-    const paddings = [];
-    for (let i = 0; i < dy.rank; i++) {
-      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
-    }
-    return {x: () => pad(dy, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softmaxGradConfig = {
-  kernelName: Softmax,
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [y] = saved;
-    const {dim} = attrs;
-    const keepDims = true;
-    const dyTimesY = mul(dy, y);
-    return {
-      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softplusGradConfig = {
-  kernelName: Softplus,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, sigmoid(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spaceToBatchNDGradConfig = {
-  kernelName: SpaceToBatchND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, paddings} = attrs;
-    return {x: () => batchToSpaceND(dy, blockShape, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const splitVGradConfig = {
-  kernelName: SplitV,
-  gradFunc: (dy, saved, attrs) => {
-    const {axis} = attrs;
-    return {x: () => concat(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sqrtGradConfig = {
-  kernelName: Sqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, mul(sqrt(cast(x, "float32")), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squareGradConfig = {
-  kernelName: Square,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, mul(cast(x, "float32"), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squaredDifferenceGradConfig = {
-  kernelName: SquaredDifference,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const two = scalar(2);
-    const derA = () => mul(dy, mul(two, sub(a, b)));
-    const derB = () => mul(dy, mul(two, sub(b, a)));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const stepGradConfig = {
-  kernelName: Step,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const subGradConfig = {
-  kernelName: Sub,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(neg(res), b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sumGradConfig = {
-  kernelName: Sum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const expandedDyShape = x.shape.slice();
-    const {axis} = attrs;
-    const axes = parseAxisParam(axis, x.shape);
-    axes.forEach((axis2) => {
-      expandedDyShape[axis2] = 1;
-    });
-    const expandedDy = reshape(dy, expandedDyShape);
-    const derX = mul(expandedDy, ones2(x.shape, "float32"));
-    return {x: () => derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanGradConfig = {
-  kernelName: Tan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, square(cos(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanhGradConfig = {
-  kernelName: Tanh,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(sub(scalar(1), square(y)), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tileGradConfig = {
-  kernelName: Tile,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {reps} = attrs;
-    const derX = () => {
-      let xGrad = zerosLike(x);
-      if (x.rank === 1) {
-        for (let i = 0; i < reps[0]; ++i) {
-          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
-        }
-      } else if (x.rank === 2) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
-              x.shape[0],
-              x.shape[1]
-            ]));
-          }
-        }
-      } else if (x.rank === 3) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
-            }
-          }
-        }
-      } else if (x.rank === 4) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              for (let l = 0; l < reps[3]; ++l) {
-                xGrad = add2(xGrad, slice(dy, [
-                  i * x.shape[0],
-                  j * x.shape[1],
-                  k * x.shape[2],
-                  l * x.shape[3]
-                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
-              }
-            }
-          }
-        }
-      } else {
-        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
-      }
-      return xGrad;
-    };
-    return {x: derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const transposeGradConfig = {
-  kernelName: Transpose,
-  gradFunc: (dy, saved, attrs) => {
-    const transposeAttrs = attrs;
-    const {perm} = transposeAttrs;
-    const undoPerm = getUndoAxesPermutation(perm);
-    return {x: () => transpose(dy, undoPerm)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unpackGradConfig = {
-  kernelName: Unpack,
-  gradFunc: (dy, saved, attrs) => {
-    const unpackAttrs = attrs;
-    const {axis} = unpackAttrs;
-    return {value: () => stack(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unsortedSegmentSumGradConfig = {
-  kernelName: UnsortedSegmentSum,
-  inputsToSave: ["segmentIds"],
-  gradFunc: (dy, saved) => {
-    const [segmentIds] = saved;
-    const derX = () => {
-      return gatherDropNegatives(dy, segmentIds);
-    };
-    return {x: derX};
-  }
-};
-function gatherDropNegatives(x, indices) {
-  const zeroClippedIndices = maximum(indices, zerosLike(indices));
-  const gathered = gather(x, zeroClippedIndices);
-  let isPositive = greaterEqual(indices, scalar(0, "int32"));
-  const numIters = gathered.rank - isPositive.rank;
-  for (let i = 0; i < numIters; ++i) {
-    isPositive = expandDims(isPositive, i + 1);
-  }
-  isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
-  const zeroSlice = zerosLike(gathered);
-  return where(isPositive, gathered, zeroSlice);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const zerosLikeGradConfig = {
-  kernelName: ZerosLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gradConfigs = [
-  absGradConfig,
-  acosGradConfig,
-  acoshGradConfig,
-  addGradConfig,
-  addNGradConfig,
-  argMaxGradConfig,
-  argMinGradConfig,
-  asinGradConfig,
-  asinhGradConfig,
-  atan2GradConfig,
-  atanGradConfig,
-  atanhGradConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulGradConfig,
-  batchToSpaceNDGradConfig,
-  broadcastToGradConfig,
-  castGradConfig,
-  ceilGradConfig,
-  clipByValueGradConfig,
-  concatGradConfig,
-  conv2DBackpropInputGradConfig,
-  conv2DGradConfig,
-  conv3DGradConfig,
-  cosGradConfig,
-  coshGradConfig,
-  cumsumGradConfig,
-  depthwiseConv2dNativeGradConfig,
-  dilation2dGradConfig,
-  divGradConfig,
-  eluGradConfig,
-  erfGradConfig,
-  expGradConfig,
-  expm1GradConfig,
-  floorDivGradConfig,
-  floorGradConfig,
-  fusedBatchNormGradConfig,
-  gatherGradConfig,
-  greaterEqualGradConfig,
-  identityGradConfig,
-  isFiniteGradConfig,
-  isInfGradConfig,
-  isNanGradConfig,
-  log1pGradConfig,
-  logGradConfig,
-  logSoftmaxGradConfig,
-  lrnGradConfig,
-  maxGradConfig,
-  maxGradConfig,
-  maximumGradConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  minGradConfig,
-  minimumGradConfig,
-  modGradConfig,
-  multiplyGradConfig,
-  negateGradConfig,
-  oneHotGradConfig,
-  onesLikeGradConfig,
-  padV2GradConfig,
-  padV2GradConfig,
-  powGradConfig,
-  preluGradConfig,
-  reciprocalGradConfig,
-  relu6GradConfig,
-  reluGradConfig,
-  reshapeGradConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborGradConfig,
-  reverseGradConfig,
-  roundGradConfig,
-  rsqrtGradConfig,
-  selectV2PoolGradConfig,
-  seluGradConfig,
-  sigmoidGradConfig,
-  signGradConfig,
-  sinGradConfig,
-  sinhGradConfig,
-  sliceGradConfig,
-  softmaxGradConfig,
-  softplusGradConfig,
-  spaceToBatchNDGradConfig,
-  spaceToBatchNDGradConfig,
-  splitVGradConfig,
-  splitVGradConfig,
-  sqrtGradConfig,
-  squaredDifferenceGradConfig,
-  squareGradConfig,
-  stepGradConfig,
-  subGradConfig,
-  sumGradConfig,
-  tanGradConfig,
-  tanhGradConfig,
-  tileGradConfig,
-  transposeGradConfig,
-  unpackGradConfig,
-  unsortedSegmentSumGradConfig,
-  zerosLikeGradConfig
-];
-for (const gradientConfig of gradConfigs) {
-  registerGradient(gradientConfig);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/index.js
-const dist_exports = {};
-__export(dist_exports, {
-  Abs: () => Abs,
-  Acos: () => Acos,
-  Acosh: () => Acosh,
-  AdadeltaOptimizer: () => AdadeltaOptimizer,
-  AdagradOptimizer: () => AdagradOptimizer,
-  AdamOptimizer: () => AdamOptimizer,
-  AdamaxOptimizer: () => AdamaxOptimizer,
-  Add: () => Add,
-  AddN: () => AddN,
-  All: () => All,
-  Any: () => Any,
-  ArgMax: () => ArgMax,
-  ArgMin: () => ArgMin,
-  Asin: () => Asin,
-  Asinh: () => Asinh,
-  Atan: () => Atan,
-  Atan2: () => Atan2,
-  Atanh: () => Atanh,
-  AvgPool: () => AvgPool,
-  AvgPool3D: () => AvgPool3D,
-  AvgPool3DBackprop: () => AvgPool3DBackprop,
-  AvgPoolBackprop: () => AvgPoolBackprop,
-  BatchMatMul: () => BatchMatMul,
-  BatchToSpaceND: () => BatchToSpaceND,
-  BroadcastTo: () => BroadcastTo,
-  Cast: () => Cast,
-  Ceil: () => Ceil,
-  ClipByValue: () => ClipByValue,
-  Complex: () => Complex,
-  Concat: () => Concat,
-  Conv2D: () => Conv2D,
-  Conv2DBackpropFilter: () => Conv2DBackpropFilter,
-  Conv2DBackpropInput: () => Conv2DBackpropInput,
-  Conv3D: () => Conv3D,
-  Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
-  Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
-  Cos: () => Cos,
-  Cosh: () => Cosh,
-  CropAndResize: () => CropAndResize,
-  Cumsum: () => Cumsum,
-  DataStorage: () => DataStorage,
-  DepthToSpace: () => DepthToSpace,
-  DepthwiseConv2dNative: () => DepthwiseConv2dNative,
-  DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
-  DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
-  Diag: () => Diag,
-  Dilation2D: () => Dilation2D,
-  Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
-  Dilation2DBackpropInput: () => Dilation2DBackpropInput,
-  Div: () => Div,
-  ENV: () => ENV,
-  Elu: () => Elu,
-  EluGrad: () => EluGrad,
-  Environment: () => Environment,
-  Equal: () => Equal,
-  Erf: () => Erf,
-  Exp: () => Exp,
-  Expm1: () => Expm1,
-  FFT: () => FFT,
-  Fill: () => Fill,
-  FlipLeftRight: () => FlipLeftRight,
-  Floor: () => Floor,
-  FloorDiv: () => FloorDiv,
-  FromPixels: () => FromPixels,
-  FusedBatchNorm: () => FusedBatchNorm,
-  FusedConv2D: () => FusedConv2D,
-  FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
-  GatherNd: () => GatherNd,
-  GatherV2: () => GatherV2,
-  Greater: () => Greater,
-  GreaterEqual: () => GreaterEqual,
-  IFFT: () => IFFT,
-  Identity: () => Identity,
-  Imag: () => Imag,
-  IsFinite: () => IsFinite,
-  IsInf: () => IsInf,
-  IsNan: () => IsNan,
-  KernelBackend: () => KernelBackend,
-  LRN: () => LRN,
-  LRNBackprop: () => LRNBackprop,
-  Less: () => Less,
-  LessEqual: () => LessEqual,
-  LinSpace: () => LinSpace,
-  Log: () => Log,
-  Log1p: () => Log1p,
-  LogSoftmax: () => LogSoftmax,
-  LogicalAnd: () => LogicalAnd,
-  LogicalNot: () => LogicalNot,
-  LogicalOr: () => LogicalOr,
-  Max: () => Max,
-  MaxPool: () => MaxPool,
-  MaxPool3D: () => MaxPool3D,
-  MaxPool3DBackprop: () => MaxPool3DBackprop,
-  MaxPoolBackprop: () => MaxPoolBackprop,
-  MaxPoolWithArgmax: () => MaxPoolWithArgmax,
-  Maximum: () => Maximum,
-  Mean: () => Mean,
-  Min: () => Min,
-  Minimum: () => Minimum,
-  Mod: () => Mod,
-  MomentumOptimizer: () => MomentumOptimizer,
-  Multiply: () => Multiply,
-  Negate: () => Negate,
-  NonMaxSuppressionV3: () => NonMaxSuppressionV3,
-  NonMaxSuppressionV4: () => NonMaxSuppressionV4,
-  NonMaxSuppressionV5: () => NonMaxSuppressionV5,
-  NotEqual: () => NotEqual,
-  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
-  OneHot: () => OneHot,
-  OnesLike: () => OnesLike,
-  Optimizer: () => Optimizer,
-  PadV2: () => PadV2,
-  Pool: () => Pool,
-  Pow: () => Pow,
-  Prelu: () => Prelu,
-  Prod: () => Prod,
-  RMSPropOptimizer: () => RMSPropOptimizer,
-  Range: () => Range,
-  Rank: () => Rank,
-  Real: () => Real,
-  Reciprocal: () => Reciprocal,
-  Reduction: () => Reduction,
-  Relu: () => Relu,
-  Relu6: () => Relu6,
-  Reshape: () => Reshape,
-  ResizeBilinear: () => ResizeBilinear,
-  ResizeBilinearGrad: () => ResizeBilinearGrad,
-  ResizeNearestNeighbor: () => ResizeNearestNeighbor,
-  ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
-  Reverse: () => Reverse,
-  RotateWithOffset: () => RotateWithOffset,
-  Round: () => Round,
-  Rsqrt: () => Rsqrt,
-  SGDOptimizer: () => SGDOptimizer,
-  ScatterNd: () => ScatterNd,
-  SelectV2: () => SelectV2,
-  Selu: () => Selu,
-  Sigmoid: () => Sigmoid,
-  Sign: () => Sign,
-  Sin: () => Sin,
-  Sinh: () => Sinh,
-  Slice: () => Slice,
-  Softmax: () => Softmax,
-  Softplus: () => Softplus,
-  SpaceToBatchND: () => SpaceToBatchND,
-  SparseToDense: () => SparseToDense,
-  SplitV: () => SplitV,
-  Sqrt: () => Sqrt,
-  Square: () => Square,
-  SquaredDifference: () => SquaredDifference,
-  Step: () => Step,
-  StridedSlice: () => StridedSlice,
-  Sub: () => Sub,
-  Sum: () => Sum,
-  Tan: () => Tan,
-  Tanh: () => Tanh,
-  Tensor: () => Tensor,
-  TensorBuffer: () => TensorBuffer,
-  Tile: () => Tile,
-  TopK: () => TopK,
-  Transpose: () => Transpose,
-  Unpack: () => Unpack,
-  UnsortedSegmentSum: () => UnsortedSegmentSum,
-  Variable: () => Variable,
-  ZerosLike: () => ZerosLike,
-  _FusedMatMul: () => _FusedMatMul,
-  abs: () => abs,
-  acos: () => acos,
-  acosh: () => acosh,
-  add: () => add2,
-  addN: () => addN,
-  addStrict: () => addStrict,
-  all: () => all,
-  any: () => any,
-  argMax: () => argMax,
-  argMin: () => argMin,
-  asin: () => asin,
-  asinh: () => asinh,
-  atan: () => atan,
-  atan2: () => atan2,
-  atanh: () => atanh,
-  avgPool: () => avgPool,
-  avgPool3d: () => avgPool3d,
-  backend: () => backend,
-  backend_util: () => backend_util_exports,
-  basicLSTMCell: () => basicLSTMCell,
-  batchNorm: () => batchNorm,
-  batchNorm2d: () => batchNorm2d,
-  batchNorm3d: () => batchNorm3d,
-  batchNorm4d: () => batchNorm4d,
-  batchToSpaceND: () => batchToSpaceND,
-  booleanMaskAsync: () => booleanMaskAsync,
-  broadcastTo: () => broadcastTo,
-  browser: () => browser_exports,
-  buffer: () => buffer,
-  cast: () => cast,
-  ceil: () => ceil,
-  clipByValue: () => clipByValue,
-  clone: () => clone,
-  complex: () => complex,
-  concat: () => concat,
-  concat1d: () => concat1d,
-  concat2d: () => concat2d,
-  concat3d: () => concat3d,
-  concat4d: () => concat4d,
-  conv1d: () => conv1d,
-  conv2d: () => conv2d,
-  conv2dTranspose: () => conv2dTranspose,
-  conv3d: () => conv3d,
-  conv3dTranspose: () => conv3dTranspose,
-  cos: () => cos,
-  cosh: () => cosh,
-  cosineWindow: () => cosineWindow,
-  cumsum: () => cumsum,
-  customGrad: () => customGrad,
-  deprecationWarn: () => deprecationWarn,
-  depthToSpace: () => depthToSpace,
-  depthwiseConv2d: () => depthwiseConv2d,
-  device_util: () => device_util_exports,
-  diag: () => diag,
-  dilation2d: () => dilation2d,
-  disableDeprecationWarnings: () => disableDeprecationWarnings,
-  dispose: () => dispose,
-  disposeVariables: () => disposeVariables,
-  div: () => div,
-  divNoNan: () => divNoNan,
-  divStrict: () => divStrict,
-  dot: () => dot,
-  dropout: () => dropout,
-  elu: () => elu,
-  enableDebugMode: () => enableDebugMode,
-  enableProdMode: () => enableProdMode,
-  enclosingPowerOfTwo: () => enclosingPowerOfTwo,
-  engine: () => engine15,
-  env: () => env,
-  equal: () => equal,
-  equalStrict: () => equalStrict,
-  erf: () => erf,
-  exp: () => exp,
-  expandDims: () => expandDims,
-  expm1: () => expm1,
-  eye: () => eye,
-  fft: () => fft,
-  fill: () => fill,
-  findBackend: () => findBackend,
-  findBackendFactory: () => findBackendFactory,
-  floor: () => floor,
-  floorDiv: () => floorDiv,
-  fused: () => fused_ops_exports,
-  gather: () => gather,
-  gatherND: () => gatherND,
-  gather_util: () => gather_nd_util_exports,
-  getBackend: () => getBackend,
-  getGradient: () => getGradient,
-  getKernel: () => getKernel,
-  getKernelsForBackend: () => getKernelsForBackend,
-  grad: () => grad,
-  grads: () => grads,
-  greater: () => greater,
-  greaterEqual: () => greaterEqual,
-  greaterEqualStrict: () => greaterEqualStrict,
-  greaterStrict: () => greaterStrict,
-  ifft: () => ifft,
-  imag: () => imag,
-  image: () => image,
-  inTopKAsync: () => inTopKAsync,
-  io: () => io_exports,
-  irfft: () => irfft,
-  isFinite: () => isFinite2,
-  isInf: () => isInf,
-  isNaN: () => isNaN2,
-  keep: () => keep,
-  kernel_impls: () => kernel_impls_exports,
-  leakyRelu: () => leakyRelu,
-  less: () => less,
-  lessEqual: () => lessEqual,
-  lessEqualStrict: () => lessEqualStrict,
-  lessStrict: () => lessStrict,
-  linalg: () => linalg,
-  linspace: () => linspace,
-  localResponseNormalization: () => localResponseNormalization,
-  log: () => log,
-  log1p: () => log1p,
-  logSigmoid: () => logSigmoid,
-  logSoftmax: () => logSoftmax,
-  logSumExp: () => logSumExp,
-  logicalAnd: () => logicalAnd,
-  logicalNot: () => logicalNot,
-  logicalOr: () => logicalOr,
-  logicalXor: () => logicalXor,
-  losses: () => losses,
-  matMul: () => matMul,
-  math: () => math_exports,
-  max: () => max,
-  maxPool: () => maxPool,
-  maxPool3d: () => maxPool3d,
-  maxPoolWithArgmax: () => maxPoolWithArgmax,
-  maximum: () => maximum,
-  maximumStrict: () => maximumStrict,
-  mean: () => mean,
-  memory: () => memory,
-  min: () => min,
-  minimum: () => minimum,
-  minimumStrict: () => minimumStrict,
-  mod: () => mod,
-  modStrict: () => modStrict,
-  moments: () => moments,
-  movingAverage: () => movingAverage,
-  mul: () => mul,
-  mulStrict: () => mulStrict,
-  multiRNNCell: () => multiRNNCell,
-  multinomial: () => multinomial,
-  neg: () => neg,
-  nextFrame: () => nextFrame,
-  norm: () => norm,
-  notEqual: () => notEqual,
-  notEqualStrict: () => notEqualStrict,
-  oneHot: () => oneHot,
-  ones: () => ones2,
-  onesLike: () => onesLike,
-  op: () => op,
-  outerProduct: () => outerProduct,
-  pad: () => pad,
-  pad1d: () => pad1d,
-  pad2d: () => pad2d,
-  pad3d: () => pad3d,
-  pad4d: () => pad4d,
-  pool: () => pool,
-  pow: () => pow,
-  powStrict: () => powStrict,
-  prelu: () => prelu,
-  print: () => print,
-  prod: () => prod,
-  profile: () => profile,
-  rand: () => rand,
-  randomGamma: () => randomGamma,
-  randomNormal: () => randomNormal,
-  randomUniform: () => randomUniform,
-  range: () => range,
-  ready: () => ready,
-  real: () => real,
-  reciprocal: () => reciprocal,
-  registerBackend: () => registerBackend,
-  registerGradient: () => registerGradient,
-  registerKernel: () => registerKernel,
-  relu: () => relu,
-  relu6: () => relu6,
-  removeBackend: () => removeBackend,
-  reshape: () => reshape,
-  reverse: () => reverse,
-  reverse1d: () => reverse1d,
-  reverse2d: () => reverse2d,
-  reverse3d: () => reverse3d,
-  reverse4d: () => reverse4d,
-  rfft: () => rfft,
-  round: () => round,
-  rsqrt: () => rsqrt,
-  scalar: () => scalar,
-  scatterND: () => scatterND,
-  scatter_util: () => scatter_nd_util_exports,
-  selu: () => selu,
-  separableConv2d: () => separableConv2d,
-  serialization: () => serialization_exports,
-  setBackend: () => setBackend,
-  setPlatform: () => setPlatform,
-  setdiff1dAsync: () => setdiff1dAsync,
-  sigmoid: () => sigmoid,
-  sign: () => sign,
-  signal: () => signal,
-  sin: () => sin,
-  sinh: () => sinh,
-  slice: () => slice,
-  slice1d: () => slice1d,
-  slice2d: () => slice2d,
-  slice3d: () => slice3d,
-  slice4d: () => slice4d,
-  slice_util: () => slice_util_exports,
-  softmax: () => softmax,
-  softplus: () => softplus,
-  spaceToBatchND: () => spaceToBatchND,
-  sparseToDense: () => sparseToDense,
-  spectral: () => spectral,
-  split: () => split,
-  sqrt: () => sqrt,
-  square: () => square,
-  squaredDifference: () => squaredDifference,
-  squaredDifferenceStrict: () => squaredDifferenceStrict,
-  squeeze: () => squeeze,
-  stack: () => stack,
-  step: () => step,
-  stridedSlice: () => stridedSlice,
-  sub: () => sub,
-  subStrict: () => subStrict,
-  sum: () => sum2,
-  sumOutType: () => sumOutType,
-  tan: () => tan,
-  tanh: () => tanh2,
-  tensor: () => tensor4,
-  tensor1d: () => tensor1d,
-  tensor2d: () => tensor2d,
-  tensor3d: () => tensor3d,
-  tensor4d: () => tensor4d,
-  tensor5d: () => tensor5d,
-  tensor6d: () => tensor6d,
-  tensor_util: () => tensor_util_exports,
-  test_util: () => test_util_exports,
-  tidy: () => tidy,
-  tile: () => tile,
-  time: () => time,
-  topk: () => topk,
-  train: () => train,
-  transpose: () => transpose,
-  truncatedNormal: () => truncatedNormal,
-  unregisterGradient: () => unregisterGradient,
-  unregisterKernel: () => unregisterKernel,
-  unsortedSegmentSum: () => unsortedSegmentSum,
-  unstack: () => unstack,
-  upcastType: () => upcastType,
-  util: () => util_exports,
-  valueAndGrad: () => valueAndGrad,
-  valueAndGrads: () => valueAndGrads,
-  variable: () => variable,
-  variableGrads: () => variableGrads,
-  version_core: () => version,
-  where: () => where,
-  whereAsync: () => whereAsync,
-  zeros: () => zeros,
-  zerosLike: () => zerosLike
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
 // build/src/draw/drawContour.js
 function drawContour(ctx, points, isClosed = false) {
   ctx.beginPath();
@@ -19987,23 +95,26 @@ __export(utils_exports, {
   isTensor4D: () => isTensor4D,
   isValidNumber: () => isValidNumber,
   isValidProbablitiy: () => isValidProbablitiy,
-  range: () => range4,
-  round: () => round3
+  range: () => range,
+  round: () => round
 });
-function isTensor(tensor17, dim) {
-  return tensor17 instanceof Tensor && tensor17.shape.length === dim;
+import {
+  Tensor
+} from "@tensorflow/tfjs";
+function isTensor(tensor2, dim) {
+  return tensor2 instanceof Tensor && tensor2.shape.length === dim;
 }
-function isTensor1D(tensor17) {
-  return isTensor(tensor17, 1);
+function isTensor1D(tensor2) {
+  return isTensor(tensor2, 1);
 }
-function isTensor2D(tensor17) {
-  return isTensor(tensor17, 2);
+function isTensor2D(tensor2) {
+  return isTensor(tensor2, 2);
 }
-function isTensor3D(tensor17) {
-  return isTensor(tensor17, 3);
+function isTensor3D(tensor2) {
+  return isTensor(tensor2, 3);
 }
-function isTensor4D(tensor17) {
-  return isTensor(tensor17, 4);
+function isTensor4D(tensor2) {
+  return isTensor(tensor2, 4);
 }
 function isFloat(num) {
   return num % 1 !== 0;
@@ -20011,7 +122,7 @@ function isFloat(num) {
 function isEven(num) {
   return num % 2 === 0;
 }
-function round3(num, prec = 2) {
+function round(num, prec = 2) {
   const f = Math.pow(10, prec);
   return Math.floor(num * f) / f;
 }
@@ -20023,10 +134,10 @@ function computeReshapedDimensions({width, height}, inputSize) {
   return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
 }
 function getCenterPoint(pts) {
-  return pts.reduce((sum26, pt) => sum26.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
+  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
 }
-function range4(num, start, step7) {
-  return Array(num).fill(0).map((_, i) => start + i * step7);
+function range(num, start, step) {
+  return Array(num).fill(0).map((_, i) => start + i * step);
 }
 function isValidNumber(num) {
   return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
@@ -20299,15 +410,15 @@ function iou(box1, box2, isIOU = true) {
 function minBbox(pts) {
   const xs = pts.map((pt) => pt.x);
   const ys = pts.map((pt) => pt.y);
-  const minX = xs.reduce((min4, x) => x < min4 ? x : min4, Infinity);
-  const minY = ys.reduce((min4, y) => y < min4 ? y : min4, Infinity);
-  const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0);
-  const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0);
+  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity);
+  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity);
+  const maxX = xs.reduce((max, x) => max < x ? x : max, 0);
+  const maxY = ys.reduce((max, y) => max < y ? y : max, 0);
   return new BoundingBox(minX, minY, maxX, maxY);
 }
 
 // build/src/ops/nonMaxSuppression.js
-function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
+function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) {
   let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex);
   const pick = [];
   while (indicesSortedByScore.length > 0) {
@@ -20327,6 +438,12 @@ function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
 }
 
 // build/src/ops/normalize.js
+import {
+  concat,
+  fill,
+  sub,
+  tidy
+} from "@tensorflow/tfjs";
 function normalize(x, meanRgb) {
   return tidy(() => {
     const [r, g, b] = meanRgb;
@@ -20339,8 +456,14 @@ function normalize(x, meanRgb) {
 }
 
 // build/src/ops/padToSquare.js
+import {
+  cast,
+  concat as concat2,
+  fill as fill2,
+  tidy as tidy2
+} from "@tensorflow/tfjs";
 function padToSquare(imgTensor, isCenterImage = false) {
-  return tidy(() => {
+  return tidy2(() => {
     const [height, width] = imgTensor.shape.slice(1);
     if (height === width) {
       return imgTensor;
@@ -20351,7 +474,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
     const createPaddingTensor = (paddingAmount2) => {
       const paddingTensorShape = imgTensor.shape.slice();
       paddingTensorShape[paddingAxis] = paddingAmount2;
-      return fill(paddingTensorShape, 0);
+      return fill2(paddingTensorShape, 0);
     };
     const paddingTensorAppend = createPaddingTensor(paddingAmount);
     const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -20361,7 +484,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
       imgTensor,
       paddingTensorAppend
     ].filter((t) => !!t).map((t) => cast(t, "float32"));
-    return concat(tensorsToStack, paddingAxis);
+    return concat2(tensorsToStack, paddingAxis);
   });
 }
 
@@ -20378,7 +501,7 @@ function shuffleArray(inputArray) {
 }
 
 // build/src/ops/index.js
-function sigmoid6(x) {
+function sigmoid(x) {
   return 1 / (1 + Math.exp(-x));
 }
 function inverseSigmoid(x) {
@@ -20515,7 +638,7 @@ class FaceMatch {
     return this._distance;
   }
   toString(withDistance = true) {
-    return `${this.label}${withDistance ? ` (${round3(this.distance)})` : ""}`;
+    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
   }
 }
 
@@ -20602,7 +725,7 @@ function extendWithFaceDetection(sourceObj, detection) {
 
 // build/src/env/createBrowserEnv.js
 function createBrowserEnv() {
-  const fetch3 = window["fetch"] || function() {
+  const fetch = window["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for browser environment");
   };
   const readFile = function() {
@@ -20616,7 +739,7 @@ function createBrowserEnv() {
     Video: HTMLVideoElement,
     createCanvasElement: () => document.createElement("canvas"),
     createImageElement: () => document.createElement("img"),
-    fetch: fetch3,
+    fetch,
     readFile
   };
 }
@@ -20633,8 +756,8 @@ function createFileSystem(fs) {
   }
   const readFile = fs ? function(filePath) {
     return new Promise((res, rej) => {
-      fs.readFile(filePath, function(err, buffer11) {
-        return err ? rej(err) : res(buffer11);
+      fs.readFile(filePath, function(err, buffer) {
+        return err ? rej(err) : res(buffer);
       });
     });
   } : function() {
@@ -20661,11 +784,11 @@ function createNodejsEnv() {
     }
     throw new Error("createImageElement - missing Image implementation for nodejs environment");
   };
-  const fetch3 = global["fetch"] || function() {
+  const fetch = global["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for nodejs environment");
   };
   const fileSystem = createFileSystem();
-  return __assign({
+  return {
     Canvas: Canvas || class {
     },
     CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
@@ -20678,12 +801,13 @@ function createNodejsEnv() {
     },
     createCanvasElement,
     createImageElement,
-    fetch: fetch3
-  }, fileSystem);
+    fetch,
+    ...fileSystem
+  };
 }
 
 // build/src/env/isBrowser.js
-function isBrowser2() {
+function isBrowser() {
   return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
 }
 
@@ -20691,42 +815,42 @@ function isBrowser2() {
 
 // build/src/env/index.js
 const isNodejs = __toModule(require_isNodejs());
-let environment11;
+let environment;
 function getEnv() {
-  if (!environment11) {
+  if (!environment) {
     throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
   }
-  return environment11;
+  return environment;
 }
-function setEnv(env17) {
-  environment11 = env17;
+function setEnv(env16) {
+  environment = env16;
 }
 function initialize() {
-  if (isBrowser2()) {
+  if (isBrowser()) {
     return setEnv(createBrowserEnv());
   }
   if (isNodejs.isNodejs()) {
     return setEnv(createNodejsEnv());
   }
 }
-function monkeyPatch(env17) {
-  if (!environment11) {
+function monkeyPatch(env16) {
+  if (!environment) {
     initialize();
   }
-  if (!environment11) {
+  if (!environment) {
     throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
   }
-  const {Canvas = environment11.Canvas, Image = environment11.Image} = env17;
-  environment11.Canvas = Canvas;
-  environment11.Image = Image;
-  environment11.createCanvasElement = env17.createCanvasElement || (() => new Canvas());
-  environment11.createImageElement = env17.createImageElement || (() => new Image());
-  environment11.ImageData = env17.ImageData || environment11.ImageData;
-  environment11.Video = env17.Video || environment11.Video;
-  environment11.fetch = env17.fetch || environment11.fetch;
-  environment11.readFile = env17.readFile || environment11.readFile;
+  const {Canvas = environment.Canvas, Image = environment.Image} = env16;
+  environment.Canvas = Canvas;
+  environment.Image = Image;
+  environment.createCanvasElement = env16.createCanvasElement || (() => new Canvas());
+  environment.createImageElement = env16.createImageElement || (() => new Image());
+  environment.ImageData = env16.ImageData || environment.ImageData;
+  environment.Video = env16.Video || environment.Video;
+  environment.fetch = env16.fetch || environment.fetch;
+  environment.readFile = env16.readFile || environment.readFile;
 }
-const env2 = {
+const env = {
   getEnv,
   setEnv,
   initialize,
@@ -20734,14 +858,14 @@ const env2 = {
   createFileSystem,
   createNodejsEnv,
   monkeyPatch,
-  isBrowser: isBrowser2,
+  isBrowser,
   isNodejs: isNodejs.isNodejs
 };
 initialize();
 
 // build/src/dom/resolveInput.js
 function resolveInput(arg) {
-  if (!env2.isNodejs() && typeof arg === "string") {
+  if (!env.isNodejs() && typeof arg === "string") {
     return document.getElementById(arg);
   }
   return arg;
@@ -20749,7 +873,7 @@ function resolveInput(arg) {
 
 // build/src/dom/getContext2dOrThrow.js
 function getContext2dOrThrow(canvasArg) {
-  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env2.getEnv();
+  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
   if (canvasArg instanceof CanvasRenderingContext2D2) {
     return canvasArg;
   }
@@ -20871,21 +995,21 @@ function drawDetections(canvasArg, detections) {
   detectionsArray.forEach((det) => {
     const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0;
     const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det);
-    const label = score ? `${round3(score)}` : void 0;
+    const label = score ? `${round(score)}` : void 0;
     new DrawBox(box, {label}).draw(canvasArg);
   });
 }
 
 // build/src/dom/isMediaLoaded.js
 function isMediaLoaded(media) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
 }
 
 // build/src/dom/awaitMediaLoaded.js
 function awaitMediaLoaded(media) {
   return new Promise((resolve, reject) => {
-    if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media)) {
+    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
       return resolve(null);
     }
     function onLoad(e) {
@@ -20918,7 +1042,7 @@ function bufferToImage(buf) {
       if (typeof reader.result !== "string") {
         return reject("bufferToImage - expected reader.result to be a string, in onload");
       }
-      const img = env2.getEnv().createImageElement();
+      const img = env.getEnv().createImageElement();
       img.onload = () => resolve(img);
       img.onerror = reject;
       img.src = reader.result;
@@ -20930,7 +1054,7 @@ function bufferToImage(buf) {
 
 // build/src/dom/getMediaDimensions.js
 function getMediaDimensions(input) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   if (input instanceof Image) {
     return new Dimensions(input.naturalWidth, input.naturalHeight);
   }
@@ -20942,14 +1066,14 @@ function getMediaDimensions(input) {
 
 // build/src/dom/createCanvas.js
 function createCanvas({width, height}) {
-  const {createCanvasElement} = env2.getEnv();
+  const {createCanvasElement} = env.getEnv();
   const canvas = createCanvasElement();
   canvas.width = width;
   canvas.height = height;
   return canvas;
 }
 function createCanvasFromMedia(media, dims) {
-  const {ImageData: ImageData2} = env2.getEnv();
+  const {ImageData: ImageData2} = env.getEnv();
   if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
     throw new Error("createCanvasFromMedia - media has not finished loading yet");
   }
@@ -20964,24 +1088,28 @@ function createCanvasFromMedia(media, dims) {
 }
 
 // build/src/dom/imageTensorToCanvas.js
+import {
+  browser,
+  tidy as tidy3
+} from "@tensorflow/tfjs";
 async function imageTensorToCanvas(imgTensor, canvas) {
-  const targetCanvas = canvas || env2.getEnv().createCanvasElement();
+  const targetCanvas = canvas || env.getEnv().createCanvasElement();
   const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-  const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-  await browser_exports.toPixels(imgTensor3D, targetCanvas);
+  const imgTensor3D = tidy3(() => imgTensor.as3D(height, width, numChannels).toInt());
+  await browser.toPixels(imgTensor3D, targetCanvas);
   imgTensor3D.dispose();
   return targetCanvas;
 }
 
 // build/src/dom/isMediaElement.js
 function isMediaElement(input) {
-  const {Image, Canvas, Video} = env2.getEnv();
+  const {Image, Canvas, Video} = env.getEnv();
   return input instanceof Image || input instanceof Canvas || input instanceof Video;
 }
 
 // build/src/dom/imageToSquare.js
 function imageToSquare(input, inputSize, centerImage = false) {
-  const {Image, Canvas} = env2.getEnv();
+  const {Image, Canvas} = env.getEnv();
   if (!(input instanceof Image || input instanceof Canvas)) {
     throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
   }
@@ -20999,6 +1127,14 @@ function imageToSquare(input, inputSize, centerImage = false) {
 }
 
 // build/src/dom/NetInput.js
+import {
+  Tensor as Tensor2,
+  browser as browser2,
+  cast as cast2,
+  image,
+  stack,
+  tidy as tidy4
+} from "@tensorflow/tfjs";
 class NetInput {
   constructor(inputs, treatAsBatchInput = false) {
     this._imageTensors = [];
@@ -21025,7 +1161,7 @@ class NetInput {
         this._inputDimensions[idx] = input.shape.slice(1);
         return;
       }
-      const canvas = input instanceof env2.getEnv().Canvas ? input : createCanvasFromMedia(input);
+      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
       this._canvases[idx] = canvas;
       this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
     });
@@ -21049,7 +1185,7 @@ class NetInput {
     return this._inputSize;
   }
   get reshapedInputDimensions() {
-    return range4(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
+    return range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
   }
   getInput(batchIdx) {
     return this.canvases[batchIdx] || this.imageTensors[batchIdx];
@@ -21073,10 +1209,10 @@ class NetInput {
   }
   toBatchTensor(inputSize, isCenterInputs = true) {
     this._inputSize = inputSize;
-    return tidy(() => {
-      const inputTensors = range4(this.batchSize, 0, 1).map((batchIdx) => {
+    return tidy4(() => {
+      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
         const input = this.getInput(batchIdx);
-        if (input instanceof Tensor) {
+        if (input instanceof Tensor2) {
           let imgTensor = isTensor4D(input) ? input : input.expandDims();
           imgTensor = padToSquare(imgTensor, isCenterInputs);
           if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
@@ -21084,12 +1220,12 @@ class NetInput {
           }
           return imgTensor.as3D(inputSize, inputSize, 3);
         }
-        if (input instanceof env2.getEnv().Canvas) {
-          return browser_exports.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+        if (input instanceof env.getEnv().Canvas) {
+          return browser2.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
         }
         throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
       });
-      const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
+      const batchTensor = stack(inputTensors.map((t) => cast2(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
       return batchTensor;
     });
   }
@@ -21126,7 +1262,7 @@ async function toNetInput(inputs) {
 
 // build/src/dom/extractFaces.js
 async function extractFaces(input, detections) {
-  const {Canvas} = env2.getEnv();
+  const {Canvas} = env.getEnv();
   let canvas = input;
   if (!(input instanceof Canvas)) {
     const netInput = await toNetInput(input);
@@ -21146,6 +1282,10 @@ async function extractFaces(input, detections) {
 }
 
 // build/src/dom/extractFaceTensors.js
+import {
+  slice3d,
+  tidy as tidy5
+} from "@tensorflow/tfjs";
 async function extractFaceTensors(imageTensor, detections) {
   if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
     throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");
@@ -21153,7 +1293,7 @@ async function extractFaceTensors(imageTensor, detections) {
   if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
     throw new Error("extractFaceTensors - batchSize > 1 not supported");
   }
-  return tidy(() => {
+  return tidy5(() => {
     const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);
     const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight));
     const faceTensors = boxes.map(({x, y, width, height}) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
@@ -21163,8 +1303,8 @@ async function extractFaceTensors(imageTensor, detections) {
 
 // build/src/dom/fetchOrThrow.js
 async function fetchOrThrow(url, init) {
-  const fetch3 = env2.getEnv().fetch;
-  const res = await fetch3(url, init);
+  const fetch = env.getEnv().fetch;
+  const res = await fetch(url, init);
   if (!(res.status < 400)) {
     throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
   }
@@ -21219,10 +1359,13 @@ function getModelUris(uri, defaultModelName) {
 }
 
 // build/src/dom/loadWeightMap.js
+import {
+  io
+} from "@tensorflow/tfjs";
 async function loadWeightMap(uri, defaultModelName) {
   const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
   let manifest = await fetchJson(manifestUri);
-  return io_exports.loadWeights(manifest, modelBaseUri);
+  return io.loadWeights(manifest, modelBaseUri);
 }
 
 // build/src/dom/matchDimensions.js
@@ -21238,6 +1381,12 @@ function matchDimensions(input, reference, useMediaDimensions = false) {
 // build/src/dom/index.js
 
 // build/src/NeuralNetwork.js
+import {
+  Tensor as Tensor3,
+  Variable,
+  io as io2,
+  tensor
+} from "@tensorflow/tfjs";
 class NeuralNetwork {
   constructor(_name) {
     this._name = _name;
@@ -21257,10 +1406,10 @@ class NeuralNetwork {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     return obj[objProp];
   }
-  reassignParamFromPath(paramPath, tensor17) {
+  reassignParamFromPath(paramPath, tensor2) {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     obj[objProp].dispose();
-    obj[objProp] = tensor17;
+    obj[objProp] = tensor2;
   }
   getParamList() {
     return this._paramMappings.map(({paramPath}) => ({
@@ -21275,15 +1424,15 @@ class NeuralNetwork {
     return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
   }
   variable() {
-    this.getFrozenParams().forEach(({path, tensor: tensor17}) => {
-      this.reassignParamFromPath(path, tensor17.variable());
+    this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
+      this.reassignParamFromPath(path, tensor2.variable());
     });
   }
   freeze() {
-    this.getTrainableParams().forEach(({path, tensor: variable3}) => {
-      const tensor17 = tensor4(variable3.dataSync());
-      variable3.dispose();
-      this.reassignParamFromPath(path, tensor17);
+    this.getTrainableParams().forEach(({path, tensor: variable}) => {
+      const tensor2 = tensor(variable.dataSync());
+      variable.dispose();
+      this.reassignParamFromPath(path, tensor2);
     });
   }
   dispose(throwOnRedispose = true) {
@@ -21296,7 +1445,7 @@ class NeuralNetwork {
     this._params = void 0;
   }
   serializeParams() {
-    return new Float32Array(this.getParamList().map(({tensor: tensor17}) => Array.from(tensor17.dataSync())).reduce((flat, arr) => flat.concat(arr)));
+    return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
   }
   async load(weightsOrUrl) {
     if (weightsOrUrl instanceof Float32Array) {
@@ -21316,12 +1465,12 @@ class NeuralNetwork {
     if (filePath && typeof filePath !== "string") {
       throw new Error(`${this._name}.loadFromDisk - expected model file path`);
     }
-    const {readFile} = env2.getEnv();
+    const {readFile} = env.getEnv();
     const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName());
     const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((filePath2) => readFile(filePath2).then((buf) => buf.buffer)));
-    const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk);
+    const loadWeights = io2.weightsLoaderFactory(fetchWeightsFromDisk);
     const manifest = JSON.parse((await readFile(manifestUri)).toString());
-    const weightMap = await loadWeights2(manifest, modelBaseUri);
+    const weightMap = await loadWeights(manifest, modelBaseUri);
     this.loadFromWeightMap(weightMap);
   }
   loadFromWeightMap(weightMap) {
@@ -21345,7 +1494,7 @@ class NeuralNetwork {
       return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
     }, {nextObj: this.params});
     const {obj, objProp} = result;
-    if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+    if (!obj || !objProp || !(obj[objProp] instanceof Tensor3)) {
       throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
     }
     return {obj, objProp};
@@ -21353,17 +1502,28 @@ class NeuralNetwork {
 }
 
 // build/src/common/depthwiseSeparableConv.js
+import {
+  add,
+  separableConv2d,
+  tidy as tidy6
+} from "@tensorflow/tfjs";
 function depthwiseSeparableConv(x, params, stride) {
-  return tidy(() => {
+  return tidy6(() => {
     let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
-    out = add2(out, params.bias);
+    out = add(out, params.bias);
     return out;
   });
 }
 
 // build/src/faceFeatureExtractor/denseBlock.js
+import {
+  add as add2,
+  conv2d,
+  relu,
+  tidy as tidy7
+} from "@tensorflow/tfjs";
 function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
-  return tidy(() => {
+  return tidy7(() => {
     const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
     const in3 = relu(add2(out1, out2));
@@ -21372,7 +1532,7 @@ function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
   });
 }
 function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-  return tidy(() => {
+  return tidy7(() => {
     const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
     const in3 = relu(add2(out1, out2));
@@ -21384,10 +1544,16 @@ function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = tr
 }
 
 // build/src/common/convLayer.js
+import {
+  add as add3,
+  conv2d as conv2d2,
+  relu as relu2,
+  tidy as tidy8
+} from "@tensorflow/tfjs";
 function convLayer(x, params, padding = "same", withRelu = false) {
-  return tidy(() => {
-    const out = add2(conv2d(x, params.filters, [1, 1], padding), params.bias);
-    return withRelu ? relu(out) : out;
+  return tidy8(() => {
+    const out = add3(conv2d2(x, params.filters, [1, 1], padding), params.bias);
+    return withRelu ? relu2(out) : out;
   });
 }
 
@@ -21401,6 +1567,10 @@ function disposeUnusedWeightTensors(weightMap, paramMappings) {
 }
 
 // build/src/common/extractConvParamsFactory.js
+import {
+  tensor1d,
+  tensor4d
+} from "@tensorflow/tfjs";
 function extractConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
     const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
@@ -21411,10 +1581,14 @@ function extractConvParamsFactory(extractWeights, paramMappings) {
 }
 
 // build/src/common/extractFCParamsFactory.js
+import {
+  tensor1d as tensor1d2,
+  tensor2d
+} from "@tensorflow/tfjs";
 function extractFCParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
     const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tensor1d(extractWeights(channelsOut));
+    const fc_bias = tensor1d2(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
     return {
       weights: fc_weights,
@@ -21433,11 +1607,15 @@ class SeparableConvParams {
 }
 
 // build/src/common/extractSeparableConvParamsFactory.js
+import {
+  tensor1d as tensor1d3,
+  tensor4d as tensor4d2
+} from "@tensorflow/tfjs";
 function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const depthwise_filter = tensor4d2(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+    const pointwise_filter = tensor4d2(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+    const bias = tensor1d3(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
     return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
   };
@@ -21454,12 +1632,12 @@ function loadSeparableConvParamsFactory(extractWeightEntry) {
 // build/src/common/extractWeightEntryFactory.js
 function extractWeightEntryFactory(weightMap, paramMappings) {
   return function(originalPath, paramRank, mappedPath) {
-    const tensor17 = weightMap[originalPath];
-    if (!isTensor(tensor17, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor17}`);
+    const tensor2 = weightMap[originalPath];
+    if (!isTensor(tensor2, paramRank)) {
+      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
     }
     paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor17;
+    return tensor2;
   };
 }
 
@@ -21569,6 +1747,11 @@ function extractParamsFromWeigthMap(weightMap) {
 }
 
 // build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+import {
+  avgPool,
+  scalar,
+  tidy as tidy9
+} from "@tensorflow/tfjs";
 class FaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("FaceFeatureExtractor");
@@ -21578,7 +1761,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tidy9(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
@@ -21605,8 +1788,13 @@ class FaceFeatureExtractor extends NeuralNetwork {
 }
 
 // build/src/common/fullyConnectedLayer.js
+import {
+  add as add4,
+  matMul,
+  tidy as tidy10
+} from "@tensorflow/tfjs";
 function fullyConnectedLayer(x, params) {
-  return tidy(() => add2(matMul(x, params.weights), params.bias));
+  return tidy10(() => add4(matMul(x, params.weights), params.bias));
 }
 
 // build/src/faceProcessor/extractParams.js
@@ -21652,6 +1840,9 @@ function seperateWeightMaps(weightMap) {
 }
 
 // build/src/faceProcessor/FaceProcessor.js
+import {
+  tidy as tidy11
+} from "@tensorflow/tfjs";
 class FaceProcessor extends NeuralNetwork {
   constructor(_name, faceFeatureExtractor) {
     super(_name);
@@ -21665,7 +1856,7 @@ class FaceProcessor extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tidy11(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
       return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
     });
@@ -21715,12 +1906,17 @@ class FaceExpressions {
 }
 
 // build/src/faceExpressionNet/FaceExpressionNet.js
+import {
+  softmax,
+  tidy as tidy12,
+  unstack
+} from "@tensorflow/tfjs";
 class FaceExpressionNet extends FaceProcessor {
   constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
     super("FaceExpressionNet", faceFeatureExtractor);
   }
   forwardInput(input) {
-    return tidy(() => softmax(this.runNet(input)));
+    return tidy12(() => softmax(this.runNet(input)));
   }
   async forward(input) {
     return this.forwardInput(await toNetInput(input));
@@ -21770,7 +1966,7 @@ function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, te
     const sorted = expr.asSortedArray();
     const resultsToDisplay = sorted.filter((expr2) => expr2.probability > minConfidence);
     const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0);
-    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round3(expr2.probability)})`), anchor);
+    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round(expr2.probability)})`), anchor);
     drawTextField.draw(canvasArg);
   });
 }
@@ -21899,7 +2095,7 @@ function extractParams5(weights, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
@@ -21953,7 +2149,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
@@ -21967,22 +2163,30 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
 }
 
 // build/src/xception/TinyXception.js
+import {
+  add as add5,
+  conv2d as conv2d3,
+  maxPool,
+  relu as relu3,
+  scalar as scalar2,
+  tidy as tidy13
+} from "@tensorflow/tfjs";
 function conv(x, params, stride) {
-  return add2(conv2d(x, params.filters, stride, "same"), params.bias);
+  return add5(conv2d3(x, params.filters, stride, "same"), params.bias);
 }
 function reductionBlock(x, params, isActivateInput = true) {
-  let out = isActivateInput ? relu(x) : x;
+  let out = isActivateInput ? relu3(x) : x;
   out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv1, [1, 1]);
   out = maxPool(out, [3, 3], [2, 2], "same");
-  out = add2(out, conv(x, params.expansion_conv, [2, 2]));
+  out = add5(out, conv(x, params.expansion_conv, [2, 2]));
   return out;
 }
 function mainBlock(x, params) {
-  let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]);
-  out = add2(out, x);
+  let out = depthwiseSeparableConv(relu3(x), params.separable_conv0, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv1, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv2, [1, 1]);
+  out = add5(out, x);
   return out;
 }
 class TinyXception extends NeuralNetwork {
@@ -21995,18 +2199,18 @@ class TinyXception extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyXception - load model before inference");
     }
-    return tidy(() => {
+    return tidy13(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
-      let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar2(256));
+      let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2]));
       out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
       out = reductionBlock(out, params.entry_flow.reduction_block_1);
-      range4(this._numMainBlocks, 0, 1).forEach((idx) => {
+      range(this._numMainBlocks, 0, 1).forEach((idx) => {
         out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
       });
       out = reductionBlock(out, params.exit_flow.reduction_block);
-      out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
+      out = relu3(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
       return out;
     });
   }
@@ -22067,6 +2271,12 @@ var Gender;
 })(Gender || (Gender = {}));
 
 // build/src/ageGenderNet/AgeGenderNet.js
+import {
+  avgPool as avgPool2,
+  softmax as softmax2,
+  tidy as tidy14,
+  unstack as unstack2
+} from "@tensorflow/tfjs";
 class AgeGenderNet extends NeuralNetwork {
   constructor(faceFeatureExtractor = new TinyXception(2)) {
     super("AgeGenderNet");
@@ -22080,18 +2290,18 @@ class AgeGenderNet extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tidy14(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
+      const pooled = avgPool2(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
       const age = fullyConnectedLayer(pooled, params.fc.age).as1D();
       const gender = fullyConnectedLayer(pooled, params.fc.gender);
       return {age, gender};
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tidy14(() => {
       const {age, gender} = this.runNet(input);
-      return {age, gender: softmax(gender)};
+      return {age, gender: softmax2(gender)};
     });
   }
   async forward(input) {
@@ -22100,8 +2310,8 @@ class AgeGenderNet extends NeuralNetwork {
   async predictAgeAndGender(input) {
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput);
-    const ages = unstack(out.age);
-    const genders = unstack(out.gender);
+    const ages = unstack2(out.age);
+    const genders = unstack2(out.gender);
     const ageAndGenderTensors = ages.map((ageTensor, i) => ({
       ageTensor,
       genderTensor: genders[i]
@@ -22152,6 +2362,12 @@ class AgeGenderNet extends NeuralNetwork {
 // build/src/ageGenderNet/index.js
 
 // build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+import {
+  fill as fill3,
+  stack as stack2,
+  tidy as tidy15,
+  unstack as unstack3
+} from "@tensorflow/tfjs";
 class FaceLandmark68NetBase extends FaceProcessor {
   postProcess(output, inputSize, originalDimensions) {
     const inputDimensions = originalDimensions.map(({width, height}) => {
@@ -22162,10 +2378,10 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
     });
     const batchSize = inputDimensions.length;
-    return tidy(() => {
-      const createInterleavedTensor = (fillX, fillY) => stack([
-        fill([68], fillX),
-        fill([68], fillY)
+    return tidy15(() => {
+      const createInterleavedTensor = (fillX, fillY) => stack2([
+        fill3([68], fillX),
+        fill3([68], fillY)
       ], 1).as2D(1, 136).as1D();
       const getPadding = (batchIdx, cond) => {
         const {width, height} = inputDimensions[batchIdx];
@@ -22173,12 +2389,12 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
       const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
       const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      const landmarkTensors = output.mul(fill([batchSize, 136], inputSize)).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+      const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize)).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
       return landmarkTensors;
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tidy15(() => {
       const out = this.runNet(input);
       return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
     });
@@ -22188,7 +2404,7 @@ class FaceLandmark68NetBase extends FaceProcessor {
   }
   async detectLandmarks(input) {
     const netInput = await toNetInput(input);
-    const landmarkTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const landmarkTensors = tidy15(() => unstack3(this.forwardInput(netInput)));
     const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => {
       const landmarksArray = Array.from(await landmarkTensor.data());
       const xCoords = landmarksArray.filter((_, i) => isEven(i));
@@ -22250,6 +2466,11 @@ function extractParamsTiny(weights) {
 }
 
 // build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+import {
+  avgPool as avgPool3,
+  scalar as scalar3,
+  tidy as tidy16
+} from "@tensorflow/tfjs";
 class TinyFaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("TinyFaceFeatureExtractor");
@@ -22259,14 +2480,14 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyFaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tidy16(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar3(255));
       let out = denseBlock3(normalized, params.dense0, true);
       out = denseBlock3(out, params.dense1);
       out = denseBlock3(out, params.dense2);
-      out = avgPool(out, [14, 14], [2, 2], "valid");
+      out = avgPool3(out, [14, 14], [2, 2], "valid");
       return out;
     });
   }
@@ -22302,17 +2523,26 @@ class FaceLandmarkNet extends FaceLandmark68Net {
 }
 
 // build/src/faceRecognitionNet/scaleLayer.js
+import {
+  add as add6,
+  mul
+} from "@tensorflow/tfjs";
 function scale(x, params) {
-  return add2(mul(x, params.weights), params.biases);
+  return add6(mul(x, params.weights), params.biases);
 }
 
 // build/src/faceRecognitionNet/convLayer.js
+import {
+  add as add7,
+  conv2d as conv2d4,
+  relu as relu4
+} from "@tensorflow/tfjs";
 function convLayer2(x, params, strides, withRelu, padding = "same") {
   const {filters, bias} = params.conv;
-  let out = conv2d(x, filters, strides, padding);
-  out = add2(out, bias);
+  let out = conv2d4(x, filters, strides, padding);
+  out = add7(out, bias);
   out = scale(out, params.scale);
-  return withRelu ? relu(out) : out;
+  return withRelu ? relu4(out) : out;
 }
 function conv2(x, params) {
   return convLayer2(x, params, [1, 1], true);
@@ -22325,6 +2555,13 @@ function convDown(x, params) {
 }
 
 // build/src/faceRecognitionNet/extractParams.js
+import {
+  tensor1d as tensor1d4,
+  tensor2d as tensor2d2,
+  tensor4d as tensor4d3,
+  tidy as tidy17,
+  transpose
+} from "@tensorflow/tfjs";
 function extractorsFactory5(extractWeights, paramMappings) {
   function extractFilterValues(numFilterValues, numFilters, filterSize) {
     const weights = extractWeights(numFilterValues);
@@ -22332,17 +2569,17 @@ function extractorsFactory5(extractWeights, paramMappings) {
     if (isFloat(depth)) {
       throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
     }
-    return tidy(() => transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
+    return tidy17(() => transpose(tensor4d3(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
   }
   function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
     const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-    const bias = tensor1d(extractWeights(numFilters));
+    const bias = tensor1d4(extractWeights(numFilters));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
     return {filters, bias};
   }
   function extractScaleLayerParams(numWeights, mappedPrefix) {
-    const weights = tensor1d(extractWeights(numWeights));
-    const biases = tensor1d(extractWeights(numWeights));
+    const weights = tensor1d4(extractWeights(numWeights));
+    const biases = tensor1d4(extractWeights(numWeights));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
     return {
       weights,
@@ -22383,7 +2620,7 @@ function extractParams9(weights) {
   const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1");
   const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2");
   const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out");
-  const fc = tidy(() => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
+  const fc = tidy17(() => transpose(tensor2d2(extractWeights(256 * 128), [128, 256]), [1, 0]));
   paramMappings.push({paramPath: `fc`});
   if (getRemainingWeights().length !== 0) {
     throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
@@ -22480,37 +2717,52 @@ function extractParamsFromWeigthMap9(weightMap) {
 }
 
 // build/src/faceRecognitionNet/residualLayer.js
+import {
+  add as add8,
+  avgPool as avgPool4,
+  concat as concat3,
+  relu as relu5,
+  zeros
+} from "@tensorflow/tfjs";
 function residual(x, params) {
   let out = conv2(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  out = add2(out, x);
-  out = relu(out);
+  out = add8(out, x);
+  out = relu5(out);
   return out;
 }
 function residualDown(x, params) {
   let out = convDown(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  let pooled = avgPool(x, 2, 2, "valid");
-  const zeros9 = zeros(pooled.shape);
+  let pooled = avgPool4(x, 2, 2, "valid");
+  const zeros2 = zeros(pooled.shape);
   const isPad = pooled.shape[3] !== out.shape[3];
   const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
   if (isAdjustShape) {
     const padShapeX = [...out.shape];
     padShapeX[1] = 1;
     const zerosW = zeros(padShapeX);
-    out = concat([out, zerosW], 1);
+    out = concat3([out, zerosW], 1);
     const padShapeY = [...out.shape];
     padShapeY[2] = 1;
     const zerosH = zeros(padShapeY);
-    out = concat([out, zerosH], 2);
+    out = concat3([out, zerosH], 2);
   }
-  pooled = isPad ? concat([pooled, zeros9], 3) : pooled;
-  out = add2(pooled, out);
-  out = relu(out);
+  pooled = isPad ? concat3([pooled, zeros2], 3) : pooled;
+  out = add8(pooled, out);
+  out = relu5(out);
   return out;
 }
 
 // build/src/faceRecognitionNet/FaceRecognitionNet.js
+import {
+  cast as cast3,
+  matMul as matMul2,
+  maxPool as maxPool2,
+  scalar as scalar4,
+  tidy as tidy18,
+  unstack as unstack4
+} from "@tensorflow/tfjs";
 class FaceRecognitionNet extends NeuralNetwork {
   constructor() {
     super("FaceRecognitionNet");
@@ -22520,12 +2772,12 @@ class FaceRecognitionNet extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceRecognitionNet - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(150, true), "float32");
+    return tidy18(() => {
+      const batchTensor = cast3(input.toBatchTensor(150, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
       let out = convDown(normalized, params.conv32_down);
-      out = maxPool(out, 3, 2, "valid");
+      out = maxPool2(out, 3, 2, "valid");
       out = residual(out, params.conv32_1);
       out = residual(out, params.conv32_2);
       out = residual(out, params.conv32_3);
@@ -22541,7 +2793,7 @@ class FaceRecognitionNet extends NeuralNetwork {
       out = residual(out, params.conv256_2);
       out = residualDown(out, params.conv256_down_out);
       const globalAvg = out.mean([1, 2]);
-      const fullyConnected = matMul(globalAvg, params.fc);
+      const fullyConnected = matMul2(globalAvg, params.fc);
       return fullyConnected;
     });
   }
@@ -22550,7 +2802,7 @@ class FaceRecognitionNet extends NeuralNetwork {
   }
   async computeFaceDescriptor(input) {
     const netInput = await toNetInput(input);
-    const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const faceDescriptorTensors = tidy18(() => unstack4(this.forwardInput(netInput)));
     const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data()));
     faceDescriptorTensors.forEach((t) => t.dispose());
     return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];
@@ -22600,13 +2852,18 @@ function extendWithGender(sourceObj, gender, genderProbability) {
 // build/src/factories/index.js
 
 // build/src/ssdMobilenetv1/extractParams.js
+import {
+  tensor1d as tensor1d5,
+  tensor3d,
+  tensor4d as tensor4d4
+} from "@tensorflow/tfjs";
 function extractorsFactory7(extractWeights, paramMappings) {
   function extractDepthwiseConvParams(numChannels, mappedPrefix) {
-    const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
-    const batch_norm_scale = tensor1d(extractWeights(numChannels));
-    const batch_norm_offset = tensor1d(extractWeights(numChannels));
-    const batch_norm_mean = tensor1d(extractWeights(numChannels));
-    const batch_norm_variance = tensor1d(extractWeights(numChannels));
+    const filters = tensor4d4(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+    const batch_norm_scale = tensor1d5(extractWeights(numChannels));
+    const batch_norm_offset = tensor1d5(extractWeights(numChannels));
+    const batch_norm_mean = tensor1d5(extractWeights(numChannels));
+    const batch_norm_variance = tensor1d5(extractWeights(numChannels));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`});
     return {
       filters,
@@ -22617,8 +2874,8 @@ function extractorsFactory7(extractWeights, paramMappings) {
     };
   }
   function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-    const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const filters = tensor4d4(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+    const bias = tensor1d5(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
     return {filters, bias};
   }
@@ -22857,28 +3114,40 @@ function extractParamsFromWeigthMap11(weightMap) {
 }
 
 // build/src/ssdMobilenetv1/pointwiseConvLayer.js
+import {
+  add as add9,
+  clipByValue,
+  conv2d as conv2d5,
+  tidy as tidy19
+} from "@tensorflow/tfjs";
 function pointwiseConvLayer(x, params, strides) {
-  return tidy(() => {
-    let out = conv2d(x, params.filters, strides, "same");
-    out = add2(out, params.batch_norm_offset);
+  return tidy19(() => {
+    let out = conv2d5(x, params.filters, strides, "same");
+    out = add9(out, params.batch_norm_offset);
     return clipByValue(out, 0, 6);
   });
 }
 
 // build/src/ssdMobilenetv1/mobileNetV1.js
+import {
+  batchNorm,
+  clipByValue as clipByValue2,
+  depthwiseConv2d,
+  tidy as tidy20
+} from "@tensorflow/tfjs";
 const epsilon = 0.0010000000474974513;
 function depthwiseConvLayer(x, params, strides) {
-  return tidy(() => {
+  return tidy20(() => {
     let out = depthwiseConv2d(x, params.filters, strides, "same");
     out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
-    return clipByValue(out, 0, 6);
+    return clipByValue2(out, 0, 6);
   });
 }
 function getStridesForLayerIdx(layerIdx) {
   return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];
 }
 function mobileNetV1(x, params) {
-  return tidy(() => {
+  return tidy20(() => {
     let conv11;
     let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
     const convPairParams = [
@@ -22916,7 +3185,7 @@ function mobileNetV1(x, params) {
 }
 
 // build/src/ssdMobilenetv1/nonMaxSuppression.js
-function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
   const numBoxes = boxes.shape[0];
   const outputSize = Math.min(maxOutputSize, numBoxes);
   const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score);
@@ -22967,15 +3236,31 @@ function IOU(boxes, i, j) {
 }
 
 // build/src/ssdMobilenetv1/outputLayer.js
+import {
+  add as add10,
+  div,
+  exp,
+  mul as mul2,
+  reshape,
+  scalar as scalar5,
+  sigmoid as sigmoid2,
+  slice,
+  stack as stack3,
+  sub as sub2,
+  tidy as tidy21,
+  tile,
+  transpose as transpose2,
+  unstack as unstack5
+} from "@tensorflow/tfjs";
 function getCenterCoordinatesAndSizesLayer(x) {
-  const vec = unstack(transpose(x, [1, 0]));
+  const vec = unstack5(transpose2(x, [1, 0]));
   const sizes = [
-    sub(vec[2], vec[0]),
-    sub(vec[3], vec[1])
+    sub2(vec[2], vec[0]),
+    sub2(vec[3], vec[1])
   ];
   const centers = [
-    add2(vec[0], div(sizes[0], scalar(2))),
-    add2(vec[1], div(sizes[1], scalar(2)))
+    add10(vec[0], div(sizes[0], scalar5(2))),
+    add10(vec[1], div(sizes[1], scalar5(2)))
   ];
   return {
     sizes,
@@ -22984,28 +3269,28 @@ function getCenterCoordinatesAndSizesLayer(x) {
 }
 function decodeBoxesLayer(x0, x1) {
   const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-  const vec = unstack(transpose(x1, [1, 0]));
-  const div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
-  const add0_out = add2(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
-  const div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
-  const add1_out = add2(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
-  return transpose(stack([
-    sub(add0_out, div0_out),
-    sub(add1_out, div1_out),
-    add2(add0_out, div0_out),
-    add2(add1_out, div1_out)
+  const vec = unstack5(transpose2(x1, [1, 0]));
+  const div0_out = div(mul2(exp(div(vec[2], scalar5(5))), sizes[0]), scalar5(2));
+  const add0_out = add10(mul2(div(vec[0], scalar5(10)), sizes[0]), centers[0]);
+  const div1_out = div(mul2(exp(div(vec[3], scalar5(5))), sizes[1]), scalar5(2));
+  const add1_out = add10(mul2(div(vec[1], scalar5(10)), sizes[1]), centers[1]);
+  return transpose2(stack3([
+    sub2(add0_out, div0_out),
+    sub2(add1_out, div1_out),
+    add10(add0_out, div0_out),
+    add10(add1_out, div1_out)
   ]), [1, 0]);
 }
 function outputLayer(boxPredictions, classPredictions, params) {
-  return tidy(() => {
+  return tidy21(() => {
     const batchSize = boxPredictions.shape[0];
     let boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
     boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
-    const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+    const scoresAndClasses = sigmoid2(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
     let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
     scores = reshape(scores, [batchSize, scores.shape[1]]);
-    const boxesByBatch = unstack(boxes);
-    const scoresByBatch = unstack(scores);
+    const boxesByBatch = unstack5(boxes);
+    const scoresByBatch = unstack5(scores);
     return {
       boxes: boxesByBatch,
       scores: scoresByBatch
@@ -23014,11 +3299,15 @@ function outputLayer(boxPredictions, classPredictions, params) {
 }
 
 // build/src/ssdMobilenetv1/boxPredictionLayer.js
+import {
+  reshape as reshape2,
+  tidy as tidy22
+} from "@tensorflow/tfjs";
 function boxPredictionLayer(x, params) {
-  return tidy(() => {
+  return tidy22(() => {
     const batchSize = x.shape[0];
-    const boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-    const classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+    const boxPredictionEncoding = reshape2(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+    const classPrediction = reshape2(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
     return {
       boxPredictionEncoding,
       classPrediction
@@ -23027,8 +3316,12 @@ function boxPredictionLayer(x, params) {
 }
 
 // build/src/ssdMobilenetv1/predictionLayer.js
+import {
+  concat as concat4,
+  tidy as tidy23
+} from "@tensorflow/tfjs";
 function predictionLayer(x, conv11, params) {
-  return tidy(() => {
+  return tidy23(() => {
     const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
     const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
     const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
@@ -23043,7 +3336,7 @@ function predictionLayer(x, conv11, params) {
     const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
     const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
     const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
-    const boxPredictions = concat([
+    const boxPredictions = concat4([
       boxPrediction0.boxPredictionEncoding,
       boxPrediction1.boxPredictionEncoding,
       boxPrediction2.boxPredictionEncoding,
@@ -23051,7 +3344,7 @@ function predictionLayer(x, conv11, params) {
       boxPrediction4.boxPredictionEncoding,
       boxPrediction5.boxPredictionEncoding
     ], 1);
-    const classPredictions = concat([
+    const classPredictions = concat4([
       boxPrediction0.classPrediction,
       boxPrediction1.classPrediction,
       boxPrediction2.classPrediction,
@@ -23088,6 +3381,13 @@ class SsdMobilenetv1Options {
 }
 
 // build/src/ssdMobilenetv1/SsdMobilenetv1.js
+import {
+  cast as cast4,
+  mul as mul3,
+  scalar as scalar6,
+  sub as sub3,
+  tidy as tidy24
+} from "@tensorflow/tfjs";
 class SsdMobilenetv1 extends NeuralNetwork {
   constructor() {
     super("SsdMobilenetv1");
@@ -23097,9 +3397,9 @@ class SsdMobilenetv1 extends NeuralNetwork {
     if (!params) {
       throw new Error("SsdMobilenetv1 - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(512, false), "float32");
-      const x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
+    return tidy24(() => {
+      const batchTensor = cast4(input.toBatchTensor(512, false), "float32");
+      const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1));
       const features = mobileNetV1(x, params.mobilenetv1);
       const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer);
       return outputLayer(boxPredictions, classPredictions, params.output_layer);
@@ -23120,7 +3420,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
     }
     const scoresData = Array.from(await scores.data());
     const iouThreshold = 0.5;
-    const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+    const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
     const reshapedDims = netInput.getReshapedInputDimensions(0);
     const inputSize = netInput.inputSize;
     const padX = inputSize / reshapedDims.width;
@@ -23188,7 +3488,7 @@ const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
 const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
 
 // build/src/tinyYolov2/config.js
-const isNumber2 = (arg) => typeof arg === "number";
+const isNumber = (arg) => typeof arg === "number";
 function validateConfig(config2) {
   if (!config2) {
     throw new Error(`invalid config: ${config2}`);
@@ -23196,58 +3496,83 @@ function validateConfig(config2) {
   if (typeof config2.withSeparableConvs !== "boolean") {
     throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
   }
-  if (!isNumber2(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
+  if (!isNumber(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
     throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config2.iouThreshold}`);
   }
   if (!Array.isArray(config2.classes) || !config2.classes.length || !config2.classes.every((c) => typeof c === "string")) {
     throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config2.classes)}`);
   }
-  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) {
+  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) {
     throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config2.anchors)}`);
   }
-  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber2))) {
+  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber))) {
     throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
   }
 }
 
 // build/src/tinyYolov2/leaky.js
+import {
+  add as add11,
+  mul as mul4,
+  relu as relu6,
+  scalar as scalar7,
+  sub as sub4,
+  tidy as tidy25
+} from "@tensorflow/tfjs";
 function leaky(x) {
-  return tidy(() => {
-    const min4 = mul(x, scalar(0.10000000149011612));
-    return add2(relu(sub(x, min4)), min4);
+  return tidy25(() => {
+    const min = mul4(x, scalar7(0.10000000149011612));
+    return add11(relu6(sub4(x, min)), min);
   });
 }
 
 // build/src/tinyYolov2/convWithBatchNorm.js
+import {
+  add as add12,
+  conv2d as conv2d6,
+  mul as mul5,
+  pad,
+  sub as sub5,
+  tidy as tidy26
+} from "@tensorflow/tfjs";
 function convWithBatchNorm(x, params) {
-  return tidy(() => {
+  return tidy26(() => {
     let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = conv2d(out, params.conv.filters, [1, 1], "valid");
-    out = sub(out, params.bn.sub);
-    out = mul(out, params.bn.truediv);
-    out = add2(out, params.conv.bias);
+    out = conv2d6(out, params.conv.filters, [1, 1], "valid");
+    out = sub5(out, params.bn.sub);
+    out = mul5(out, params.bn.truediv);
+    out = add12(out, params.conv.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/depthwiseSeparableConv.js
+import {
+  add as add13,
+  pad as pad2,
+  separableConv2d as separableConv2d2,
+  tidy as tidy27
+} from "@tensorflow/tfjs";
 function depthwiseSeparableConv3(x, params) {
-  return tidy(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = add2(out, params.bias);
+  return tidy27(() => {
+    let out = pad2(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+    out = separableConv2d2(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
+    out = add13(out, params.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/extractParams.js
+import {
+  tensor1d as tensor1d6
+} from "@tensorflow/tfjs";
 function extractorsFactory9(extractWeights, paramMappings) {
   const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
   function extractBatchNormParams(size, mappedPrefix) {
-    const sub32 = tensor1d(extractWeights(size));
-    const truediv = tensor1d(extractWeights(size));
+    const sub6 = tensor1d6(extractWeights(size));
+    const truediv = tensor1d6(extractWeights(size));
     paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
     const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
@@ -23301,9 +3626,9 @@ function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
 function extractorsFactory10(weightMap, paramMappings) {
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
   function extractBatchNormParams(prefix) {
-    const sub32 = extractWeightEntry(`${prefix}/sub`, 1);
+    const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
     const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvParams(prefix) {
     const filters = extractWeightEntry(`${prefix}/filters`, 4);
@@ -23385,6 +3710,14 @@ class TinyYolov2Options {
 }
 
 // build/src/tinyYolov2/TinyYolov2Base.js
+import {
+  cast as cast5,
+  maxPool as maxPool3,
+  scalar as scalar8,
+  softmax as softmax3,
+  tidy as tidy28,
+  unstack as unstack6
+} from "@tensorflow/tfjs";
 class TinyYolov2Base extends NeuralNetwork {
   constructor(config2) {
     super("TinyYolov2");
@@ -23402,34 +3735,34 @@ class TinyYolov2Base extends NeuralNetwork {
   }
   runTinyYolov2(x, params) {
     let out = convWithBatchNorm(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = maxPool3(out, [2, 2], [1, 1], "same");
     out = convWithBatchNorm(out, params.conv6);
     out = convWithBatchNorm(out, params.conv7);
     return convLayer(out, params.conv8, "valid", false);
   }
   runMobilenet(x, params) {
     let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv3(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = maxPool3(out, [2, 2], [1, 1], "same");
     out = params.conv6 ? depthwiseSeparableConv3(out, params.conv6) : out;
     out = params.conv7 ? depthwiseSeparableConv3(out, params.conv7) : out;
     return convLayer(out, params.conv8, "valid", false);
@@ -23439,10 +3772,10 @@ class TinyYolov2Base extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyYolov2 - load model before inference");
     }
-    return tidy(() => {
-      let batchTensor = cast(input.toBatchTensor(inputSize, false), "float32");
+    return tidy28(() => {
+      let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32");
       batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
-      batchTensor = batchTensor.div(scalar(256));
+      batchTensor = batchTensor.div(scalar8(256));
       return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
     });
   }
@@ -23453,7 +3786,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput, inputSize);
-    const out0 = tidy(() => unstack(out)[0].expandDims());
+    const out0 = tidy28(() => unstack6(out)[0].expandDims());
     const inputDimensions = {
       width: netInput.getInputWidth(0),
       height: netInput.getInputHeight(0)
@@ -23465,7 +3798,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const scores = results.map((res) => res.score);
     const classScores = results.map((res) => res.classScore);
     const classNames = results.map((res) => this.config.classes[res.label]);
-    const indices = nonMaxSuppression2(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
+    const indices = nonMaxSuppression(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
     const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions));
     return detections;
   }
@@ -23490,11 +3823,11 @@ class TinyYolov2Base extends NeuralNetwork {
     const correctionFactorY = inputSize / height;
     const numCells = outputTensor.shape[1];
     const numBoxes = this.config.anchors.length;
-    const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => {
+    const [boxesTensor, scoresTensor, classScoresTensor] = tidy28(() => {
       const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);
       const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
       const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
-      const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0);
+      const classScores = this.withClassScores ? softmax3(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar8(0);
       return [boxes, scores, classScores];
     });
     const results = [];
@@ -23503,22 +3836,23 @@ class TinyYolov2Base extends NeuralNetwork {
     for (let row = 0; row < numCells; row++) {
       for (let col = 0; col < numCells; col++) {
         for (let anchor = 0; anchor < numBoxes; anchor++) {
-          const score = sigmoid6(scoresData[row][col][anchor][0]);
+          const score = sigmoid(scoresData[row][col][anchor][0]);
           if (!scoreThreshold || score > scoreThreshold) {
-            const ctX = (col + sigmoid6(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
-            const ctY = (row + sigmoid6(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
+            const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
+            const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
             const width2 = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX;
             const height2 = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY;
             const x = ctX - width2 / 2;
             const y = ctY - height2 / 2;
             const pos = {row, col, anchor};
             const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0};
-            results.push(__assign({
+            results.push({
               box: new BoundingBox(x, y, x + width2, y + height2),
               score,
               classScore: score * classScore,
-              label
-            }, pos));
+              label,
+              ...pos
+            });
           }
         }
       }
@@ -23534,7 +3868,7 @@ class TinyYolov2Base extends NeuralNetwork {
     return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
       classScore,
       label
-    })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr);
+    })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
   }
 }
 TinyYolov2Base.DEFAULT_FILTER_SIZES = [
@@ -23611,11 +3945,14 @@ class ComposableTask {
 }
 
 // build/src/globalApi/extractFacesAndComputeResults.js
+import {
+  Tensor as Tensor4
+} from "@tensorflow/tfjs";
 async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) {
   const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection);
-  const faces = extractedFaces || (input instanceof Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
+  const faces = extractedFaces || (input instanceof Tensor4 ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
   const results = await computeResults(faces);
-  faces.forEach((f) => f instanceof Tensor && f.dispose());
+  faces.forEach((f) => f instanceof Tensor4 && f.dispose());
   return results;
 }
 async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
@@ -23833,6 +4170,9 @@ class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
 }
 
 // build/src/globalApi/DetectFaceLandmarksTasks.js
+import {
+  Tensor as Tensor5
+} from "@tensorflow/tfjs";
 class DetectFaceLandmarksTaskBase extends ComposableTask {
   constructor(parentTask, input, useTinyLandmarkNet) {
     super();
@@ -23848,9 +4188,9 @@ class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
   async run() {
     const parentResults = await this.parentTask;
     const detections = parentResults.map((res) => res.detection);
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
+    const faces = this.input instanceof Tensor5 ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
     const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face)));
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof Tensor5 && f.dispose());
     return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
   }
   withFaceExpressions() {
@@ -23870,9 +4210,9 @@ class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
       return;
     }
     const {detection} = parentResult;
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
+    const faces = this.input instanceof Tensor5 ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
     const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof Tensor5 && f.dispose());
     return extendWithFaceLandmarks(parentResult, landmarks);
   }
   withFaceExpressions() {
@@ -24061,12 +4401,13 @@ function resizeResults(results, dimensions) {
 }
 
 // build/package.json
-var version2 = "0.5.2";
+var version = "0.5.3";
 
 // build/src/index.js
+import * as tf42 from "@tensorflow/tfjs";
 const node = typeof process !== "undefined" ? process.version : false;
-const browser = typeof navigator !== "undefined" ? navigator.userAgent : false;
-const version3 = {faceapi: version2, node, browser, tfjs: version, backend: getBackend()};
+const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
+const version2 = {faceapi: version, node, browser: browser3};
 export {
   AgeGenderNet,
   BoundingBox,
@@ -24131,7 +4472,7 @@ export {
   detectLandmarks,
   detectSingleFace,
   draw_exports as draw,
-  env2 as env,
+  env,
   euclideanDistance,
   extendWithAge,
   extendWithFaceDescriptor,
@@ -24172,7 +4513,7 @@ export {
   matchDimensions,
   minBbox,
   nets,
-  nonMaxSuppression2 as nonMaxSuppression,
+  nonMaxSuppression,
   normalize,
   padToSquare,
   predictAgeAndGender,
@@ -24180,14 +4521,14 @@ export {
   resizeResults,
   resolveInput,
   shuffleArray,
-  sigmoid6 as sigmoid,
+  sigmoid,
   ssdMobilenetv1,
-  dist_exports as tf,
+  tf42 as tf,
   tinyFaceDetector,
   tinyYolov23 as tinyYolov2,
   toNetInput,
   utils_exports as utils,
   validateConfig,
-  version3 as version
+  version2 as version
 };
 //# sourceMappingURL=face-api.esm.js.map
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index 121deb8..ce37833 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["node_modules/seedrandom/lib/alea.js", "node_modules/seedrandom/lib/xor128.js", "node_modules/seedrandom/lib/xorwow.js", "node_modules/seedrandom/lib/xorshift7.js", "node_modules/seedrandom/lib/xor4096.js", "node_modules/seedrandom/lib/tychei.js", "disabled:crypto", "node_modules/seedrandom/seedrandom.js", "node_modules/seedrandom/index.js", "build/src/env/../../../src/env/isNodejs.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/environment.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/global_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_names.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/profiler.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tape.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_format.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/types.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/engine.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/device_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/flags.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util_env.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/operation.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/complex.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor_ops_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/types.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io_utils.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/router_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/model_management.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/buffer.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cast.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clone.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/print.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/base_side_effects.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/browser_files.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/progress.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/weights_loader.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/http.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/passthrough.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reshape.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mat_mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/one_hot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/confusion_matrix.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/math.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/browser.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/serialization.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/test_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/version.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/globals.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floorDiv.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/abs.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add_n.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/axis_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/all.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/any.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan2.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/basic_lstm_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batch_to_space_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_to.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ceil.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clip_by_value.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cumsum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depth_to_space.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depthwise_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/diag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dilation2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div_no_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/elu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/erf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expand_dims.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expm1.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tile.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/eye.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/fill.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reduce_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/segment_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/imag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_finite.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_inf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/maximum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scalar.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/leaky_relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/linspace.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/local_response_normalization.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log1p.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/gradients.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/neg.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softplus.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sub.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sum_exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_and.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_not.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_or.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_xor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_with_argmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mean.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/minimum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mod.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/square.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/moments.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multi_rnn_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multinomial.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/not_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/real.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/outer_product.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/space_to_batch_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pow.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/prelu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/prod.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rand.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rand_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_gamma.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_normal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_uniform.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/range.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reciprocal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/relu6.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/round.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rsqrt.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/selu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/separable_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/setdiff1d_async.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sign.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/fft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/ifft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/irfft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/split_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/split.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/rfft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sqrt.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/squared_difference.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/squeeze.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/stack.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/step.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/strided_slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor5d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor6d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/topk.ts", 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"build/src/globalApi/../../../src/globalApi/DetectFaceLandmarksTasks.ts", "build/src/globalApi/../../../src/globalApi/DetectFacesTasks.ts", "build/src/globalApi/../../../src/globalApi/detectFaces.ts", "build/src/globalApi/../../../src/globalApi/allFaces.ts", "build/src/../../src/euclideanDistance.ts", "build/src/globalApi/../../../src/globalApi/FaceMatcher.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/index.ts", "build/src/../../src/resizeResults.ts", "build/src/../../src/index.ts"],
-  "sourcesContent": ["// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],
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   "names": []
 }
diff --git a/dist/face-api.iife.js b/dist/face-api.iife.js
index 3691736..755d9ee 100644
--- a/dist/face-api.iife.js
+++ b/dist/face-api.iife.js
@@ -1,7 +1,6 @@
 var faceapi = (() => {
   var __defineProperty = Object.defineProperty;
   var __hasOwnProperty = Object.prototype.hasOwnProperty;
-  var __assign = Object.assign;
   var __commonJS = (callback, module) => () => {
     if (!module) {
       module = {exports: {}};
@@ -12,10 +11,10 @@ var faceapi = (() => {
   var __markAsModule = (target) => {
     return __defineProperty(target, "__esModule", {value: true});
   };
-  var __export = (target, all3) => {
+  var __export = (target, all) => {
     __markAsModule(target);
-    for (var name in all3)
-      __defineProperty(target, name, {get: all3[name], enumerable: true});
+    for (var name in all)
+      __defineProperty(target, name, {get: all[name], enumerable: true});
   };
   var __exportStar = (target, module) => {
     __markAsModule(target);
@@ -32,646 +31,6 @@ var faceapi = (() => {
     return __exportStar(__defineProperty({}, "default", {value: module, enumerable: true}), module);
   };
 
-  // node_modules/seedrandom/lib/alea.js
-  var require_alea = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function Alea(seed) {
-        var me = this, mash = Mash();
-        me.next = function() {
-          var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
-          me.s0 = me.s1;
-          me.s1 = me.s2;
-          return me.s2 = t - (me.c = t | 0);
-        };
-        me.c = 1;
-        me.s0 = mash(" ");
-        me.s1 = mash(" ");
-        me.s2 = mash(" ");
-        me.s0 -= mash(seed);
-        if (me.s0 < 0) {
-          me.s0 += 1;
-        }
-        me.s1 -= mash(seed);
-        if (me.s1 < 0) {
-          me.s1 += 1;
-        }
-        me.s2 -= mash(seed);
-        if (me.s2 < 0) {
-          me.s2 += 1;
-        }
-        mash = null;
-      }
-      function copy(f, t) {
-        t.c = f.c;
-        t.s0 = f.s0;
-        t.s1 = f.s1;
-        t.s2 = f.s2;
-        return t;
-      }
-      function impl(seed, opts) {
-        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
-        prng.int32 = function() {
-          return xg.next() * 4294967296 | 0;
-        };
-        prng.double = function() {
-          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
-        };
-        prng.quick = prng;
-        if (state) {
-          if (typeof state == "object")
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      function Mash() {
-        var n = 4022871197;
-        var mash = function(data) {
-          data = data.toString();
-          for (var i = 0; i < data.length; i++) {
-            n += data.charCodeAt(i);
-            var h = 0.02519603282416938 * n;
-            n = h >>> 0;
-            h -= n;
-            h *= n;
-            n = h >>> 0;
-            h -= n;
-            n += h * 4294967296;
-          }
-          return (n >>> 0) * 23283064365386963e-26;
-        };
-        return mash;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.alea = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // node_modules/seedrandom/lib/xor128.js
-  var require_xor128 = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function XorGen(seed) {
-        var me = this, strseed = "";
-        me.x = 0;
-        me.y = 0;
-        me.z = 0;
-        me.w = 0;
-        me.next = function() {
-          var t = me.x ^ me.x << 11;
-          me.x = me.y;
-          me.y = me.z;
-          me.z = me.w;
-          return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
-        };
-        if (seed === (seed | 0)) {
-          me.x = seed;
-        } else {
-          strseed += seed;
-        }
-        for (var k = 0; k < strseed.length + 64; k++) {
-          me.x ^= strseed.charCodeAt(k) | 0;
-          me.next();
-        }
-      }
-      function copy(f, t) {
-        t.x = f.x;
-        t.y = f.y;
-        t.z = f.z;
-        t.w = f.w;
-        return t;
-      }
-      function impl(seed, opts) {
-        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-          return (xg.next() >>> 0) / 4294967296;
-        };
-        prng.double = function() {
-          do {
-            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-          } while (result === 0);
-          return result;
-        };
-        prng.int32 = xg.next;
-        prng.quick = prng;
-        if (state) {
-          if (typeof state == "object")
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.xor128 = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // node_modules/seedrandom/lib/xorwow.js
-  var require_xorwow = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function XorGen(seed) {
-        var me = this, strseed = "";
-        me.next = function() {
-          var t = me.x ^ me.x >>> 2;
-          me.x = me.y;
-          me.y = me.z;
-          me.z = me.w;
-          me.w = me.v;
-          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
-        };
-        me.x = 0;
-        me.y = 0;
-        me.z = 0;
-        me.w = 0;
-        me.v = 0;
-        if (seed === (seed | 0)) {
-          me.x = seed;
-        } else {
-          strseed += seed;
-        }
-        for (var k = 0; k < strseed.length + 64; k++) {
-          me.x ^= strseed.charCodeAt(k) | 0;
-          if (k == strseed.length) {
-            me.d = me.x << 10 ^ me.x >>> 4;
-          }
-          me.next();
-        }
-      }
-      function copy(f, t) {
-        t.x = f.x;
-        t.y = f.y;
-        t.z = f.z;
-        t.w = f.w;
-        t.v = f.v;
-        t.d = f.d;
-        return t;
-      }
-      function impl(seed, opts) {
-        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-          return (xg.next() >>> 0) / 4294967296;
-        };
-        prng.double = function() {
-          do {
-            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-          } while (result === 0);
-          return result;
-        };
-        prng.int32 = xg.next;
-        prng.quick = prng;
-        if (state) {
-          if (typeof state == "object")
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.xorwow = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // node_modules/seedrandom/lib/xorshift7.js
-  var require_xorshift7 = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function XorGen(seed) {
-        var me = this;
-        me.next = function() {
-          var X = me.x, i = me.i, t, v, w;
-          t = X[i];
-          t ^= t >>> 7;
-          v = t ^ t << 24;
-          t = X[i + 1 & 7];
-          v ^= t ^ t >>> 10;
-          t = X[i + 3 & 7];
-          v ^= t ^ t >>> 3;
-          t = X[i + 4 & 7];
-          v ^= t ^ t << 7;
-          t = X[i + 7 & 7];
-          t = t ^ t << 13;
-          v ^= t ^ t << 9;
-          X[i] = v;
-          me.i = i + 1 & 7;
-          return v;
-        };
-        function init(me2, seed2) {
-          var j, w, X = [];
-          if (seed2 === (seed2 | 0)) {
-            w = X[0] = seed2;
-          } else {
-            seed2 = "" + seed2;
-            for (j = 0; j < seed2.length; ++j) {
-              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
-            }
-          }
-          while (X.length < 8)
-            X.push(0);
-          for (j = 0; j < 8 && X[j] === 0; ++j)
-            ;
-          if (j == 8)
-            w = X[7] = -1;
-          else
-            w = X[j];
-          me2.x = X;
-          me2.i = 0;
-          for (j = 256; j > 0; --j) {
-            me2.next();
-          }
-        }
-        init(me, seed);
-      }
-      function copy(f, t) {
-        t.x = f.x.slice();
-        t.i = f.i;
-        return t;
-      }
-      function impl(seed, opts) {
-        if (seed == null)
-          seed = +new Date();
-        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-          return (xg.next() >>> 0) / 4294967296;
-        };
-        prng.double = function() {
-          do {
-            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-          } while (result === 0);
-          return result;
-        };
-        prng.int32 = xg.next;
-        prng.quick = prng;
-        if (state) {
-          if (state.x)
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.xorshift7 = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // node_modules/seedrandom/lib/xor4096.js
-  var require_xor4096 = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function XorGen(seed) {
-        var me = this;
-        me.next = function() {
-          var w = me.w, X = me.X, i = me.i, t, v;
-          me.w = w = w + 1640531527 | 0;
-          v = X[i + 34 & 127];
-          t = X[i = i + 1 & 127];
-          v ^= v << 13;
-          t ^= t << 17;
-          v ^= v >>> 15;
-          t ^= t >>> 12;
-          v = X[i] = v ^ t;
-          me.i = i;
-          return v + (w ^ w >>> 16) | 0;
-        };
-        function init(me2, seed2) {
-          var t, v, i, j, w, X = [], limit = 128;
-          if (seed2 === (seed2 | 0)) {
-            v = seed2;
-            seed2 = null;
-          } else {
-            seed2 = seed2 + "\0";
-            v = 0;
-            limit = Math.max(limit, seed2.length);
-          }
-          for (i = 0, j = -32; j < limit; ++j) {
-            if (seed2)
-              v ^= seed2.charCodeAt((j + 32) % seed2.length);
-            if (j === 0)
-              w = v;
-            v ^= v << 10;
-            v ^= v >>> 15;
-            v ^= v << 4;
-            v ^= v >>> 13;
-            if (j >= 0) {
-              w = w + 1640531527 | 0;
-              t = X[j & 127] ^= v + w;
-              i = t == 0 ? i + 1 : 0;
-            }
-          }
-          if (i >= 128) {
-            X[(seed2 && seed2.length || 0) & 127] = -1;
-          }
-          i = 127;
-          for (j = 4 * 128; j > 0; --j) {
-            v = X[i + 34 & 127];
-            t = X[i = i + 1 & 127];
-            v ^= v << 13;
-            t ^= t << 17;
-            v ^= v >>> 15;
-            t ^= t >>> 12;
-            X[i] = v ^ t;
-          }
-          me2.w = w;
-          me2.X = X;
-          me2.i = i;
-        }
-        init(me, seed);
-      }
-      function copy(f, t) {
-        t.i = f.i;
-        t.w = f.w;
-        t.X = f.X.slice();
-        return t;
-      }
-      ;
-      function impl(seed, opts) {
-        if (seed == null)
-          seed = +new Date();
-        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-          return (xg.next() >>> 0) / 4294967296;
-        };
-        prng.double = function() {
-          do {
-            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-          } while (result === 0);
-          return result;
-        };
-        prng.int32 = xg.next;
-        prng.quick = prng;
-        if (state) {
-          if (state.X)
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.xor4096 = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // node_modules/seedrandom/lib/tychei.js
-  var require_tychei = __commonJS((exports, module) => {
-    (function(global2, module2, define2) {
-      function XorGen(seed) {
-        var me = this, strseed = "";
-        me.next = function() {
-          var b = me.b, c = me.c, d = me.d, a = me.a;
-          b = b << 25 ^ b >>> 7 ^ c;
-          c = c - d | 0;
-          d = d << 24 ^ d >>> 8 ^ a;
-          a = a - b | 0;
-          me.b = b = b << 20 ^ b >>> 12 ^ c;
-          me.c = c = c - d | 0;
-          me.d = d << 16 ^ c >>> 16 ^ a;
-          return me.a = a - b | 0;
-        };
-        me.a = 0;
-        me.b = 0;
-        me.c = 2654435769 | 0;
-        me.d = 1367130551;
-        if (seed === Math.floor(seed)) {
-          me.a = seed / 4294967296 | 0;
-          me.b = seed | 0;
-        } else {
-          strseed += seed;
-        }
-        for (var k = 0; k < strseed.length + 20; k++) {
-          me.b ^= strseed.charCodeAt(k) | 0;
-          me.next();
-        }
-      }
-      function copy(f, t) {
-        t.a = f.a;
-        t.b = f.b;
-        t.c = f.c;
-        t.d = f.d;
-        return t;
-      }
-      ;
-      function impl(seed, opts) {
-        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-          return (xg.next() >>> 0) / 4294967296;
-        };
-        prng.double = function() {
-          do {
-            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-          } while (result === 0);
-          return result;
-        };
-        prng.int32 = xg.next;
-        prng.quick = prng;
-        if (state) {
-          if (typeof state == "object")
-            copy(state, xg);
-          prng.state = function() {
-            return copy(xg, {});
-          };
-        }
-        return prng;
-      }
-      if (module2 && module2.exports) {
-        module2.exports = impl;
-      } else if (define2 && define2.amd) {
-        define2(function() {
-          return impl;
-        });
-      } else {
-        this.tychei = impl;
-      }
-    })(exports, typeof module == "object" && module, typeof define == "function" && define);
-  });
-
-  // disabled:crypto
-  var require_disabled_crypto = __commonJS(() => {
-  });
-
-  // node_modules/seedrandom/seedrandom.js
-  var require_seedrandom = __commonJS((exports, module) => {
-    (function(pool3, math) {
-      var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
-      function seedrandom2(seed, options, callback) {
-        var key = [];
-        options = options == true ? {entropy: true} : options || {};
-        var shortseed = mixkey(flatten2(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
-        var arc4 = new ARC4(key);
-        var prng = function() {
-          var n = arc4.g(chunks), d = startdenom, x = 0;
-          while (n < significance) {
-            n = (n + x) * width;
-            d *= width;
-            x = arc4.g(1);
-          }
-          while (n >= overflow) {
-            n /= 2;
-            d /= 2;
-            x >>>= 1;
-          }
-          return (n + x) / d;
-        };
-        prng.int32 = function() {
-          return arc4.g(4) | 0;
-        };
-        prng.quick = function() {
-          return arc4.g(4) / 4294967296;
-        };
-        prng.double = prng;
-        mixkey(tostring(arc4.S), pool3);
-        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
-          if (state) {
-            if (state.S) {
-              copy(state, arc4);
-            }
-            prng2.state = function() {
-              return copy(arc4, {});
-            };
-          }
-          if (is_math_call) {
-            math[rngname] = prng2;
-            return seed2;
-          } else
-            return prng2;
-        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
-      }
-      math["seed" + rngname] = seedrandom2;
-      function ARC4(key) {
-        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
-        if (!keylen) {
-          key = [keylen++];
-        }
-        while (i < width) {
-          s[i] = i++;
-        }
-        for (i = 0; i < width; i++) {
-          s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
-          s[j] = t;
-        }
-        (me.g = function(count) {
-          var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
-          while (count--) {
-            t2 = s2[i2 = mask & i2 + 1];
-            r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
-          }
-          me.i = i2;
-          me.j = j2;
-          return r;
-        })(width);
-      }
-      function copy(f, t) {
-        t.i = f.i;
-        t.j = f.j;
-        t.S = f.S.slice();
-        return t;
-      }
-      ;
-      function flatten2(obj, depth) {
-        var result = [], typ = typeof obj, prop;
-        if (depth && typ == "object") {
-          for (prop in obj) {
-            try {
-              result.push(flatten2(obj[prop], depth - 1));
-            } catch (e) {
-            }
-          }
-        }
-        return result.length ? result : typ == "string" ? obj : obj + "\0";
-      }
-      function mixkey(seed, key) {
-        var stringseed = seed + "", smear, j = 0;
-        while (j < stringseed.length) {
-          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
-        }
-        return tostring(key);
-      }
-      function autoseed() {
-        try {
-          var out;
-          if (nodecrypto && (out = nodecrypto.randomBytes)) {
-            out = out(width);
-          } else {
-            out = new Uint8Array(width);
-            (global2.crypto || global2.msCrypto).getRandomValues(out);
-          }
-          return tostring(out);
-        } catch (e) {
-          var browser = global2.navigator, plugins = browser && browser.plugins;
-          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
-        }
-      }
-      function tostring(a) {
-        return String.fromCharCode.apply(0, a);
-      }
-      mixkey(math.random(), pool3);
-      if (typeof module == "object" && module.exports) {
-        module.exports = seedrandom2;
-        try {
-          nodecrypto = require_disabled_crypto();
-        } catch (ex) {
-        }
-      } else if (typeof define == "function" && define.amd) {
-        define(function() {
-          return seedrandom2;
-        });
-      }
-    })([], Math);
-  });
-
-  // node_modules/seedrandom/index.js
-  var require_seedrandom2 = __commonJS((exports, module) => {
-    var alea2 = require_alea();
-    var xor128 = require_xor128();
-    var xorwow = require_xorwow();
-    var xorshift7 = require_xorshift7();
-    var xor4096 = require_xor4096();
-    var tychei = require_tychei();
-    var sr = require_seedrandom();
-    sr.alea = alea2;
-    sr.xor128 = xor128;
-    sr.xorwow = xorwow;
-    sr.xorshift7 = xorshift7;
-    sr.xor4096 = xor4096;
-    sr.tychei = tychei;
-    module.exports = sr;
-  });
-
   // build/src/env/isNodejs.js
   var require_isNodejs = __commonJS((exports, module) => {
     __export(exports, {
@@ -748,7 +107,7 @@ var faceapi = (() => {
       detectLandmarks: () => detectLandmarks,
       detectSingleFace: () => detectSingleFace,
       draw: () => draw_exports,
-      env: () => env2,
+      env: () => env,
       euclideanDistance: () => euclideanDistance,
       extendWithAge: () => extendWithAge,
       extendWithFaceDescriptor: () => extendWithFaceDescriptor,
@@ -789,7 +148,7 @@ var faceapi = (() => {
       matchDimensions: () => matchDimensions,
       minBbox: () => minBbox,
       nets: () => nets,
-      nonMaxSuppression: () => nonMaxSuppression2,
+      nonMaxSuppression: () => nonMaxSuppression,
       normalize: () => normalize,
       padToSquare: () => padToSquare,
       predictAgeAndGender: () => predictAgeAndGender,
@@ -797,19272 +156,22 @@ var faceapi = (() => {
       resizeResults: () => resizeResults,
       resolveInput: () => resolveInput,
       shuffleArray: () => shuffleArray,
-      sigmoid: () => sigmoid6,
+      sigmoid: () => sigmoid,
       ssdMobilenetv1: () => ssdMobilenetv1,
-      tf: () => dist_exports,
+      tf: () => tf42,
       tinyFaceDetector: () => tinyFaceDetector,
       tinyYolov2: () => tinyYolov23,
       toNetInput: () => toNetInput,
       utils: () => utils_exports,
       validateConfig: () => validateConfig,
-      version: () => version3
+      version: () => version2
     });
+    const tf42 = __toModule(require("@tensorflow/tfjs"));
     const node = typeof process !== "undefined" ? process.version : false;
-    const browser = typeof navigator !== "undefined" ? navigator.userAgent : false;
-    const version3 = {faceapi: version2, node, browser, tfjs: version, backend: getBackend()};
+    const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
+    const version2 = {faceapi: version, node, browser: browser3};
   });
 
-  // node_modules/@tensorflow/tfjs-core/dist/environment.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
-  class Environment {
-    constructor(global2) {
-      this.global = global2;
-      this.flags = {};
-      this.flagRegistry = {};
-      this.urlFlags = {};
-      this.populateURLFlags();
-    }
-    setPlatform(platformName, platform) {
-      if (this.platform != null) {
-        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
-      }
-      this.platformName = platformName;
-      this.platform = platform;
-    }
-    registerFlag(flagName, evaluationFn, setHook) {
-      this.flagRegistry[flagName] = {evaluationFn, setHook};
-      if (this.urlFlags[flagName] != null) {
-        const flagValue = this.urlFlags[flagName];
-        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
-        this.set(flagName, flagValue);
-      }
-    }
-    async getAsync(flagName) {
-      if (flagName in this.flags) {
-        return this.flags[flagName];
-      }
-      this.flags[flagName] = await this.evaluateFlag(flagName);
-      return this.flags[flagName];
-    }
-    get(flagName) {
-      if (flagName in this.flags) {
-        return this.flags[flagName];
-      }
-      const flagValue = this.evaluateFlag(flagName);
-      if (flagValue instanceof Promise) {
-        throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
-      }
-      this.flags[flagName] = flagValue;
-      return this.flags[flagName];
-    }
-    getNumber(flagName) {
-      return this.get(flagName);
-    }
-    getBool(flagName) {
-      return this.get(flagName);
-    }
-    getFlags() {
-      return this.flags;
-    }
-    get features() {
-      return this.flags;
-    }
-    set(flagName, value) {
-      if (this.flagRegistry[flagName] == null) {
-        throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
-      }
-      this.flags[flagName] = value;
-      if (this.flagRegistry[flagName].setHook != null) {
-        this.flagRegistry[flagName].setHook(value);
-      }
-    }
-    evaluateFlag(flagName) {
-      if (this.flagRegistry[flagName] == null) {
-        throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
-      }
-      return this.flagRegistry[flagName].evaluationFn();
-    }
-    setFlags(flags3) {
-      this.flags = Object.assign({}, flags3);
-    }
-    reset() {
-      this.flags = {};
-      this.urlFlags = {};
-      this.populateURLFlags();
-    }
-    populateURLFlags() {
-      if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
-        return;
-      }
-      const urlParams = getQueryParams(this.global.location.search);
-      if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-        const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
-        keyValues.forEach((keyValue) => {
-          const [key, value] = keyValue.split(":");
-          this.urlFlags[key] = parseValue(key, value);
-        });
-      }
-    }
-  }
-  function getQueryParams(queryString) {
-    const params = {};
-    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
-      decodeParam(params, t[0], t[1]);
-      return t.join("=");
-    });
-    return params;
-  }
-  function decodeParam(params, name, value) {
-    params[decodeURIComponent(name)] = decodeURIComponent(value || "");
-  }
-  function parseValue(flagName, value) {
-    value = value.toLowerCase();
-    if (value === "true" || value === "false") {
-      return value === "true";
-    } else if (`${+value}` === value) {
-      return +value;
-    }
-    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
-  }
-  function env() {
-    return ENV;
-  }
-  let ENV = null;
-  function setEnvironmentGlobal(environment12) {
-    ENV = environment12;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/global_util.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  let globalNameSpace;
-  function getGlobalNamespace() {
-    if (globalNameSpace == null) {
-      let ns;
-      if (typeof window !== "undefined") {
-        ns = window;
-      } else if (typeof global !== "undefined") {
-        ns = global;
-      } else if (typeof process !== "undefined") {
-        ns = process;
-      } else if (typeof self !== "undefined") {
-        ns = self;
-      } else {
-        throw new Error("Could not find a global object");
-      }
-      globalNameSpace = ns;
-    }
-    return globalNameSpace;
-  }
-  function getGlobalMap() {
-    const ns = getGlobalNamespace();
-    if (ns._tfGlobals == null) {
-      ns._tfGlobals = new Map();
-    }
-    return ns._tfGlobals;
-  }
-  function getGlobal(key, init) {
-    const globalMap = getGlobalMap();
-    if (globalMap.has(key)) {
-      return globalMap.get(key);
-    } else {
-      const singleton = init();
-      globalMap.set(key, singleton);
-      return globalMap.get(key);
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
-  const Abs = "Abs";
-  const Acos = "Acos";
-  const Acosh = "Acosh";
-  const Add = "Add";
-  const AddN = "AddN";
-  const All = "All";
-  const Any = "Any";
-  const ArgMax = "ArgMax";
-  const ArgMin = "ArgMin";
-  const Asin = "Asin";
-  const Asinh = "Asinh";
-  const Atan = "Atan";
-  const Atanh = "Atanh";
-  const Atan2 = "Atan2";
-  const AvgPool = "AvgPool";
-  const AvgPoolBackprop = "AvgPoolBackprop";
-  const AvgPool3D = "AvgPool3D";
-  const AvgPool3DBackprop = "AvgPool3DBackprop";
-  const BatchMatMul = "BatchMatMul";
-  const BatchToSpaceND = "BatchToSpaceND";
-  const BroadcastTo = "BroadcastTo";
-  const Cast = "Cast";
-  const Ceil = "Ceil";
-  const ClipByValue = "ClipByValue";
-  const Complex = "Complex";
-  const Concat = "Concat";
-  const Conv2D = "Conv2D";
-  const Conv2DBackpropFilter = "Conv2DBackpropFilter";
-  const Conv2DBackpropInput = "Conv2DBackpropInput";
-  const Conv3D = "Conv3D";
-  const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
-  const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
-  const Cos = "Cos";
-  const Cosh = "Cosh";
-  const Cumsum = "Cumsum";
-  const CropAndResize = "CropAndResize";
-  const DepthToSpace = "DepthToSpace";
-  const DepthwiseConv2dNative = "DepthwiseConv2dNative";
-  const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
-  const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
-  const Diag = "Diag";
-  const Dilation2D = "Dilation2D";
-  const Dilation2DBackpropInput = "Dilation2DBackpropInput";
-  const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
-  const Div = "Div";
-  const Elu = "Elu";
-  const EluGrad = "EluGrad";
-  const Erf = "Erf";
-  const Equal = "Equal";
-  const Exp = "Exp";
-  const Expm1 = "Expm1";
-  const FFT = "FFT";
-  const Fill = "Fill";
-  const FlipLeftRight = "FlipLeftRight";
-  const Floor = "Floor";
-  const FloorDiv = "FloorDiv";
-  const FusedBatchNorm = "FusedBatchNorm";
-  const GatherV2 = "GatherV2";
-  const GatherNd = "GatherNd";
-  const Greater = "Greater";
-  const GreaterEqual = "GreaterEqual";
-  const Identity = "Identity";
-  const IFFT = "IFFT";
-  const Imag = "Imag";
-  const IsFinite = "IsFinite";
-  const IsInf = "IsInf";
-  const IsNan = "IsNan";
-  const Less = "Less";
-  const LessEqual = "LessEqual";
-  const LinSpace = "LinSpace";
-  const Log = "Log";
-  const Log1p = "Log1p";
-  const LogicalAnd = "LogicalAnd";
-  const LogicalNot = "LogicalNot";
-  const LogicalOr = "LogicalOr";
-  const LogSoftmax = "LogSoftmax";
-  const LRN = "LRN";
-  const LRNBackprop = "LRNBackprop";
-  const Max = "Max";
-  const Maximum = "Maximum";
-  const MaxPool = "MaxPool";
-  const MaxPoolBackprop = "MaxPoolBackprop";
-  const MaxPool3D = "MaxPool3D";
-  const MaxPool3DBackprop = "MaxPool3DBackprop";
-  const MaxPoolWithArgmax = "MaxPoolWithArgmax";
-  const Mean = "Mean";
-  const Min = "Min";
-  const Minimum = "Minimum";
-  const Mod = "Mod";
-  const Multiply = "Multiply";
-  const Negate = "Negate";
-  const NotEqual = "NotEqual";
-  const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
-  const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
-  const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
-  const OnesLike = "OnesLike";
-  const OneHot = "OneHot";
-  const PadV2 = "PadV2";
-  const Pool = "Pool";
-  const Pow = "Pow";
-  const Prelu = "Prelu";
-  const Prod = "Prod";
-  const Range = "Range";
-  const Real = "Real";
-  const Reciprocal = "Reciprocal";
-  const Relu = "Relu";
-  const Reshape = "Reshape";
-  const ResizeNearestNeighbor = "ResizeNearestNeighbor";
-  const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
-  const ResizeBilinear = "ResizeBilinear";
-  const ResizeBilinearGrad = "ResizeBilinearGrad";
-  const Relu6 = "Relu6";
-  const Reverse = "Reverse";
-  const Round = "Round";
-  const Rsqrt = "Rsqrt";
-  const ScatterNd = "ScatterNd";
-  const SelectV2 = "SelectV2";
-  const Selu = "Selu";
-  const Slice = "Slice";
-  const Sin = "Sin";
-  const Sinh = "Sinh";
-  const Sign = "Sign";
-  const Sigmoid = "Sigmoid";
-  const Softplus = "Softplus";
-  const Sqrt = "Sqrt";
-  const Sum = "Sum";
-  const SpaceToBatchND = "SpaceToBatchND";
-  const SplitV = "SplitV";
-  const Softmax = "Softmax";
-  const SquaredDifference = "SquaredDifference";
-  const Square = "Square";
-  const Sub = "Sub";
-  const SparseToDense = "SparseToDense";
-  const StridedSlice = "StridedSlice";
-  const Tan = "Tan";
-  const Tanh = "Tanh";
-  const Tile = "Tile";
-  const TopK = "TopK";
-  const Transpose = "Transpose";
-  const Unpack = "Unpack";
-  const UnsortedSegmentSum = "UnsortedSegmentSum";
-  const ZerosLike = "ZerosLike";
-  const Step = "Step";
-  const FromPixels = "FromPixels";
-  const RotateWithOffset = "RotateWithOffset";
-  const _FusedMatMul = "_FusedMatMul";
-  const FusedConv2D = "FusedConv2D";
-  const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
-
-  // node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const kernelRegistry = getGlobal("kernelRegistry", () => new Map());
-  const gradRegistry = getGlobal("gradRegistry", () => new Map());
-  function getKernel(kernelName, backendName) {
-    const key = makeKey(kernelName, backendName);
-    return kernelRegistry.get(key);
-  }
-  function getGradient(kernelName) {
-    return gradRegistry.get(kernelName);
-  }
-  function getKernelsForBackend(backendName) {
-    const it = kernelRegistry.entries();
-    const result = [];
-    while (true) {
-      const {done, value} = it.next();
-      if (done) {
-        break;
-      }
-      const [key, config2] = value;
-      const [backend2] = key.split("_");
-      if (backend2 === backendName) {
-        result.push(config2);
-      }
-    }
-    return result;
-  }
-  function registerKernel(config2) {
-    const {kernelName, backendName} = config2;
-    const key = makeKey(kernelName, backendName);
-    if (kernelRegistry.has(key)) {
-      console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
-    }
-    kernelRegistry.set(key, config2);
-  }
-  function registerGradient(config2) {
-    const {kernelName} = config2;
-    if (gradRegistry.has(kernelName)) {
-      if (env().getBool("DEBUG")) {
-        console.warn(`Overriding the gradient for '${kernelName}'`);
-      }
-    }
-    gradRegistry.set(kernelName, config2);
-  }
-  function unregisterKernel(kernelName, backendName) {
-    const key = makeKey(kernelName, backendName);
-    if (!kernelRegistry.has(key)) {
-      throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
-    }
-    kernelRegistry.delete(key);
-  }
-  function unregisterGradient(kernelName) {
-    if (!gradRegistry.has(kernelName)) {
-      throw new Error(`The gradient '${kernelName}' for backend is not registered`);
-    }
-    gradRegistry.delete(kernelName);
-  }
-  function makeKey(kernelName, backendName) {
-    return `${backendName}_${kernelName}`;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/util.js
-  const util_exports = {};
-  __export(util_exports, {
-    arraysEqual: () => arraysEqual,
-    assert: () => assert,
-    assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
-    assertNonNull: () => assertNonNull,
-    assertShapesMatch: () => assertShapesMatch,
-    bytesFromStringArray: () => bytesFromStringArray,
-    bytesPerElement: () => bytesPerElement,
-    checkConversionForErrors: () => checkConversionForErrors,
-    clamp: () => clamp,
-    computeStrides: () => computeStrides,
-    createShuffledIndices: () => createShuffledIndices,
-    decodeString: () => decodeString,
-    distSquared: () => distSquared,
-    encodeString: () => encodeString,
-    fetch: () => fetch2,
-    flatten: () => flatten,
-    getArrayFromDType: () => getArrayFromDType,
-    getTypedArrayFromDType: () => getTypedArrayFromDType,
-    hasEncodingLoss: () => hasEncodingLoss,
-    indexToLoc: () => indexToLoc,
-    inferDtype: () => inferDtype,
-    inferFromImplicitShape: () => inferFromImplicitShape,
-    isBoolean: () => isBoolean,
-    isFunction: () => isFunction,
-    isInt: () => isInt,
-    isNumber: () => isNumber,
-    isScalarShape: () => isScalarShape,
-    isString: () => isString,
-    isTypedArray: () => isTypedArray,
-    isValidDtype: () => isValidDtype,
-    locToIndex: () => locToIndex,
-    makeOnesTypedArray: () => makeOnesTypedArray,
-    makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
-    makeZerosTypedArray: () => makeZerosTypedArray,
-    nearestDivisor: () => nearestDivisor,
-    nearestLargerEven: () => nearestLargerEven,
-    now: () => now,
-    parseAxisParam: () => parseAxisParam,
-    randUniform: () => randUniform,
-    repeatedTry: () => repeatedTry,
-    rightPad: () => rightPad,
-    shuffle: () => shuffle,
-    sizeFromShape: () => sizeFromShape,
-    sizeToSquarishShape: () => sizeToSquarishShape,
-    squeezeShape: () => squeezeShape,
-    sum: () => sum,
-    tanh: () => tanh,
-    toNestedArray: () => toNestedArray,
-    toTypedArray: () => toTypedArray
-  });
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function shuffle(array) {
-    let counter = array.length;
-    let temp = 0;
-    let index = 0;
-    while (counter > 0) {
-      index = Math.random() * counter | 0;
-      counter--;
-      temp = array[counter];
-      array[counter] = array[index];
-      array[index] = temp;
-    }
-  }
-  function clamp(min4, x, max6) {
-    return Math.max(min4, Math.min(x, max6));
-  }
-  function nearestLargerEven(val) {
-    return val % 2 === 0 ? val : val + 1;
-  }
-  function sum(arr) {
-    let sum26 = 0;
-    for (let i = 0; i < arr.length; i++) {
-      sum26 += arr[i];
-    }
-    return sum26;
-  }
-  function randUniform(a, b) {
-    const r = Math.random();
-    return b * r + (1 - r) * a;
-  }
-  function distSquared(a, b) {
-    let result = 0;
-    for (let i = 0; i < a.length; i++) {
-      const diff = Number(a[i]) - Number(b[i]);
-      result += diff * diff;
-    }
-    return result;
-  }
-  function assert(expr, msg) {
-    if (!expr) {
-      throw new Error(typeof msg === "string" ? msg : msg());
-    }
-  }
-  function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
-    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-  }
-  function assertNonNull(a) {
-    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
-  }
-  function flatten(arr, result = [], skipTypedArray = false) {
-    if (result == null) {
-      result = [];
-    }
-    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-      for (let i = 0; i < arr.length; ++i) {
-        flatten(arr[i], result, skipTypedArray);
-      }
-    } else {
-      result.push(arr);
-    }
-    return result;
-  }
-  function sizeFromShape(shape) {
-    if (shape.length === 0) {
-      return 1;
-    }
-    let size = shape[0];
-    for (let i = 1; i < shape.length; i++) {
-      size *= shape[i];
-    }
-    return size;
-  }
-  function isScalarShape(shape) {
-    return shape.length === 0;
-  }
-  function arraysEqual(n1, n2) {
-    if (n1 === n2) {
-      return true;
-    }
-    if (n1 == null || n2 == null) {
-      return false;
-    }
-    if (n1.length !== n2.length) {
-      return false;
-    }
-    for (let i = 0; i < n1.length; i++) {
-      if (n1[i] !== n2[i]) {
-        return false;
-      }
-    }
-    return true;
-  }
-  function isInt(a) {
-    return a % 1 === 0;
-  }
-  function tanh(x) {
-    if (Math.tanh != null) {
-      return Math.tanh(x);
-    }
-    if (x === Infinity) {
-      return 1;
-    } else if (x === -Infinity) {
-      return -1;
-    } else {
-      const e2x = Math.exp(2 * x);
-      return (e2x - 1) / (e2x + 1);
-    }
-  }
-  function sizeToSquarishShape(size) {
-    const width = Math.ceil(Math.sqrt(size));
-    return [width, Math.ceil(size / width)];
-  }
-  function createShuffledIndices(n) {
-    const shuffledIndices = new Uint32Array(n);
-    for (let i = 0; i < n; ++i) {
-      shuffledIndices[i] = i;
-    }
-    shuffle(shuffledIndices);
-    return shuffledIndices;
-  }
-  function rightPad(a, size) {
-    if (size <= a.length) {
-      return a;
-    }
-    return a + " ".repeat(size - a.length);
-  }
-  function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
-    return new Promise((resolve, reject) => {
-      let tryCount = 0;
-      const tryFn = () => {
-        if (checkFn()) {
-          resolve();
-          return;
-        }
-        tryCount++;
-        const nextBackoff = delayFn(tryCount);
-        if (maxCounter != null && tryCount >= maxCounter) {
-          reject();
-          return;
-        }
-        setTimeout(tryFn, nextBackoff);
-      };
-      tryFn();
-    });
-  }
-  function inferFromImplicitShape(shape, size) {
-    let shapeProd = 1;
-    let implicitIdx = -1;
-    for (let i = 0; i < shape.length; ++i) {
-      if (shape[i] >= 0) {
-        shapeProd *= shape[i];
-      } else if (shape[i] === -1) {
-        if (implicitIdx !== -1) {
-          throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
-        }
-        implicitIdx = i;
-      } else if (shape[i] < 0) {
-        throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
-      }
-    }
-    if (implicitIdx === -1) {
-      if (size > 0 && size !== shapeProd) {
-        throw Error(`Size(${size}) must match the product of shape ${shape}`);
-      }
-      return shape;
-    }
-    if (shapeProd === 0) {
-      throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
-    }
-    if (size % shapeProd !== 0) {
-      throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
-    }
-    const newShape = shape.slice();
-    newShape[implicitIdx] = size / shapeProd;
-    return newShape;
-  }
-  function parseAxisParam(axis, shape) {
-    const rank = shape.length;
-    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
-    assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
-    assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
-    return axis.map((a) => a < 0 ? rank + a : a);
-  }
-  function squeezeShape(shape, axis) {
-    const newShape = [];
-    const keptDims = [];
-    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
-    const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
-    let j = 0;
-    for (let i = 0; i < shape.length; ++i) {
-      if (axes != null) {
-        if (axes[j] === i && shape[i] !== 1) {
-          throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
-        }
-        if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
-          newShape.push(shape[i]);
-          keptDims.push(i);
-        }
-        if (axes[j] <= i) {
-          j++;
-        }
-      }
-      if (shape[i] !== 1) {
-        newShape.push(shape[i]);
-        keptDims.push(i);
-      }
-    }
-    return {newShape, keptDims};
-  }
-  function getTypedArrayFromDType(dtype, size) {
-    let values = null;
-    if (dtype == null || dtype === "float32") {
-      values = new Float32Array(size);
-    } else if (dtype === "int32") {
-      values = new Int32Array(size);
-    } else if (dtype === "bool") {
-      values = new Uint8Array(size);
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-    return values;
-  }
-  function getArrayFromDType(dtype, size) {
-    let values = null;
-    if (dtype == null || dtype === "float32") {
-      values = new Float32Array(size);
-    } else if (dtype === "int32") {
-      values = new Int32Array(size);
-    } else if (dtype === "bool") {
-      values = new Uint8Array(size);
-    } else if (dtype === "string") {
-      values = new Array(size);
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-    return values;
-  }
-  function checkConversionForErrors(vals, dtype) {
-    for (let i = 0; i < vals.length; i++) {
-      const num = vals[i];
-      if (isNaN(num) || !isFinite(num)) {
-        throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
-      }
-    }
-  }
-  function isValidDtype(dtype) {
-    return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
-  }
-  function hasEncodingLoss(oldType, newType) {
-    if (newType === "complex64") {
-      return false;
-    }
-    if (newType === "float32" && oldType !== "complex64") {
-      return false;
-    }
-    if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
-      return false;
-    }
-    if (newType === "bool" && oldType === "bool") {
-      return false;
-    }
-    return true;
-  }
-  function isTypedArray(a) {
-    return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
-  }
-  function bytesPerElement(dtype) {
-    if (dtype === "float32" || dtype === "int32") {
-      return 4;
-    } else if (dtype === "complex64") {
-      return 8;
-    } else if (dtype === "bool") {
-      return 1;
-    } else {
-      throw new Error(`Unknown dtype ${dtype}`);
-    }
-  }
-  function bytesFromStringArray(arr) {
-    if (arr == null) {
-      return 0;
-    }
-    let bytes = 0;
-    arr.forEach((x) => bytes += x.length);
-    return bytes;
-  }
-  function isString(value) {
-    return typeof value === "string" || value instanceof String;
-  }
-  function isBoolean(value) {
-    return typeof value === "boolean";
-  }
-  function isNumber(value) {
-    return typeof value === "number";
-  }
-  function inferDtype(values) {
-    if (Array.isArray(values)) {
-      return inferDtype(values[0]);
-    }
-    if (values instanceof Float32Array) {
-      return "float32";
-    } else if (values instanceof Int32Array || values instanceof Uint8Array) {
-      return "int32";
-    } else if (isNumber(values)) {
-      return "float32";
-    } else if (isString(values)) {
-      return "string";
-    } else if (isBoolean(values)) {
-      return "bool";
-    }
-    return "float32";
-  }
-  function isFunction(f) {
-    return !!(f && f.constructor && f.call && f.apply);
-  }
-  function nearestDivisor(size, start) {
-    for (let i = start; i < size; ++i) {
-      if (size % i === 0) {
-        return i;
-      }
-    }
-    return size;
-  }
-  function computeStrides(shape) {
-    const rank = shape.length;
-    if (rank < 2) {
-      return [];
-    }
-    const strides = new Array(rank - 1);
-    strides[rank - 2] = shape[rank - 1];
-    for (let i = rank - 3; i >= 0; --i) {
-      strides[i] = strides[i + 1] * shape[i + 1];
-    }
-    return strides;
-  }
-  function toTypedArray(a, dtype) {
-    if (dtype === "string") {
-      throw new Error("Cannot convert a string[] to a TypedArray");
-    }
-    if (Array.isArray(a)) {
-      a = flatten(a);
-    }
-    if (env().getBool("DEBUG")) {
-      checkConversionForErrors(a, dtype);
-    }
-    if (noConversionNeeded(a, dtype)) {
-      return a;
-    }
-    if (dtype == null || dtype === "float32" || dtype === "complex64") {
-      return new Float32Array(a);
-    } else if (dtype === "int32") {
-      return new Int32Array(a);
-    } else if (dtype === "bool") {
-      const bool = new Uint8Array(a.length);
-      for (let i = 0; i < bool.length; ++i) {
-        if (Math.round(a[i]) !== 0) {
-          bool[i] = 1;
-        }
-      }
-      return bool;
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-  }
-  function createNestedArray(offset, shape, a) {
-    const ret = new Array();
-    if (shape.length === 1) {
-      const d = shape[0];
-      for (let i = 0; i < d; i++) {
-        ret[i] = a[offset + i];
-      }
-    } else {
-      const d = shape[0];
-      const rest = shape.slice(1);
-      const len = rest.reduce((acc, c) => acc * c);
-      for (let i = 0; i < d; i++) {
-        ret[i] = createNestedArray(offset + i * len, rest, a);
-      }
-    }
-    return ret;
-  }
-  function toNestedArray(shape, a) {
-    if (shape.length === 0) {
-      return a[0];
-    }
-    const size = shape.reduce((acc, c) => acc * c);
-    if (size === 0) {
-      return [];
-    }
-    if (size !== a.length) {
-      throw new Error(`[${shape}] does not match the input size ${a.length}.`);
-    }
-    return createNestedArray(0, shape, a);
-  }
-  function noConversionNeeded(a, dtype) {
-    return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
-  }
-  function makeOnesTypedArray(size, dtype) {
-    const array = makeZerosTypedArray(size, dtype);
-    for (let i = 0; i < array.length; i++) {
-      array[i] = 1;
-    }
-    return array;
-  }
-  function makeZerosTypedArray(size, dtype) {
-    if (dtype == null || dtype === "float32" || dtype === "complex64") {
-      return new Float32Array(size);
-    } else if (dtype === "int32") {
-      return new Int32Array(size);
-    } else if (dtype === "bool") {
-      return new Uint8Array(size);
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-  }
-  function makeZerosNestedTypedArray(shape, dtype) {
-    const size = shape.reduce((prev, curr) => prev * curr, 1);
-    if (dtype == null || dtype === "float32") {
-      return toNestedArray(shape, new Float32Array(size));
-    } else if (dtype === "int32") {
-      return toNestedArray(shape, new Int32Array(size));
-    } else if (dtype === "bool") {
-      return toNestedArray(shape, new Uint8Array(size));
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-  }
-  function now() {
-    return env().platform.now();
-  }
-  function assertNonNegativeIntegerDimensions(shape) {
-    shape.forEach((dimSize) => {
-      assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
-    });
-  }
-  function fetch2(path, requestInits) {
-    return env().platform.fetch(path, requestInits);
-  }
-  function encodeString(s, encoding = "utf-8") {
-    encoding = encoding || "utf-8";
-    return env().platform.encode(s, encoding);
-  }
-  function decodeString(bytes, encoding = "utf-8") {
-    encoding = encoding || "utf-8";
-    return env().platform.decode(bytes, encoding);
-  }
-  function locToIndex(locs, rank, strides) {
-    if (rank === 0) {
-      return 0;
-    } else if (rank === 1) {
-      return locs[0];
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += strides[i] * locs[i];
-    }
-    return index;
-  }
-  function indexToLoc(index, rank, strides) {
-    if (rank === 0) {
-      return [];
-    } else if (rank === 1) {
-      return [index];
-    }
-    const locs = new Array(rank);
-    for (let i = 0; i < locs.length - 1; ++i) {
-      locs[i] = Math.floor(index / strides[i]);
-      index -= locs[i] * strides[i];
-    }
-    locs[locs.length - 1] = index;
-    return locs;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/profiler.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class Profiler {
-    constructor(backendTimer, logger) {
-      this.backendTimer = backendTimer;
-      this.logger = logger;
-      if (logger == null) {
-        this.logger = new Logger();
-      }
-    }
-    profileKernel(kernelName, inputs, f) {
-      let outputs;
-      const holdResultWrapperFn = () => {
-        outputs = f();
-      };
-      const timer = this.backendTimer.time(holdResultWrapperFn);
-      for (let i = 0; i < outputs.length; i++) {
-        const output = outputs[i];
-        output.data().then((tensorVals) => {
-          checkComputationForErrors(tensorVals, output.dtype, kernelName);
-        });
-      }
-      const kernelProfile = {
-        kernelName,
-        outputs,
-        inputs,
-        timeMs: timer.then((timing) => timing.kernelMs),
-        extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
-      };
-      return kernelProfile;
-    }
-    logKernelProfile(kernelProfile) {
-      const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
-      outputs.forEach((result) => {
-        Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
-          this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
-        });
-      });
-    }
-  }
-  function checkComputationForErrors(vals, dtype, kernelName) {
-    if (dtype !== "float32") {
-      return false;
-    }
-    for (let i = 0; i < vals.length; i++) {
-      const num = vals[i];
-      if (isNaN(num) || !isFinite(num)) {
-        console.warn(`Found ${num} in the result of '${kernelName}'`);
-        return true;
-      }
-    }
-    return false;
-  }
-  class Logger {
-    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
-      const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
-      const paddedName = rightPad(name, 25);
-      const rank = result.rank;
-      const size = result.size;
-      const shape = rightPad(result.shape.toString(), 14);
-      let inputShapesDescription = "";
-      for (const name2 in inputs) {
-        const input = inputs[name2];
-        if (input != null) {
-          const inputShape = input.shape || result.shape;
-          const inputRank = inputShape.length;
-          inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
-        }
-      }
-      console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/tape.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getFilteredNodesXToY(tape2, xs, y) {
-    const tensorsFromX = {};
-    const nodesFromX = {};
-    for (let i = 0; i < xs.length; i++) {
-      tensorsFromX[xs[i].id] = true;
-    }
-    for (let i = 0; i < tape2.length; i++) {
-      const node = tape2[i];
-      const nodeInputs = node.inputs;
-      for (const inputName in nodeInputs) {
-        const input = nodeInputs[inputName];
-        let anyInputFromX = false;
-        for (let j = 0; j < xs.length; j++) {
-          if (tensorsFromX[input.id]) {
-            node.outputs.forEach((output) => tensorsFromX[output.id] = true);
-            anyInputFromX = true;
-            nodesFromX[node.id] = true;
-            break;
-          }
-        }
-        if (anyInputFromX) {
-          break;
-        }
-      }
-    }
-    const tensorsLeadToY = {};
-    tensorsLeadToY[y.id] = true;
-    const nodesToY = {};
-    for (let i = tape2.length - 1; i >= 0; i--) {
-      const node = tape2[i];
-      const nodeInputs = node.inputs;
-      for (let j = 0; j < node.outputs.length; j++) {
-        if (tensorsLeadToY[node.outputs[j].id]) {
-          for (const inputName in nodeInputs) {
-            tensorsLeadToY[nodeInputs[inputName].id] = true;
-            nodesToY[node.id] = true;
-          }
-          break;
-        }
-      }
-    }
-    const filteredTape = [];
-    for (let i = 0; i < tape2.length; i++) {
-      const node = tape2[i];
-      if (nodesFromX[node.id] && nodesToY[node.id]) {
-        const prunedInputs = {};
-        for (const inputName in node.inputs) {
-          const nodeInput = node.inputs[inputName];
-          if (tensorsFromX[nodeInput.id]) {
-            prunedInputs[inputName] = nodeInput;
-          }
-        }
-        const prunedNode = Object.assign({}, node);
-        prunedNode.inputs = prunedInputs;
-        prunedNode.outputs = node.outputs;
-        filteredTape.push(prunedNode);
-      }
-    }
-    return filteredTape;
-  }
-  function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add29) {
-    for (let i = filteredTape.length - 1; i >= 0; i--) {
-      const node = filteredTape[i];
-      const dys = [];
-      node.outputs.forEach((o) => {
-        const gradTensor = tensorAccumulatedGradientMap[o.id];
-        if (gradTensor != null) {
-          dys.push(gradTensor);
-        } else {
-          dys.push(null);
-        }
-      });
-      if (node.gradient == null) {
-        throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);
-      }
-      const inputGradients = node.gradient(dys);
-      for (const inputName in node.inputs) {
-        if (!(inputName in inputGradients)) {
-          throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
-        }
-        const dx = tidy2(() => inputGradients[inputName]());
-        if (dx.dtype !== "float32") {
-          throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
-        }
-        const x = node.inputs[inputName];
-        if (!arraysEqual(dx.shape, x.shape)) {
-          throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
-        }
-        if (tensorAccumulatedGradientMap[x.id] == null) {
-          tensorAccumulatedGradientMap[x.id] = dx;
-        } else {
-          const curGradient = tensorAccumulatedGradientMap[x.id];
-          tensorAccumulatedGradientMap[x.id] = add29(curGradient, dx);
-          curGradient.dispose();
-        }
-      }
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const FORMAT_LIMIT_NUM_VALS = 20;
-  const FORMAT_NUM_FIRST_LAST_VALS = 3;
-  const FORMAT_NUM_SIG_DIGITS = 7;
-  function tensorToString(vals, shape, dtype, verbose) {
-    const strides = computeStrides(shape);
-    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
-    const rank = shape.length;
-    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
-    const lines = ["Tensor"];
-    if (verbose) {
-      lines.push(`  dtype: ${dtype}`);
-      lines.push(`  rank: ${rank}`);
-      lines.push(`  shape: [${shape}]`);
-      lines.push(`  values:`);
-    }
-    lines.push(valsLines.map((l) => "    " + l).join("\n"));
-    return lines.join("\n");
-  }
-  function computeMaxSizePerColumn(vals, shape, dtype, strides) {
-    const n = sizeFromShape(shape);
-    const numCols = strides[strides.length - 1];
-    const padPerCol = new Array(numCols).fill(0);
-    const rank = shape.length;
-    const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
-    if (rank > 1) {
-      for (let row = 0; row < n / numCols; row++) {
-        const offset = row * numCols;
-        for (let j = 0; j < numCols; j++) {
-          padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
-        }
-      }
-    }
-    return padPerCol;
-  }
-  function valToString(val, pad8, dtype) {
-    let valStr;
-    if (Array.isArray(val)) {
-      valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
-    } else if (isString(val)) {
-      valStr = `'${val}'`;
-    } else if (dtype === "bool") {
-      valStr = boolNumToString(val);
-    } else {
-      valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
-    }
-    return rightPad(valStr, pad8);
-  }
-  function boolNumToString(v) {
-    return v === 0 ? "false" : "true";
-  }
-  function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
-    const storagePerElement = dtype === "complex64" ? 2 : 1;
-    const size = shape[0];
-    const rank = shape.length;
-    if (rank === 0) {
-      if (dtype === "complex64") {
-        const complexTuple = createComplexTuples(vals);
-        return [valToString(complexTuple[0], 0, dtype)];
-      }
-      if (dtype === "bool") {
-        return [boolNumToString(vals[0])];
-      }
-      return [vals[0].toString()];
-    }
-    if (rank === 1) {
-      if (size > FORMAT_LIMIT_NUM_VALS) {
-        const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
-        let firstVals = Array.from(vals.slice(0, firstValsSize));
-        let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
-        if (dtype === "complex64") {
-          firstVals = createComplexTuples(firstVals);
-          lastVals = createComplexTuples(lastVals);
-        }
-        return [
-          "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
-        ];
-      }
-      const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
-      return [
-        "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
-      ];
-    }
-    const subshape = shape.slice(1);
-    const substrides = strides.slice(1);
-    const stride = strides[0] * storagePerElement;
-    const lines = [];
-    if (size > FORMAT_LIMIT_NUM_VALS) {
-      for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
-        const start = i * stride;
-        const end = start + stride;
-        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
-      }
-      lines.push("...");
-      for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
-        const start = i * stride;
-        const end = start + stride;
-        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-      }
-    } else {
-      for (let i = 0; i < size; i++) {
-        const start = i * stride;
-        const end = start + stride;
-        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-      }
-    }
-    const sep = rank === 2 ? "," : "";
-    lines[0] = "[" + lines[0] + sep;
-    for (let i = 1; i < lines.length - 1; i++) {
-      lines[i] = " " + lines[i] + sep;
-    }
-    let newLineSep = ",\n";
-    for (let i = 2; i < rank; i++) {
-      newLineSep += "\n";
-    }
-    lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
-    return lines;
-  }
-  function createComplexTuples(vals) {
-    const complexTuples = [];
-    for (let i = 0; i < vals.length; i += 2) {
-      complexTuples.push([vals[i], vals[i + 1]]);
-    }
-    return complexTuples;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/tensor.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class TensorBuffer {
-    constructor(shape, dtype, values) {
-      this.dtype = dtype;
-      this.shape = shape.slice();
-      this.size = sizeFromShape(shape);
-      if (values != null) {
-        const n = values.length;
-        assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
-      }
-      if (dtype === "complex64") {
-        throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
-      }
-      this.values = values || getArrayFromDType(dtype, this.size);
-      this.strides = computeStrides(shape);
-    }
-    set(value, ...locs) {
-      if (locs.length === 0) {
-        locs = [0];
-      }
-      assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
-      const index = this.locToIndex(locs);
-      this.values[index] = value;
-    }
-    get(...locs) {
-      if (locs.length === 0) {
-        locs = [0];
-      }
-      let i = 0;
-      for (const loc of locs) {
-        if (loc < 0 || loc >= this.shape[i]) {
-          const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
-          throw new Error(msg);
-        }
-        i++;
-      }
-      let index = locs[locs.length - 1];
-      for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-        index += this.strides[i2] * locs[i2];
-      }
-      return this.values[index];
-    }
-    locToIndex(locs) {
-      if (this.rank === 0) {
-        return 0;
-      } else if (this.rank === 1) {
-        return locs[0];
-      }
-      let index = locs[locs.length - 1];
-      for (let i = 0; i < locs.length - 1; ++i) {
-        index += this.strides[i] * locs[i];
-      }
-      return index;
-    }
-    indexToLoc(index) {
-      if (this.rank === 0) {
-        return [];
-      } else if (this.rank === 1) {
-        return [index];
-      }
-      const locs = new Array(this.shape.length);
-      for (let i = 0; i < locs.length - 1; ++i) {
-        locs[i] = Math.floor(index / this.strides[i]);
-        index -= locs[i] * this.strides[i];
-      }
-      locs[locs.length - 1] = index;
-      return locs;
-    }
-    get rank() {
-      return this.shape.length;
-    }
-    toTensor() {
-      return trackerFn().makeTensor(this.values, this.shape, this.dtype);
-    }
-  }
-  let trackerFn = null;
-  let opHandler = null;
-  let deprecationWarningFn = null;
-  function setTensorTracker(fn) {
-    trackerFn = fn;
-  }
-  function setOpHandler(handler) {
-    opHandler = handler;
-  }
-  function setDeprecationWarningFn(fn) {
-    deprecationWarningFn = fn;
-  }
-  class Tensor {
-    constructor(shape, dtype, dataId, id) {
-      this.kept = false;
-      this.isDisposedInternal = false;
-      this.shape = shape.slice();
-      this.dtype = dtype || "float32";
-      this.size = sizeFromShape(shape);
-      this.strides = computeStrides(shape);
-      this.dataId = dataId;
-      this.id = id;
-      this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
-    }
-    get rank() {
-      return this.shape.length;
-    }
-    async buffer() {
-      const vals = await this.data();
-      return opHandler.buffer(this.shape, this.dtype, vals);
-    }
-    bufferSync() {
-      return opHandler.buffer(this.shape, this.dtype, this.dataSync());
-    }
-    async array() {
-      const vals = await this.data();
-      return toNestedArray(this.shape, vals);
-    }
-    arraySync() {
-      return toNestedArray(this.shape, this.dataSync());
-    }
-    async data() {
-      this.throwIfDisposed();
-      const data = trackerFn().read(this.dataId);
-      if (this.dtype === "string") {
-        const bytes = await data;
-        try {
-          return bytes.map((b) => decodeString(b));
-        } catch (_a) {
-          throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-        }
-      }
-      return data;
-    }
-    dataSync() {
-      this.throwIfDisposed();
-      const data = trackerFn().readSync(this.dataId);
-      if (this.dtype === "string") {
-        try {
-          return data.map((b) => decodeString(b));
-        } catch (_a) {
-          throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-        }
-      }
-      return data;
-    }
-    async bytes() {
-      this.throwIfDisposed();
-      const data = await trackerFn().read(this.dataId);
-      if (this.dtype === "string") {
-        return data;
-      } else {
-        return new Uint8Array(data.buffer);
-      }
-    }
-    dispose() {
-      if (this.isDisposed) {
-        return;
-      }
-      trackerFn().disposeTensor(this);
-      this.isDisposedInternal = true;
-    }
-    get isDisposed() {
-      return this.isDisposedInternal;
-    }
-    throwIfDisposed() {
-      if (this.isDisposed) {
-        throw new Error(`Tensor is disposed.`);
-      }
-    }
-    print(verbose = false) {
-      return opHandler.print(this, verbose);
-    }
-    clone() {
-      this.throwIfDisposed();
-      return opHandler.clone(this);
-    }
-    toString(verbose = false) {
-      const vals = this.dataSync();
-      return tensorToString(vals, this.shape, this.dtype, verbose);
-    }
-    cast(dtype) {
-      this.throwIfDisposed();
-      return opHandler.cast(this, dtype);
-    }
-    variable(trainable = true, name, dtype) {
-      this.throwIfDisposed();
-      return trackerFn().makeVariable(this, trainable, name, dtype);
-    }
-  }
-  Object.defineProperty(Tensor, Symbol.hasInstance, {
-    value: (instance) => {
-      return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
-    }
-  });
-  class Variable extends Tensor {
-    constructor(initialValue, trainable, name, tensorId) {
-      super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
-      this.trainable = trainable;
-      this.name = name;
-    }
-    assign(newValue) {
-      if (newValue.dtype !== this.dtype) {
-        throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
-      }
-      if (!arraysEqual(newValue.shape, this.shape)) {
-        throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
-      }
-      trackerFn().disposeTensor(this);
-      this.dataId = newValue.dataId;
-      trackerFn().incRef(this, null);
-    }
-    dispose() {
-      trackerFn().disposeVariable(this);
-      this.isDisposedInternal = true;
-    }
-  }
-  Object.defineProperty(Variable, Symbol.hasInstance, {
-    value: (instance) => {
-      return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
-    }
-  });
-
-  // node_modules/@tensorflow/tfjs-core/dist/types.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  var Rank;
-  (function(Rank2) {
-    Rank2["R0"] = "R0";
-    Rank2["R1"] = "R1";
-    Rank2["R2"] = "R2";
-    Rank2["R3"] = "R3";
-    Rank2["R4"] = "R4";
-    Rank2["R5"] = "R5";
-    Rank2["R6"] = "R6";
-  })(Rank || (Rank = {}));
-  var UpcastInt32AndMap;
-  (function(UpcastInt32AndMap2) {
-    UpcastInt32AndMap2["float32"] = "float32";
-    UpcastInt32AndMap2["int32"] = "int32";
-    UpcastInt32AndMap2["bool"] = "int32";
-    UpcastInt32AndMap2["complex64"] = "complex64";
-  })(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
-  var UpcastBoolAndMap;
-  (function(UpcastBoolAndMap2) {
-    UpcastBoolAndMap2["float32"] = "float32";
-    UpcastBoolAndMap2["int32"] = "int32";
-    UpcastBoolAndMap2["bool"] = "bool";
-    UpcastBoolAndMap2["complex64"] = "complex64";
-  })(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
-  var UpcastFloat32AndMap;
-  (function(UpcastFloat32AndMap2) {
-    UpcastFloat32AndMap2["float32"] = "float32";
-    UpcastFloat32AndMap2["int32"] = "float32";
-    UpcastFloat32AndMap2["bool"] = "float32";
-    UpcastFloat32AndMap2["complex64"] = "complex64";
-  })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
-  var UpcastComplex64AndMap;
-  (function(UpcastComplex64AndMap2) {
-    UpcastComplex64AndMap2["float32"] = "complex64";
-    UpcastComplex64AndMap2["int32"] = "complex64";
-    UpcastComplex64AndMap2["bool"] = "complex64";
-    UpcastComplex64AndMap2["complex64"] = "complex64";
-  })(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
-  const upcastTypeMap = {
-    float32: UpcastFloat32AndMap,
-    int32: UpcastInt32AndMap,
-    bool: UpcastBoolAndMap,
-    complex64: UpcastComplex64AndMap
-  };
-  function upcastType(typeA, typeB) {
-    if (typeA === "string" || typeB === "string") {
-      if (typeA === "string" && typeB === "string") {
-        return "string";
-      }
-      throw new Error(`Can not upcast ${typeA} with ${typeB}`);
-    }
-    return upcastTypeMap[typeA][typeB];
-  }
-  function sumOutType(type) {
-    return upcastType(type, "int32");
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
-  const tensor_util_exports = {};
-  __export(tensor_util_exports, {
-    assertTypesMatch: () => assertTypesMatch,
-    getTensorsInContainer: () => getTensorsInContainer,
-    isTensorInList: () => isTensorInList,
-    makeTypesMatch: () => makeTypesMatch
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function makeTypesMatch(a, b) {
-    if (a.dtype === b.dtype) {
-      return [a, b];
-    }
-    const dtype = upcastType(a.dtype, b.dtype);
-    return [a.cast(dtype), b.cast(dtype)];
-  }
-  function assertTypesMatch(a, b) {
-    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
-  }
-  function isTensorInList(tensor17, tensorList) {
-    return tensorList.some((x) => x.id === tensor17.id);
-  }
-  function getTensorsInContainer(result) {
-    const list = [];
-    const seen = new Set();
-    walkTensorContainer(result, list, seen);
-    return list;
-  }
-  function walkTensorContainer(container, list, seen) {
-    if (container == null) {
-      return;
-    }
-    if (container instanceof Tensor) {
-      list.push(container);
-      return;
-    }
-    if (!isIterable(container)) {
-      return;
-    }
-    const iterable = container;
-    for (const k in iterable) {
-      const val = iterable[k];
-      if (!seen.has(val)) {
-        seen.add(val);
-        walkTensorContainer(val, list, seen);
-      }
-    }
-  }
-  function isIterable(obj) {
-    return Array.isArray(obj) || typeof obj === "object";
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/engine.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class EngineState {
-    constructor() {
-      this.registeredVariables = {};
-      this.nextTapeNodeId = 0;
-      this.numBytes = 0;
-      this.numTensors = 0;
-      this.numStringTensors = 0;
-      this.numDataBuffers = 0;
-      this.gradientDepth = 0;
-      this.kernelDepth = 0;
-      this.scopeStack = [];
-      this.numDataMovesStack = [];
-      this.nextScopeId = 0;
-      this.tensorInfo = new WeakMap();
-      this.profiling = false;
-      this.activeProfile = {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};
-    }
-    dispose() {
-      for (const variableName in this.registeredVariables) {
-        this.registeredVariables[variableName].dispose();
-      }
-    }
-  }
-  class Engine {
-    constructor(ENV3) {
-      this.ENV = ENV3;
-      this.registry = {};
-      this.registryFactory = {};
-      this.pendingBackendInitId = 0;
-      this.state = new EngineState();
-    }
-    async ready() {
-      if (this.pendingBackendInit != null) {
-        return this.pendingBackendInit.then(() => {
-        });
-      }
-      if (this.backendInstance != null) {
-        return;
-      }
-      const sortedBackends = this.getSortedBackends();
-      for (let i = 0; i < sortedBackends.length; i++) {
-        const backendName = sortedBackends[i];
-        const success = await this.initializeBackend(backendName).success;
-        if (success) {
-          await this.setBackend(backendName);
-          return;
-        }
-      }
-      throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-    }
-    get backend() {
-      if (this.pendingBackendInit != null) {
-        throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-      }
-      if (this.backendInstance == null) {
-        const {name, asyncInit} = this.initializeBackendsAndReturnBest();
-        if (asyncInit) {
-          throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-        }
-        this.setBackend(name);
-      }
-      return this.backendInstance;
-    }
-    backendNames() {
-      return Object.keys(this.registryFactory);
-    }
-    findBackend(backendName) {
-      if (!(backendName in this.registry)) {
-        if (backendName in this.registryFactory) {
-          const {asyncInit} = this.initializeBackend(backendName);
-          if (asyncInit) {
-            return null;
-          }
-        } else {
-          return null;
-        }
-      }
-      return this.registry[backendName];
-    }
-    findBackendFactory(backendName) {
-      if (!(backendName in this.registryFactory)) {
-        return null;
-      }
-      return this.registryFactory[backendName].factory;
-    }
-    registerBackend(backendName, factory, priority = 1) {
-      if (backendName in this.registryFactory) {
-        console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
-        return false;
-      }
-      this.registryFactory[backendName] = {factory, priority};
-      return true;
-    }
-    async setBackend(backendName) {
-      if (this.registryFactory[backendName] == null) {
-        throw new Error(`Backend name '${backendName}' not found in registry`);
-      }
-      this.backendName = backendName;
-      if (this.registry[backendName] == null) {
-        this.backendInstance = null;
-        const {success, asyncInit} = this.initializeBackend(backendName);
-        const result = asyncInit ? await success : success;
-        if (!result) {
-          return false;
-        }
-      }
-      this.backendInstance = this.registry[backendName];
-      this.setupRegisteredKernels();
-      this.profiler = new Profiler(this.backendInstance);
-      return true;
-    }
-    setupRegisteredKernels() {
-      const kernels = getKernelsForBackend(this.backendName);
-      kernels.forEach((kernel) => {
-        if (kernel.setupFunc != null) {
-          kernel.setupFunc(this.backendInstance);
-        }
-      });
-    }
-    disposeRegisteredKernels(backendName) {
-      const kernels = getKernelsForBackend(backendName);
-      kernels.forEach((kernel) => {
-        if (kernel.disposeFunc != null) {
-          kernel.disposeFunc(this.registry[backendName]);
-        }
-      });
-    }
-    initializeBackend(backendName) {
-      const registryFactoryEntry = this.registryFactory[backendName];
-      if (registryFactoryEntry == null) {
-        throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
-      }
-      try {
-        const backend2 = registryFactoryEntry.factory();
-        if (Promise.resolve(backend2) === backend2) {
-          const promiseId = ++this.pendingBackendInitId;
-          const success = backend2.then((backendInstance) => {
-            if (promiseId < this.pendingBackendInitId) {
-              return false;
-            }
-            this.registry[backendName] = backendInstance;
-            this.pendingBackendInit = null;
-            return true;
-          }).catch((err) => {
-            if (promiseId < this.pendingBackendInitId) {
-              return false;
-            }
-            this.pendingBackendInit = null;
-            console.warn(`Initialization of backend ${backendName} failed`);
-            console.warn(err.stack || err.message);
-            return false;
-          });
-          this.pendingBackendInit = success;
-          return {success, asyncInit: true};
-        } else {
-          this.registry[backendName] = backend2;
-          return {success: true, asyncInit: false};
-        }
-      } catch (err) {
-        console.warn(`Initialization of backend ${backendName} failed`);
-        console.warn(err.stack || err.message);
-        return {success: false, asyncInit: false};
-      }
-    }
-    removeBackend(backendName) {
-      if (!(backendName in this.registryFactory)) {
-        throw new Error(`${backendName} backend not found in registry`);
-      }
-      if (this.backendName === backendName && this.pendingBackendInit != null) {
-        this.pendingBackendInitId++;
-      }
-      if (backendName in this.registry) {
-        this.disposeRegisteredKernels(backendName);
-        this.registry[backendName].dispose();
-        delete this.registry[backendName];
-      }
-      delete this.registryFactory[backendName];
-      if (this.backendName === backendName) {
-        this.pendingBackendInit = null;
-        this.backendName = null;
-        this.backendInstance = null;
-      }
-    }
-    getSortedBackends() {
-      if (Object.keys(this.registryFactory).length === 0) {
-        throw new Error("No backend found in registry.");
-      }
-      return Object.keys(this.registryFactory).sort((a, b) => {
-        return this.registryFactory[b].priority - this.registryFactory[a].priority;
-      });
-    }
-    initializeBackendsAndReturnBest() {
-      const sortedBackends = this.getSortedBackends();
-      for (let i = 0; i < sortedBackends.length; i++) {
-        const backendName = sortedBackends[i];
-        const {success, asyncInit} = this.initializeBackend(backendName);
-        if (asyncInit || success) {
-          return {name: backendName, asyncInit};
-        }
-      }
-      throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-    }
-    moveData(backend2, dataId) {
-      const info = this.state.tensorInfo.get(dataId);
-      const srcBackend = info.backend;
-      const values = this.readSync(dataId);
-      srcBackend.disposeData(dataId);
-      info.backend = backend2;
-      backend2.move(dataId, values, info.shape, info.dtype);
-      if (this.shouldCheckForMemLeaks()) {
-        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
-      }
-    }
-    tidy(nameOrFn, fn) {
-      let name = null;
-      if (fn == null) {
-        if (typeof nameOrFn !== "function") {
-          throw new Error("Please provide a function to tidy()");
-        }
-        fn = nameOrFn;
-      } else {
-        if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
-          throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
-        }
-        if (typeof fn !== "function") {
-          throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
-        }
-        name = nameOrFn;
-      }
-      let result;
-      return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
-        result = fn();
-        if (result instanceof Promise) {
-          console.error("Cannot return a Promise inside of tidy.");
-        }
-        return result;
-      });
-    }
-    scopedRun(start, end, f) {
-      start();
-      try {
-        const res = f();
-        end();
-        return res;
-      } catch (ex) {
-        end();
-        throw ex;
-      }
-    }
-    nextTensorId() {
-      return Engine.nextTensorId++;
-    }
-    nextVariableId() {
-      return Engine.nextVariableId++;
-    }
-    clone(x) {
-      const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
-      const inputs = {x};
-      const grad2 = (dy) => ({
-        x: () => {
-          const dtype = "float32";
-          const gradInputs = {x: dy};
-          const attrs = {dtype};
-          return ENGINE.runKernelFunc((backend2) => backend2.cast(dy, dtype), gradInputs, null, Cast, attrs);
-        }
-      });
-      const saved = [];
-      this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
-      return y;
-    }
-    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {
-      const forwardFunc = null;
-      const backwardsFunc = null;
-      return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
-    }
-    shouldCheckForMemLeaks() {
-      return this.ENV.getBool("IS_TEST");
-    }
-    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
-      const numDataIdsAfter = this.backend.numDataIds();
-      let numOutputDataIds = 0;
-      outInfos.forEach((info) => {
-        numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
-      });
-      const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
-      const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
-      if (dataIdsLeaked > 0) {
-        throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
-      }
-    }
-    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
-      let outputs;
-      let saved = [];
-      const isTapeOn = this.isTapeOn();
-      if (kernelName == null) {
-        kernelName = this.state.activeScope != null ? this.state.activeScope.name : "";
-      }
-      const startingBytecount = this.state.numBytes;
-      const startingNumTensors = this.state.numTensors;
-      if (this.shouldCheckForMemLeaks()) {
-        this.state.numDataMovesStack.push(0);
-      }
-      let kernelFunc;
-      const kernel = getKernel(kernelName, this.backendName);
-      let out;
-      if (kernel != null) {
-        kernelFunc = () => {
-          const numDataIdsBefore = this.backend.numDataIds();
-          out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
-          const outInfos = Array.isArray(out) ? out : [out];
-          if (this.shouldCheckForMemLeaks()) {
-            this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
-          }
-          const outTensors = outInfos.map(({dataId, shape, dtype}) => this.makeTensorFromDataId(dataId, shape, dtype));
-          if (isTapeOn) {
-            let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
-            if (tensorsToSave == null) {
-              if (outputsToSave == null) {
-                outputsToSave = [];
-              }
-              const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);
-              tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);
-            }
-            saved = this.saveTensorsForBackwardMode(tensorsToSave);
-          }
-          return outTensors;
-        };
-      } else {
-        const saveFunc = (tensors) => {
-          if (!isTapeOn) {
-            return;
-          }
-          saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-        };
-        kernelFunc = () => {
-          const numDataIdsBefore = this.backend.numDataIds();
-          out = this.tidy(() => forwardFunc(this.backend, saveFunc));
-          const outs = Array.isArray(out) ? out : [out];
-          if (this.shouldCheckForMemLeaks()) {
-            this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
-          }
-          return outs;
-        };
-      }
-      let kernelProfile;
-      this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-        if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
-          outputs = kernelFunc();
-        } else {
-          kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());
-          if (this.ENV.getBool("DEBUG")) {
-            this.profiler.logKernelProfile(kernelProfile);
-          }
-          outputs = kernelProfile.outputs;
-        }
-      });
-      if (isTapeOn) {
-        this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);
-      }
-      if (this.state.profiling) {
-        this.state.activeProfile.kernels.push({
-          name: kernelName,
-          bytesAdded: this.state.numBytes - startingBytecount,
-          totalBytesSnapshot: this.state.numBytes,
-          tensorsAdded: this.state.numTensors - startingNumTensors,
-          totalTensorsSnapshot: this.state.numTensors,
-          inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
-          outputShapes: outputs.map((item) => item.shape),
-          kernelTimeMs: kernelProfile.timeMs,
-          extraInfo: kernelProfile.extraInfo
-        });
-      }
-      return Array.isArray(out) ? outputs : outputs[0];
-    }
-    saveTensorsForBackwardMode(tensors) {
-      const saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-      return saved;
-    }
-    getTensorsForGradient(kernelName, inputs, outputs) {
-      const gradConfig = getGradient(kernelName);
-      if (gradConfig != null) {
-        const inputsToSave = gradConfig.inputsToSave || [];
-        const outputsToSave = gradConfig.outputsToSave || [];
-        let inputTensorsToSave;
-        if (gradConfig.saveAllInputs) {
-          assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
-          inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
-        } else {
-          inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
-        }
-        const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
-        return inputTensorsToSave.concat(outputTensorsToSave);
-      }
-      return null;
-    }
-    makeTensor(values, shape, dtype, backend2) {
-      if (values == null) {
-        throw new Error("Values passed to engine.makeTensor() are null");
-      }
-      dtype = dtype || "float32";
-      backend2 = backend2 || this.backend;
-      let backendVals = values;
-      if (dtype === "string" && isString(values[0])) {
-        backendVals = values.map((d) => encodeString(d));
-      }
-      const dataId = backend2.write(backendVals, shape, dtype);
-      const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-      this.incRef(t, backend2);
-      if (dtype === "string") {
-        const info = this.state.tensorInfo.get(dataId);
-        const newBytes = bytesFromStringArray(backendVals);
-        this.state.numBytes += newBytes - info.bytes;
-        info.bytes = newBytes;
-      }
-      return t;
-    }
-    makeTensorFromDataId(dataId, shape, dtype, backend2) {
-      dtype = dtype || "float32";
-      const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-      this.incRef(t, backend2);
-      return t;
-    }
-    makeVariable(initialValue, trainable = true, name, dtype) {
-      name = name || this.nextVariableId().toString();
-      if (dtype != null && dtype !== initialValue.dtype) {
-        initialValue = initialValue.cast(dtype);
-      }
-      const v = new Variable(initialValue, trainable, name, this.nextTensorId());
-      if (this.state.registeredVariables[v.name] != null) {
-        throw new Error(`Variable with name ${v.name} was already registered`);
-      }
-      this.state.registeredVariables[v.name] = v;
-      this.incRef(v, this.backend);
-      return v;
-    }
-    incRef(a, backend2) {
-      const refCount = this.state.tensorInfo.has(a.dataId) ? this.state.tensorInfo.get(a.dataId).refCount : 0;
-      this.state.numTensors++;
-      if (a.dtype === "string") {
-        this.state.numStringTensors++;
-      }
-      if (refCount === 0) {
-        this.state.numDataBuffers++;
-        let bytes = 0;
-        if (a.dtype !== "complex64" && a.dtype !== "string") {
-          bytes = a.size * bytesPerElement(a.dtype);
-        }
-        this.state.tensorInfo.set(a.dataId, {
-          backend: backend2 || this.backend,
-          dtype: a.dtype,
-          shape: a.shape,
-          bytes,
-          refCount: 0
-        });
-        this.state.numBytes += bytes;
-      }
-      this.state.tensorInfo.get(a.dataId).refCount++;
-      if (!(a instanceof Variable)) {
-        this.track(a);
-      }
-    }
-    disposeTensor(a) {
-      if (!this.state.tensorInfo.has(a.dataId)) {
-        return;
-      }
-      this.state.numTensors--;
-      if (a.dtype === "string") {
-        this.state.numStringTensors--;
-      }
-      const info = this.state.tensorInfo.get(a.dataId);
-      const refCount = info.refCount;
-      if (refCount <= 1) {
-        if (a.dtype !== "complex64") {
-          this.state.numBytes -= info.bytes;
-        }
-        this.state.numDataBuffers--;
-        info.backend.disposeData(a.dataId);
-        this.state.tensorInfo.delete(a.dataId);
-      } else {
-        this.state.tensorInfo.get(a.dataId).refCount--;
-      }
-    }
-    disposeVariables() {
-      for (const varName in this.state.registeredVariables) {
-        const v = this.state.registeredVariables[varName];
-        this.disposeVariable(v);
-      }
-    }
-    disposeVariable(v) {
-      this.disposeTensor(v);
-      if (this.state.registeredVariables[v.name] != null) {
-        delete this.state.registeredVariables[v.name];
-      }
-    }
-    memory() {
-      const info = this.backend.memory();
-      info.numTensors = this.state.numTensors;
-      info.numDataBuffers = this.state.numDataBuffers;
-      info.numBytes = this.state.numBytes;
-      if (this.state.numStringTensors > 0) {
-        info.unreliable = true;
-        if (info.reasons == null) {
-          info.reasons = [];
-        }
-        info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
-      }
-      return info;
-    }
-    async profile(query) {
-      this.state.profiling = true;
-      const startBytes = this.state.numBytes;
-      const startNumTensors = this.state.numTensors;
-      this.state.activeProfile.kernels = [];
-      this.state.activeProfile.result = await query();
-      this.state.profiling = false;
-      this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
-      this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
-      this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
-      for (const kernel of this.state.activeProfile.kernels) {
-        kernel.kernelTimeMs = await kernel.kernelTimeMs;
-        kernel.extraInfo = await kernel.extraInfo;
-      }
-      return this.state.activeProfile;
-    }
-    isTapeOn() {
-      return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
-    }
-    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
-      const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
-      const gradConfig = getGradient(kernelName);
-      if (gradConfig != null) {
-        gradientsFunc = gradConfig.gradFunc;
-      }
-      if (gradientsFunc != null) {
-        tapeNode.gradient = (dys) => {
-          dys = dys.map((dy, i) => {
-            if (dy == null) {
-              const output = outputs[i];
-              const vals = makeZerosTypedArray(output.size, output.dtype);
-              return this.makeTensor(vals, output.shape, output.dtype);
-            }
-            return dy;
-          });
-          return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
-        };
-      }
-      this.state.activeTape.push(tapeNode);
-    }
-    keep(result) {
-      result.kept = true;
-      return result;
-    }
-    startTape() {
-      if (this.state.gradientDepth === 0) {
-        this.state.activeTape = [];
-      }
-      this.state.gradientDepth++;
-    }
-    endTape() {
-      this.state.gradientDepth--;
-    }
-    startScope(name) {
-      const scopeInfo = {
-        track: [],
-        name: "unnamed scope",
-        id: this.state.nextScopeId++
-      };
-      if (name) {
-        scopeInfo.name = name;
-      }
-      this.state.scopeStack.push(scopeInfo);
-      this.state.activeScope = scopeInfo;
-    }
-    endScope(result) {
-      const tensorsToTrackInParent = getTensorsInContainer(result);
-      const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
-      for (let i = 0; i < this.state.activeScope.track.length; i++) {
-        const tensor17 = this.state.activeScope.track[i];
-        if (!tensor17.kept && !tensorsToTrackInParentSet.has(tensor17.id)) {
-          tensor17.dispose();
-        }
-      }
-      const oldScope = this.state.scopeStack.pop();
-      this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
-      tensorsToTrackInParent.forEach((tensor17) => {
-        if (!tensor17.kept && tensor17.scopeId === oldScope.id) {
-          this.track(tensor17);
-        }
-      });
-    }
-    gradients(f, xs, dy, allowNoGradients = false) {
-      assert(xs.length > 0, () => "gradients() received an empty list of xs.");
-      if (dy != null && dy.dtype !== "float32") {
-        throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
-      }
-      const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
-      assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
-      const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
-      if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
-        throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
-      }
-      return this.tidy("backward", () => {
-        const accumulatedGradientMap = {};
-        accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
-        backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
-        const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
-        if (this.state.gradientDepth === 0) {
-          this.state.activeTape.forEach((node) => {
-            for (const tensor17 of node.saved) {
-              tensor17.dispose();
-            }
-          });
-          this.state.activeTape = null;
-        }
-        return {value: y, grads: grads2};
-      });
-    }
-    customGrad(f) {
-      assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
-      return (...inputs) => {
-        assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
-        let res;
-        const inputMap = {};
-        inputs.forEach((input, i) => {
-          inputMap[i] = input;
-        });
-        return this.runKernelFunc((_, save) => {
-          res = f(...[...inputs, save]);
-          assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
-          assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
-          return res.value;
-        }, inputMap, (dy, saved) => {
-          const gradRes = res.gradFunc(dy, saved);
-          const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
-          assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
-          assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
-          const gradMap = {};
-          grads2.forEach((grad2, i) => {
-            gradMap[i] = () => grad2;
-          });
-          return gradMap;
-        });
-      };
-    }
-    readSync(dataId) {
-      const info = this.state.tensorInfo.get(dataId);
-      return info.backend.readSync(dataId);
-    }
-    read(dataId) {
-      const info = this.state.tensorInfo.get(dataId);
-      return info.backend.read(dataId);
-    }
-    async time(query) {
-      const start = now();
-      const timingInfo = await this.backend.time(query);
-      timingInfo.wallMs = now() - start;
-      return timingInfo;
-    }
-    track(result) {
-      if (this.state.activeScope != null) {
-        result.scopeId = this.state.activeScope.id;
-        this.state.activeScope.track.push(result);
-      }
-      return result;
-    }
-    get registeredVariables() {
-      return this.state.registeredVariables;
-    }
-    reset() {
-      this.pendingBackendInitId++;
-      this.state.dispose();
-      this.ENV.reset();
-      this.state = new EngineState();
-      for (const backendName in this.registry) {
-        this.disposeRegisteredKernels(backendName);
-        this.registry[backendName].dispose();
-        delete this.registry[backendName];
-      }
-      this.backendName = null;
-      this.backendInstance = null;
-      this.pendingBackendInit = null;
-    }
-  }
-  Engine.nextTensorId = 0;
-  Engine.nextVariableId = 0;
-  function ones(shape) {
-    const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
-    return ENGINE.makeTensor(values, shape, "float32");
-  }
-  function getOrMakeEngine() {
-    const ns = getGlobalNamespace();
-    if (ns._tfengine == null) {
-      const environment12 = new Environment(ns);
-      ns._tfengine = new Engine(environment12);
-    }
-    setEnvironmentGlobal(ns._tfengine.ENV);
-    setTensorTracker(() => ns._tfengine);
-    return ns._tfengine;
-  }
-  const ENGINE = getOrMakeEngine();
-  function add(a, b) {
-    const inputs = {a, b};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.add(a, b);
-      save([a, b]);
-      return res;
-    }, inputs, null, Add);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/device_util.js
-  const device_util_exports = {};
-  __export(device_util_exports, {
-    isBrowser: () => isBrowser,
-    isMobile: () => isMobile
-  });
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function _isNavigatorDefined() {
-    return typeof navigator !== "undefined" && navigator != null;
-  }
-  function isMobile() {
-    if (_isNavigatorDefined()) {
-      const a = navigator.userAgent || navigator.vendor || window.opera;
-      return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
-    }
-    return false;
-  }
-  function isBrowser() {
-    return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/flags.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const ENV2 = env();
-  ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
-    if (debugValue) {
-      console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
-    }
-  });
-  ENV2.registerFlag("IS_BROWSER", () => isBrowser());
-  ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
-  ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-  ENV2.registerFlag("PROD", () => false);
-  ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
-  ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-  ENV2.registerFlag("IS_TEST", () => false);
-
-  // node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function inferShape(val, dtype) {
-    let firstElem = val;
-    if (isTypedArray(val)) {
-      return dtype === "string" ? [] : [val.length];
-    }
-    if (!Array.isArray(val)) {
-      return [];
-    }
-    const shape = [];
-    while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
-      shape.push(firstElem.length);
-      firstElem = firstElem[0];
-    }
-    if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
-      deepAssertShapeConsistency(val, shape, []);
-    }
-    return shape;
-  }
-  function deepAssertShapeConsistency(val, shape, indices) {
-    indices = indices || [];
-    if (!Array.isArray(val) && !isTypedArray(val)) {
-      assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
-      return;
-    }
-    assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
-    assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
-    const subShape = shape.slice(1);
-    for (let i = 0; i < val.length; ++i) {
-      deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
-    }
-  }
-  function assertDtype(expectedDtype, actualDType, argName, functionName) {
-    if (expectedDtype == null) {
-      return;
-    }
-    if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
-      throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
-    }
-  }
-  function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
-    if (x instanceof Tensor) {
-      assertDtype(parseAsDtype, x.dtype, argName, functionName);
-      return x;
-    }
-    let inferredDtype = inferDtype(x);
-    if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
-      inferredDtype = parseAsDtype;
-    }
-    assertDtype(parseAsDtype, inferredDtype, argName, functionName);
-    if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
-      const type = x == null ? "null" : x.constructor.name;
-      throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
-    }
-    const inferredShape = inferShape(x, inferredDtype);
-    if (!isTypedArray(x) && !Array.isArray(x)) {
-      x = [x];
-    }
-    const skipTypedArray = true;
-    const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
-    return ENGINE.makeTensor(values, inferredShape, inferredDtype);
-  }
-  function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
-    if (!Array.isArray(arg)) {
-      throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
-    }
-    const tensors = arg;
-    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const OP_SCOPE_SUFFIX = "__op";
-  function op(f) {
-    const keys = Object.keys(f);
-    if (keys.length !== 1) {
-      throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
-    }
-    let opName = keys[0];
-    const fn = f[opName];
-    if (opName.endsWith("_")) {
-      opName = opName.substring(0, opName.length - 1);
-    }
-    opName = opName + OP_SCOPE_SUFFIX;
-    const f2 = (...args) => {
-      ENGINE.startScope(opName);
-      try {
-        const result = fn(...args);
-        if (result instanceof Promise) {
-          console.error("Cannot return a Promise inside of tidy.");
-        }
-        ENGINE.endScope(result);
-        return result;
-      } catch (ex) {
-        ENGINE.endScope(null);
-        throw ex;
-      }
-    };
-    Object.defineProperty(f2, "name", {value: opName, configurable: true});
-    return f2;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function complex_(real6, imag6) {
-    const $real = convertToTensor(real6, "real", "complex");
-    const $imag = convertToTensor(imag6, "imag", "complex");
-    assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
-    const forward = (backend2) => {
-      return backend2.complex($real, $imag);
-    };
-    const inputs = {real: $real, imag: $imag};
-    return ENGINE.runKernelFunc(forward, inputs, null, Complex);
-  }
-  const complex = op({complex_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function makeTensor(values, shape, inferredShape, dtype) {
-    if (dtype == null) {
-      dtype = inferDtype(values);
-    }
-    if (dtype === "complex64") {
-      throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
-    }
-    if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
-      throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
-    }
-    if (shape != null) {
-      assertNonNegativeIntegerDimensions(shape);
-      const providedSize = sizeFromShape(shape);
-      const inferredSize = sizeFromShape(inferredShape);
-      assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-      for (let i = 0; i < inferredShape.length; ++i) {
-        const inferred = inferredShape[i];
-        const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
-        assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
-      }
-    }
-    if (!isTypedArray(values) && !Array.isArray(values)) {
-      values = [values];
-    }
-    shape = shape || inferredShape;
-    values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
-    return ENGINE.makeTensor(values, shape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor4(values, shape, dtype) {
-    const inferredShape = inferShape(values, dtype);
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/types.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const DTYPE_VALUE_SIZE_MAP = {
-    float32: 4,
-    float16: 2,
-    int32: 4,
-    uint16: 2,
-    uint8: 1,
-    bool: 1,
-    complex64: 8
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const NUM_BYTES_STRING_LENGTH = 4;
-  async function encodeWeights(tensors, group) {
-    const specs = [];
-    const dataPromises = [];
-    const names = Array.isArray(tensors) ? tensors.map((tensor17) => tensor17.name) : Object.keys(tensors);
-    for (let i = 0; i < names.length; ++i) {
-      const name = names[i];
-      const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
-      if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
-        throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
-      }
-      const spec = {name, shape: t.shape, dtype: t.dtype};
-      if (t.dtype === "string") {
-        const utf8bytes = new Promise(async (resolve) => {
-          const vals = await t.bytes();
-          const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
-          const bytes = new Uint8Array(totalNumBytes);
-          let offset = 0;
-          for (let i2 = 0; i2 < vals.length; i2++) {
-            const val = vals[i2];
-            const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
-            bytes.set(bytesOfLength, offset);
-            offset += NUM_BYTES_STRING_LENGTH;
-            bytes.set(val, offset);
-            offset += val.length;
-          }
-          resolve(bytes);
-        });
-        dataPromises.push(utf8bytes);
-      } else {
-        dataPromises.push(t.data());
-      }
-      if (group != null) {
-        spec.group = group;
-      }
-      specs.push(spec);
-    }
-    const tensorValues = await Promise.all(dataPromises);
-    return {data: concatenateTypedArrays(tensorValues), specs};
-  }
-  function decodeWeights(buffer11, specs) {
-    const out = {};
-    let float16Decode;
-    let offset = 0;
-    for (const spec of specs) {
-      const name = spec.name;
-      const dtype = spec.dtype;
-      const shape = spec.shape;
-      const size = sizeFromShape(shape);
-      let values;
-      if ("quantization" in spec) {
-        const quantization = spec.quantization;
-        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-          if (!("min" in quantization && "scale" in quantization)) {
-            throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
-          }
-        } else if (quantization.dtype === "float16") {
-          if (dtype !== "float32") {
-            throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
-          }
-        } else {
-          throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-        }
-        const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
-        const byteBuffer = buffer11.slice(offset, offset + size * quantizationSizeFactor);
-        const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
-        if (dtype === "float32") {
-          if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-            values = new Float32Array(quantizedArray.length);
-            for (let i = 0; i < quantizedArray.length; i++) {
-              const v = quantizedArray[i];
-              values[i] = v * quantization.scale + quantization.min;
-            }
-          } else if (quantization.dtype === "float16") {
-            if (float16Decode === void 0) {
-              float16Decode = getFloat16Decoder();
-            }
-            values = float16Decode(quantizedArray);
-          } else {
-            throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
-          }
-        } else if (dtype === "int32") {
-          if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
-            throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
-          }
-          values = new Int32Array(quantizedArray.length);
-          for (let i = 0; i < quantizedArray.length; i++) {
-            const v = quantizedArray[i];
-            values[i] = Math.round(v * quantization.scale + quantization.min);
-          }
-        } else {
-          throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-        }
-        offset += size * quantizationSizeFactor;
-      } else if (dtype === "string") {
-        const size2 = sizeFromShape(spec.shape);
-        values = [];
-        for (let i = 0; i < size2; i++) {
-          const byteLength = new Uint32Array(buffer11.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
-          offset += NUM_BYTES_STRING_LENGTH;
-          const bytes = new Uint8Array(buffer11.slice(offset, offset + byteLength));
-          values.push(bytes);
-          offset += byteLength;
-        }
-      } else {
-        const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
-        const byteBuffer = buffer11.slice(offset, offset + size * dtypeFactor);
-        if (dtype === "float32") {
-          values = new Float32Array(byteBuffer);
-        } else if (dtype === "int32") {
-          values = new Int32Array(byteBuffer);
-        } else if (dtype === "bool") {
-          values = new Uint8Array(byteBuffer);
-        } else if (dtype === "complex64") {
-          values = new Float32Array(byteBuffer);
-          const real6 = new Float32Array(values.length / 2);
-          const image2 = new Float32Array(values.length / 2);
-          for (let i = 0; i < real6.length; i++) {
-            real6[i] = values[i * 2];
-            image2[i] = values[i * 2 + 1];
-          }
-          const realTensor = tensor4(real6, shape, "float32");
-          const imageTensor = tensor4(image2, shape, "float32");
-          out[name] = complex(realTensor, imageTensor);
-        } else {
-          throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-        }
-        offset += size * dtypeFactor;
-      }
-      if (dtype !== "complex64") {
-        out[name] = tensor4(values, shape, dtype);
-      }
-    }
-    return out;
-  }
-  function concatenateTypedArrays(xs) {
-    if (xs === null) {
-      throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
-    }
-    let totalByteLength = 0;
-    const normalizedXs = [];
-    xs.forEach((x) => {
-      totalByteLength += x.byteLength;
-      normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
-      if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
-        throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
-      }
-    });
-    const y = new Uint8Array(totalByteLength);
-    let offset = 0;
-    normalizedXs.forEach((x) => {
-      y.set(new Uint8Array(x.buffer), offset);
-      offset += x.byteLength;
-    });
-    return y.buffer;
-  }
-  const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
-  function stringByteLength(str) {
-    if (useNodeBuffer) {
-      return Buffer.byteLength(str);
-    }
-    return new Blob([str]).size;
-  }
-  function concatenateArrayBuffers(buffers) {
-    if (buffers.length === 1) {
-      return buffers[0];
-    }
-    let totalByteLength = 0;
-    buffers.forEach((buffer11) => {
-      totalByteLength += buffer11.byteLength;
-    });
-    const temp = new Uint8Array(totalByteLength);
-    let offset = 0;
-    buffers.forEach((buffer11) => {
-      temp.set(new Uint8Array(buffer11), offset);
-      offset += buffer11.byteLength;
-    });
-    return temp.buffer;
-  }
-  function basename(path) {
-    const SEPARATOR = "/";
-    path = path.trim();
-    while (path.endsWith(SEPARATOR)) {
-      path = path.slice(0, path.length - 1);
-    }
-    const items = path.split(SEPARATOR);
-    return items[items.length - 1];
-  }
-  function getModelArtifactsInfoForJSON(modelArtifacts) {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("Expected JSON model topology, received ArrayBuffer.");
-    }
-    return {
-      dateSaved: new Date(),
-      modelTopologyType: "JSON",
-      modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
-      weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
-      weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
-    };
-  }
-  function computeFloat16MantisaTable() {
-    const convertMantissa = (i) => {
-      let m = i << 13;
-      let e = 0;
-      while ((m & 8388608) === 0) {
-        e -= 8388608;
-        m <<= 1;
-      }
-      m &= ~8388608;
-      e += 947912704;
-      return m | e;
-    };
-    const mantisaTable = new Uint32Array(2048);
-    mantisaTable[0] = 0;
-    for (let i = 1; i < 1024; i++) {
-      mantisaTable[i] = convertMantissa(i);
-    }
-    for (let i = 1024; i < 2048; i++) {
-      mantisaTable[i] = 939524096 + (i - 1024 << 13);
-    }
-    return mantisaTable;
-  }
-  function computeFloat16ExponentTable() {
-    const exponentTable = new Uint32Array(64);
-    exponentTable[0] = 0;
-    exponentTable[31] = 1199570944;
-    exponentTable[32] = 2147483648;
-    exponentTable[63] = 3347054592;
-    for (let i = 1; i < 31; i++) {
-      exponentTable[i] = i << 23;
-    }
-    for (let i = 33; i < 63; i++) {
-      exponentTable[i] = 2147483648 + (i - 32 << 23);
-    }
-    return exponentTable;
-  }
-  function computeFloat16OffsetTable() {
-    const offsetTable = new Uint32Array(64);
-    for (let i = 0; i < 64; i++) {
-      offsetTable[i] = 1024;
-    }
-    offsetTable[0] = offsetTable[32] = 0;
-    return offsetTable;
-  }
-  function getFloat16Decoder() {
-    const mantisaTable = computeFloat16MantisaTable();
-    const exponentTable = computeFloat16ExponentTable();
-    const offsetTable = computeFloat16OffsetTable();
-    return (quantizedArray) => {
-      const buffer11 = new ArrayBuffer(4 * quantizedArray.length);
-      const bufferUint32View = new Uint32Array(buffer11);
-      for (let index = 0; index < quantizedArray.length; index++) {
-        const float16Bits = quantizedArray[index];
-        const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
-        bufferUint32View[index] = float32Bits;
-      }
-      return new Float32Array(buffer11);
-    };
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class IORouterRegistry {
-    constructor() {
-      this.saveRouters = [];
-      this.loadRouters = [];
-    }
-    static getInstance() {
-      if (IORouterRegistry.instance == null) {
-        IORouterRegistry.instance = new IORouterRegistry();
-      }
-      return IORouterRegistry.instance;
-    }
-    static registerSaveRouter(saveRouter) {
-      IORouterRegistry.getInstance().saveRouters.push(saveRouter);
-    }
-    static registerLoadRouter(loadRouter) {
-      IORouterRegistry.getInstance().loadRouters.push(loadRouter);
-    }
-    static getSaveHandlers(url) {
-      return IORouterRegistry.getHandlers(url, "save");
-    }
-    static getLoadHandlers(url, loadOptions) {
-      return IORouterRegistry.getHandlers(url, "load", loadOptions);
-    }
-    static getHandlers(url, handlerType, loadOptions) {
-      const validHandlers = [];
-      const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
-      routers.forEach((router) => {
-        const handler = router(url, loadOptions);
-        if (handler !== null) {
-          validHandlers.push(handler);
-        }
-      });
-      return validHandlers;
-    }
-  }
-  const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
-  const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
-  const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
-  const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const URL_SCHEME_SUFFIX = "://";
-  class ModelStoreManagerRegistry {
-    constructor() {
-      this.managers = {};
-    }
-    static getInstance() {
-      if (ModelStoreManagerRegistry.instance == null) {
-        ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
-      }
-      return ModelStoreManagerRegistry.instance;
-    }
-    static registerManager(scheme, manager) {
-      assert(scheme != null, () => "scheme must not be undefined or null.");
-      if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
-        scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
-      }
-      assert(scheme.length > 0, () => "scheme must not be an empty string.");
-      const registry = ModelStoreManagerRegistry.getInstance();
-      assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
-      registry.managers[scheme] = manager;
-    }
-    static getManager(scheme) {
-      const manager = this.getInstance().managers[scheme];
-      if (manager == null) {
-        throw new Error(`Cannot find model manager for scheme '${scheme}'`);
-      }
-      return manager;
-    }
-    static getSchemes() {
-      return Object.keys(this.getInstance().managers);
-    }
-  }
-  function parseURL(url) {
-    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
-      throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
-    }
-    return {
-      scheme: url.split(URL_SCHEME_SUFFIX)[0],
-      path: url.split(URL_SCHEME_SUFFIX)[1]
-    };
-  }
-  async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
-    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
-    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
-    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
-    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
-    const loadHandler = loadHandlers[0];
-    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
-    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
-    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
-    const saveHandler = saveHandlers[0];
-    const sourceScheme = parseURL(sourceURL).scheme;
-    const sourcePath = parseURL(sourceURL).path;
-    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
-    const modelArtifacts = await loadHandler.load();
-    if (deleteSource && sameMedium) {
-      await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-    }
-    const saveResult = await saveHandler.save(modelArtifacts);
-    if (deleteSource && !sameMedium) {
-      await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-    }
-    return saveResult.modelArtifactsInfo;
-  }
-  async function listModels() {
-    const schemes = ModelStoreManagerRegistry.getSchemes();
-    const out = {};
-    for (const scheme of schemes) {
-      const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
-      for (const path in schemeOut) {
-        const url = scheme + URL_SCHEME_SUFFIX + path;
-        out[url] = schemeOut[path];
-      }
-    }
-    return out;
-  }
-  async function removeModel(url) {
-    const schemeAndPath = parseURL(url);
-    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
-    return manager.removeModel(schemeAndPath.path);
-  }
-  async function copyModel(sourceURL, destURL) {
-    const deleteSource = false;
-    return cloneModelInternal(sourceURL, destURL, deleteSource);
-  }
-  async function moveModel(sourceURL, destURL) {
-    const deleteSource = true;
-    return cloneModelInternal(sourceURL, destURL, deleteSource);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function buffer(shape, dtype = "float32", values) {
-    dtype = dtype || "float32";
-    assertNonNegativeIntegerDimensions(shape);
-    return new TensorBuffer(shape, dtype, values);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function cast_(x, dtype) {
-    const $x = convertToTensor(x, "x", "cast");
-    if (!isValidDtype(dtype)) {
-      throw new Error(`Failed to cast to unknown dtype ${dtype}`);
-    }
-    if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
-      throw new Error("Only strings can be casted to strings");
-    }
-    const inputs = {x: $x};
-    const attrs = {dtype};
-    return ENGINE.runKernelFunc((backend2) => backend2.cast($x, dtype), inputs, null, Cast, attrs);
-  }
-  const cast = op({cast_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function clone_(x) {
-    const $x = convertToTensor(x, "x", "clone", null);
-    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Identity);
-  }
-  const clone = op({clone_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/print.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function print(x, verbose = false) {
-    console.log(x.toString(verbose));
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  getOrMakeEngine();
-  const opHandler2 = {
-    buffer,
-    cast,
-    clone,
-    print
-  };
-  setOpHandler(opHandler2);
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const DEFAULT_FILE_NAME_PREFIX = "model";
-  const DEFAULT_JSON_EXTENSION_NAME = ".json";
-  const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
-  function defer(f) {
-    return new Promise((resolve) => setTimeout(resolve)).then(f);
-  }
-  class BrowserDownloads {
-    constructor(fileNamePrefix) {
-      if (!env().getBool("IS_BROWSER")) {
-        throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-      }
-      if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
-        fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
-      }
-      if (fileNamePrefix == null || fileNamePrefix.length === 0) {
-        fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
-      }
-      this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
-      this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
-    }
-    async save(modelArtifacts) {
-      if (typeof document === "undefined") {
-        throw new Error("Browser downloads are not supported in this environment since `document` is not present");
-      }
-      const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
-      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-        throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
-      } else {
-        const weightsManifest = [{
-          paths: ["./" + this.weightDataFileName],
-          weights: modelArtifacts.weightSpecs
-        }];
-        const modelTopologyAndWeightManifest = {
-          modelTopology: modelArtifacts.modelTopology,
-          format: modelArtifacts.format,
-          generatedBy: modelArtifacts.generatedBy,
-          convertedBy: modelArtifacts.convertedBy,
-          weightsManifest
-        };
-        const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
-        const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
-        jsonAnchor.download = this.modelTopologyFileName;
-        jsonAnchor.href = modelTopologyAndWeightManifestURL;
-        await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
-        if (modelArtifacts.weightData != null) {
-          const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-          weightDataAnchor.download = this.weightDataFileName;
-          weightDataAnchor.href = weightsURL;
-          await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
-        }
-        return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
-      }
-    }
-  }
-  BrowserDownloads.URL_SCHEME = "downloads://";
-  class BrowserFiles {
-    constructor(files) {
-      if (files == null || files.length < 1) {
-        throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
-      }
-      this.files = files;
-    }
-    async load() {
-      const jsonFile = this.files[0];
-      const weightFiles = this.files.slice(1);
-      return new Promise((resolve, reject) => {
-        const jsonReader = new FileReader();
-        jsonReader.onload = (event) => {
-          const modelJSON = JSON.parse(event.target.result);
-          const modelTopology = modelJSON.modelTopology;
-          if (modelTopology == null) {
-            reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
-            return;
-          }
-          if (weightFiles.length === 0) {
-            resolve({modelTopology});
-          }
-          const weightsManifest = modelJSON.weightsManifest;
-          if (weightsManifest == null) {
-            reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
-            return;
-          }
-          let pathToFile;
-          try {
-            pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
-          } catch (err) {
-            reject(err);
-            return;
-          }
-          const weightSpecs = [];
-          const paths = [];
-          const perFileBuffers = [];
-          weightsManifest.forEach((weightsGroup) => {
-            weightsGroup.paths.forEach((path) => {
-              paths.push(path);
-              perFileBuffers.push(null);
-            });
-            weightSpecs.push(...weightsGroup.weights);
-          });
-          weightsManifest.forEach((weightsGroup) => {
-            weightsGroup.paths.forEach((path) => {
-              const weightFileReader = new FileReader();
-              weightFileReader.onload = (event2) => {
-                const weightData = event2.target.result;
-                const index = paths.indexOf(path);
-                perFileBuffers[index] = weightData;
-                if (perFileBuffers.indexOf(null) === -1) {
-                  resolve({
-                    modelTopology,
-                    weightSpecs,
-                    weightData: concatenateArrayBuffers(perFileBuffers),
-                    format: modelJSON.format,
-                    generatedBy: modelJSON.generatedBy,
-                    convertedBy: modelJSON.convertedBy,
-                    userDefinedMetadata: modelJSON.userDefinedMetadata
-                  });
-                }
-              };
-              weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
-              weightFileReader.readAsArrayBuffer(pathToFile[path]);
-            });
-          });
-        };
-        jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
-        jsonReader.readAsText(jsonFile);
-      });
-    }
-    checkManifestAndWeightFiles(manifest, files) {
-      const basenames = [];
-      const fileNames = files.map((file) => basename(file.name));
-      const pathToFile = {};
-      for (const group of manifest) {
-        group.paths.forEach((path) => {
-          const pathBasename = basename(path);
-          if (basenames.indexOf(pathBasename) !== -1) {
-            throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
-          }
-          basenames.push(pathBasename);
-          if (fileNames.indexOf(pathBasename) === -1) {
-            throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
-          } else {
-            pathToFile[path] = files[fileNames.indexOf(pathBasename)];
-          }
-        });
-      }
-      if (basenames.length !== files.length) {
-        throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
-      }
-      return pathToFile;
-    }
-  }
-  const browserDownloadsRouter = (url) => {
-    if (!env().getBool("IS_BROWSER")) {
-      return null;
-    } else {
-      if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
-        return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
-      } else {
-        return null;
-      }
-    }
-  };
-  IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
-  function browserDownloads(fileNamePrefix = "model") {
-    return new BrowserDownloads(fileNamePrefix);
-  }
-  function browserFiles(files) {
-    return new BrowserFiles(files);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/progress.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
-    checkPromises(promises);
-    startFraction = startFraction == null ? 0 : startFraction;
-    endFraction = endFraction == null ? 1 : endFraction;
-    checkFraction(startFraction, endFraction);
-    let resolvedPromise = 0;
-    const registerMonitor = (promise) => {
-      promise.then((value) => {
-        const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
-        onProgress(fraction);
-        return value;
-      });
-      return promise;
-    };
-    function checkPromises(promises2) {
-      assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
-    }
-    function checkFraction(startFraction2, endFraction2) {
-      assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
-      assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
-      assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
-    }
-    return Promise.all(promises.map(registerMonitor));
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
-    if (loadOptions == null) {
-      loadOptions = {};
-    }
-    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
-    const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
-    const fetchStartFraction = 0;
-    const fetchEndFraction = 0.5;
-    const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
-    const bufferPromises = responses.map((response) => response.arrayBuffer());
-    const bufferStartFraction = 0.5;
-    const bufferEndFraction = 1;
-    const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
-    return buffers;
-  }
-  async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
-    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
-    const loadWeights2 = weightsLoaderFactory(fetchWeights);
-    return loadWeights2(manifest, filePathPrefix, weightNames);
-  }
-  function weightsLoaderFactory(fetchWeightsFunction) {
-    return async (manifest, filePathPrefix = "", weightNames) => {
-      const groupIndicesToFetchMap = manifest.map(() => false);
-      const groupWeightsToFetch = {};
-      const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
-      const allManifestWeightNames = [];
-      manifest.forEach((manifestGroupConfig, groupIndex) => {
-        let groupOffset = 0;
-        manifestGroupConfig.weights.forEach((weightsEntry) => {
-          const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
-          const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
-          const enqueueWeightsForFetchingFn = () => {
-            groupIndicesToFetchMap[groupIndex] = true;
-            if (groupWeightsToFetch[groupIndex] == null) {
-              groupWeightsToFetch[groupIndex] = [];
-            }
-            groupWeightsToFetch[groupIndex].push({
-              manifestEntry: weightsEntry,
-              groupOffset,
-              sizeBytes: weightsBytes
-            });
-          };
-          if (weightNames != null) {
-            weightNames.forEach((weightName, weightIndex) => {
-              if (weightName === weightsEntry.name) {
-                enqueueWeightsForFetchingFn();
-                weightsFound[weightIndex] = true;
-              }
-            });
-          } else {
-            enqueueWeightsForFetchingFn();
-          }
-          allManifestWeightNames.push(weightsEntry.name);
-          groupOffset += weightsBytes;
-        });
-      });
-      if (!weightsFound.every((found) => found)) {
-        const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
-        throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
-      }
-      const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
-        if (shouldFetch) {
-          accumulator.push(i);
-        }
-        return accumulator;
-      }, []);
-      const fetchUrls = [];
-      groupIndicesToFetch.forEach((i) => {
-        manifest[i].paths.forEach((filepath) => {
-          const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
-          fetchUrls.push(fetchUrl);
-        });
-      });
-      const buffers = await fetchWeightsFunction(fetchUrls);
-      const weightsTensorMap = {};
-      let bufferIndexOffset = 0;
-      groupIndicesToFetch.forEach((i) => {
-        const numBuffers = manifest[i].paths.length;
-        let groupBytes = 0;
-        for (let i2 = 0; i2 < numBuffers; i2++) {
-          groupBytes += buffers[bufferIndexOffset + i2].byteLength;
-        }
-        const groupBuffer = new ArrayBuffer(groupBytes);
-        const groupByteBuffer = new Uint8Array(groupBuffer);
-        let groupBufferOffset = 0;
-        for (let i2 = 0; i2 < numBuffers; i2++) {
-          const buffer11 = new Uint8Array(buffers[bufferIndexOffset + i2]);
-          groupByteBuffer.set(buffer11, groupBufferOffset);
-          groupBufferOffset += buffer11.byteLength;
-        }
-        const weightsEntries = groupWeightsToFetch[i];
-        weightsEntries.forEach((weightsEntry) => {
-          const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
-          const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
-          for (const name in nameToTensorMap) {
-            weightsTensorMap[name] = nameToTensorMap[name];
-          }
-        });
-        bufferIndexOffset += numBuffers;
-      });
-      return weightsTensorMap;
-    };
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/http.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
-  const JSON_TYPE = "application/json";
-  class HTTPRequest {
-    constructor(path, loadOptions) {
-      this.DEFAULT_METHOD = "POST";
-      if (loadOptions == null) {
-        loadOptions = {};
-      }
-      this.weightPathPrefix = loadOptions.weightPathPrefix;
-      this.onProgress = loadOptions.onProgress;
-      this.weightUrlConverter = loadOptions.weightUrlConverter;
-      if (loadOptions.fetchFunc != null) {
-        assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
-        this.fetch = loadOptions.fetchFunc;
-      } else {
-        this.fetch = env().platform.fetch;
-      }
-      assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
-      if (Array.isArray(path)) {
-        assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
-      }
-      this.path = path;
-      if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
-        throw new Error("requestInit is expected to have no pre-existing body, but has one.");
-      }
-      this.requestInit = loadOptions.requestInit || {};
-    }
-    async save(modelArtifacts) {
-      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-        throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
-      }
-      const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
-      init.body = new FormData();
-      const weightsManifest = [{
-        paths: ["./model.weights.bin"],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelTopologyAndWeightManifest = {
-        modelTopology: modelArtifacts.modelTopology,
-        format: modelArtifacts.format,
-        generatedBy: modelArtifacts.generatedBy,
-        convertedBy: modelArtifacts.convertedBy,
-        userDefinedMetadata: modelArtifacts.userDefinedMetadata,
-        weightsManifest
-      };
-      init.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
-      if (modelArtifacts.weightData != null) {
-        init.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
-      }
-      const response = await this.fetch(this.path, init);
-      if (response.ok) {
-        return {
-          modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
-          responses: [response]
-        };
-      } else {
-        throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
-      }
-    }
-    async load() {
-      const modelConfigRequest = await this.fetch(this.path, this.requestInit);
-      if (!modelConfigRequest.ok) {
-        throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
-      }
-      let modelConfig;
-      try {
-        modelConfig = await modelConfigRequest.json();
-      } catch (e) {
-        let message = `Failed to parse model JSON of response from ${this.path}.`;
-        if (this.path.endsWith(".pb")) {
-          message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
-        } else {
-          message += " Please make sure the server is serving valid JSON for this request.";
-        }
-        throw new Error(message);
-      }
-      const modelTopology = modelConfig.modelTopology;
-      const weightsManifest = modelConfig.weightsManifest;
-      const generatedBy = modelConfig.generatedBy;
-      const convertedBy = modelConfig.convertedBy;
-      const format = modelConfig.format;
-      const userDefinedMetadata = modelConfig.userDefinedMetadata;
-      if (modelTopology == null && weightsManifest == null) {
-        throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-      }
-      let weightSpecs;
-      let weightData;
-      if (weightsManifest != null) {
-        const results = await this.loadWeights(weightsManifest);
-        [weightSpecs, weightData] = results;
-      }
-      return {
-        modelTopology,
-        weightSpecs,
-        weightData,
-        userDefinedMetadata,
-        generatedBy,
-        convertedBy,
-        format
-      };
-    }
-    async loadWeights(weightsManifest) {
-      const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
-      const [prefix, suffix] = parseUrl(weightPath);
-      const pathPrefix = this.weightPathPrefix || prefix;
-      const weightSpecs = [];
-      for (const entry of weightsManifest) {
-        weightSpecs.push(...entry.weights);
-      }
-      const fetchURLs = [];
-      const urlPromises = [];
-      for (const weightsGroup of weightsManifest) {
-        for (const path of weightsGroup.paths) {
-          if (this.weightUrlConverter != null) {
-            urlPromises.push(this.weightUrlConverter(path));
-          } else {
-            fetchURLs.push(pathPrefix + path + suffix);
-          }
-        }
-      }
-      if (this.weightUrlConverter) {
-        fetchURLs.push(...await Promise.all(urlPromises));
-      }
-      const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
-        requestInit: this.requestInit,
-        fetchFunc: this.fetch,
-        onProgress: this.onProgress
-      });
-      return [weightSpecs, concatenateArrayBuffers(buffers)];
-    }
-  }
-  HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
-  function parseUrl(url) {
-    const lastSlash = url.lastIndexOf("/");
-    const lastSearchParam = url.lastIndexOf("?");
-    const prefix = url.substring(0, lastSlash);
-    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
-    return [prefix + "/", suffix];
-  }
-  function isHTTPScheme(url) {
-    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
-  }
-  const httpRouter = (url, loadOptions) => {
-    if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
-      return null;
-    } else {
-      let isHTTP = true;
-      if (Array.isArray(url)) {
-        isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
-      } else {
-        isHTTP = isHTTPScheme(url);
-      }
-      if (isHTTP) {
-        return http(url, loadOptions);
-      }
-    }
-    return null;
-  };
-  IORouterRegistry.registerSaveRouter(httpRouter);
-  IORouterRegistry.registerLoadRouter(httpRouter);
-  function http(path, loadOptions) {
-    return new HTTPRequest(path, loadOptions);
-  }
-  function browserHTTPRequest(path, loadOptions) {
-    return http(path, loadOptions);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class PassthroughLoader {
-    constructor(modelArtifacts) {
-      this.modelArtifacts = modelArtifacts;
-    }
-    async load() {
-      return this.modelArtifacts;
-    }
-  }
-  class PassthroughSaver {
-    constructor(saveHandler) {
-      this.saveHandler = saveHandler;
-    }
-    async save(modelArtifacts) {
-      return this.saveHandler(modelArtifacts);
-    }
-  }
-  function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
-    if (arguments.length === 1) {
-      const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
-      if (isModelArtifacts) {
-        return new PassthroughLoader(modelArtifacts);
-      } else {
-        console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-        return new PassthroughLoader({modelTopology: modelArtifacts});
-      }
-    } else {
-      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-      return new PassthroughLoader({
-        modelTopology: modelArtifacts,
-        weightSpecs,
-        weightData,
-        trainingConfig
-      });
-    }
-  }
-  function withSaveHandler(saveHandler) {
-    return new PassthroughSaver(saveHandler);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/io/io.js
-  const io_exports = {};
-  __export(io_exports, {
-    browserFiles: () => browserFiles,
-    browserHTTPRequest: () => browserHTTPRequest,
-    concatenateArrayBuffers: () => concatenateArrayBuffers,
-    copyModel: () => copyModel,
-    decodeWeights: () => decodeWeights,
-    encodeWeights: () => encodeWeights,
-    fromMemory: () => fromMemory,
-    getLoadHandlers: () => getLoadHandlers,
-    getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
-    getSaveHandlers: () => getSaveHandlers,
-    http: () => http,
-    isHTTPScheme: () => isHTTPScheme,
-    listModels: () => listModels,
-    loadWeights: () => loadWeights,
-    moveModel: () => moveModel,
-    registerLoadRouter: () => registerLoadRouter,
-    registerSaveRouter: () => registerSaveRouter,
-    removeModel: () => removeModel,
-    weightsLoaderFactory: () => weightsLoaderFactory,
-    withSaveHandler: () => withSaveHandler
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reshape_(x, shape) {
-    const $x = convertToTensor(x, "x", "reshape", null);
-    shape = inferFromImplicitShape(shape, $x.size);
-    assert($x.size === sizeFromShape(shape), () => "new shape and old shape must have the same number of elements.");
-    const inputs = {x: $x};
-    const attrs = {shape};
-    const forward = (backend2, save) => {
-      save([$x]);
-      return backend2.reshape($x, shape);
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, Reshape, attrs);
-  }
-  const reshape = op({reshape_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function matMul_(a, b, transposeA = false, transposeB = false) {
-    let $a = convertToTensor(a, "a", "matMul");
-    let $b = convertToTensor(b, "b", "matMul");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-    const outerDimsA = $a.shape.slice(0, -2);
-    const outerDimsB = $b.shape.slice(0, -2);
-    const batchDimA = sizeFromShape(outerDimsA);
-    const batchDimB = sizeFromShape(outerDimsB);
-    assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-    assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-    const forward = (backend2, save) => {
-      save([a3D, b3D]);
-      return backend2.batchMatMul(a3D, b3D, transposeA, transposeB);
-    };
-    const inputs = {a: a3D, b: b3D};
-    const attrs = {transposeA, transposeB};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, BatchMatMul, attrs);
-    return reshape(res, outShape);
-  }
-  const matMul = op({matMul_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function oneHot_(indices, depth, onValue = 1, offValue = 0) {
-    if (depth < 2) {
-      throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
-    }
-    const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
-    const outShape = [...$indices.shape, depth];
-    const forward = (backend2, save) => {
-      save([$indices]);
-      return reshape(backend2.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);
-    };
-    const inputs = {indices: $indices};
-    const attrs = {depth, onValue, offValue};
-    return ENGINE.runKernelFunc(forward, inputs, null, OneHot, attrs);
-  }
-  const oneHot = op({oneHot_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function transpose_(x, perm) {
-    const $x = convertToTensor(x, "x", "transpose");
-    if (perm == null) {
-      perm = $x.shape.map((s, i) => i).reverse();
-    }
-    assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
-    perm.forEach((axis) => {
-      assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
-    });
-    if ($x.rank <= 1) {
-      return $x.clone();
-    }
-    const inputs = {x: $x};
-    const attrs = {perm};
-    return ENGINE.runKernelFunc((backend2) => backend2.transpose($x, perm), inputs, null, Transpose, attrs);
-  }
-  const transpose = op({transpose_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function confusionMatrix_(labels, predictions, numClasses) {
-    const $labels = convertToTensor(labels, "labels", "confusionMatrix");
-    const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
-    assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
-    assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
-    assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
-    assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
-    assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
-    const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
-    const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
-    const oneHotLabelsT = transpose(oneHotLabels);
-    return cast(matMul(oneHotLabelsT, oneHotPredictions), "int32");
-  }
-  const confusionMatrix = op({confusionMatrix_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/math.js
-  const math_exports = {};
-  __export(math_exports, {
-    confusionMatrix: () => confusionMatrix
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor3d(values, shape, dtype) {
-    assertNonNull(values);
-    if (shape != null && shape.length !== 3) {
-      throw new Error("tensor3d() requires shape to have three numbers");
-    }
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 3 && inferredShape.length !== 1) {
-      throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
-    }
-    if (inferredShape.length === 1 && shape == null) {
-      throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-    }
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
-  const browser_exports = {};
-  __export(browser_exports, {
-    fromPixels: () => fromPixels,
-    toPixels: () => toPixels
-  });
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  let fromPixels2DContext;
-  function fromPixels_(pixels, numChannels = 3) {
-    if (numChannels > 4) {
-      throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
-    }
-    if (pixels == null) {
-      throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-    }
-    let isPixelData = false;
-    let isImageData = false;
-    let isVideo = false;
-    let isImage = false;
-    let isCanvasLike = false;
-    if (pixels.data instanceof Uint8Array) {
-      isPixelData = true;
-    } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
-      isImageData = true;
-    } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
-      isVideo = true;
-    } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
-      isImage = true;
-    } else if (pixels.getContext != null) {
-      isCanvasLike = true;
-    } else {
-      throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
-    }
-    if (isVideo) {
-      const HAVE_CURRENT_DATA_READY_STATE = 2;
-      if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
-        throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-      }
-    }
-    const kernel = getKernel(FromPixels, ENGINE.backendName);
-    if (kernel != null) {
-      const inputs = {pixels};
-      const attrs = {numChannels};
-      return ENGINE.runKernel(FromPixels, inputs, attrs);
-    }
-    const [width, height] = isVideo ? [
-      pixels.videoWidth,
-      pixels.videoHeight
-    ] : [pixels.width, pixels.height];
-    let vals;
-    if (isCanvasLike) {
-      vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
-    } else if (isImageData || isPixelData) {
-      vals = pixels.data;
-    } else if (isImage || isVideo) {
-      if (fromPixels2DContext == null) {
-        fromPixels2DContext = document.createElement("canvas").getContext("2d");
-      }
-      fromPixels2DContext.canvas.width = width;
-      fromPixels2DContext.canvas.height = height;
-      fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
-      vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
-    }
-    let values;
-    if (numChannels === 4) {
-      values = new Int32Array(vals);
-    } else {
-      const numPixels = width * height;
-      values = new Int32Array(numPixels * numChannels);
-      for (let i = 0; i < numPixels; i++) {
-        for (let channel = 0; channel < numChannels; ++channel) {
-          values[i * numChannels + channel] = vals[i * 4 + channel];
-        }
-      }
-    }
-    const outShape = [height, width, numChannels];
-    return tensor3d(values, outShape, "int32");
-  }
-  async function toPixels(img, canvas) {
-    let $img = convertToTensor(img, "img", "toPixels");
-    if (!(img instanceof Tensor)) {
-      const originalImgTensor = $img;
-      $img = cast(originalImgTensor, "int32");
-      originalImgTensor.dispose();
-    }
-    if ($img.rank !== 2 && $img.rank !== 3) {
-      throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
-    }
-    const [height, width] = $img.shape.slice(0, 2);
-    const depth = $img.rank === 2 ? 1 : $img.shape[2];
-    if (depth > 4 || depth === 2) {
-      throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
-    }
-    if ($img.dtype !== "float32" && $img.dtype !== "int32") {
-      throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
-    }
-    const data = await $img.data();
-    const multiplier = $img.dtype === "float32" ? 255 : 1;
-    const bytes = new Uint8ClampedArray(width * height * 4);
-    for (let i = 0; i < height * width; ++i) {
-      const rgba = [0, 0, 0, 255];
-      for (let d = 0; d < depth; d++) {
-        const value = data[i * depth + d];
-        if ($img.dtype === "float32") {
-          if (value < 0 || value > 1) {
-            throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
-          }
-        } else if ($img.dtype === "int32") {
-          if (value < 0 || value > 255) {
-            throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
-          }
-        }
-        if (depth === 1) {
-          rgba[0] = value * multiplier;
-          rgba[1] = value * multiplier;
-          rgba[2] = value * multiplier;
-        } else {
-          rgba[d] = value * multiplier;
-        }
-      }
-      const j = i * 4;
-      bytes[j + 0] = Math.round(rgba[0]);
-      bytes[j + 1] = Math.round(rgba[1]);
-      bytes[j + 2] = Math.round(rgba[2]);
-      bytes[j + 3] = Math.round(rgba[3]);
-    }
-    if (canvas != null) {
-      canvas.width = width;
-      canvas.height = height;
-      const ctx = canvas.getContext("2d");
-      const imageData = new ImageData(bytes, width, height);
-      ctx.putImageData(imageData, 0, 0);
-    }
-    if ($img !== img) {
-      $img.dispose();
-    }
-    return bytes;
-  }
-  const fromPixels = op({fromPixels_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
-  const gather_nd_util_exports = {};
-  __export(gather_nd_util_exports, {
-    prepareAndValidate: () => prepareAndValidate
-  });
-  function prepareAndValidate(tensor17, indices) {
-    if (tensor17.rank < 1) {
-      throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor17.rank}.`);
-    }
-    if (indices.rank < 1) {
-      throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-    }
-    if (indices.dtype !== "int32") {
-      throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
-    }
-    if (indices.shape[indices.rank - 1] > tensor17.rank) {
-      throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank - 1]} vs. ${tensor17.rank}`);
-    }
-    if (tensor17.size === 0) {
-      throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor17.shape}.`);
-    }
-    const indicesShape = indices.shape;
-    const sliceRank = indicesShape[indicesShape.length - 1];
-    let nResult = 1;
-    for (let i = 0; i < indicesShape.length - 1; ++i) {
-      nResult *= indicesShape[i];
-    }
-    const inputShape = tensor17.shape;
-    const resultShape = indicesShape.slice();
-    resultShape.pop();
-    let sliceSize = 1;
-    for (let i = sliceRank; i < tensor17.rank; ++i) {
-      sliceSize *= inputShape[i];
-      resultShape.push(inputShape[i]);
-    }
-    const strides = [
-      ...computeStrides(tensor17.shape).map((stride) => stride / sliceSize),
-      1
-    ].slice(0, sliceRank);
-    return [resultShape, nResult, sliceSize, strides];
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
-  const scatter_nd_util_exports = {};
-  __export(scatter_nd_util_exports, {
-    calculateShapes: () => calculateShapes,
-    validateInput: () => validateInput,
-    validateUpdateShape: () => validateUpdateShape
-  });
-  function validateUpdateShape(shape, indices, updates) {
-    const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
-    const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
-    const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
-    if (updates.rank < batchDim) {
-      throw new Error(shapeError + ` update.rank < ${batchDim}. `);
-    }
-    if (shape.length < sliceDim + (updates.rank - batchDim)) {
-      throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
-    }
-    if (updates.rank !== batchDim + shape.length - sliceDim) {
-      throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
-    }
-    for (let d = 0; d < batchDim; ++d) {
-      if (updates.shape[d] !== indices.shape[d]) {
-        throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
-      }
-    }
-    for (let d = 0; d < updates.rank - batchDim; ++d) {
-      if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
-        throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
-      }
-    }
-  }
-  function validateInput(updates, indices, shape) {
-    if (indices.rank < 1) {
-      throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-    }
-    if (updates.rank < 1) {
-      throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
-    }
-    if (indices.dtype !== "int32") {
-      throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
-    }
-    if (shape.length < 1) {
-      throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
-    }
-    if (shape.length === 0) {
-      if (indices.size === 0) {
-        throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
-      }
-      if (updates.size === 0) {
-        throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
-      }
-    }
-    validateUpdateShape(shape, indices, updates);
-  }
-  function calculateShapes(updates, indices, shape) {
-    const indicesRank = indices.shape.length;
-    const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
-    const totalNd = shape.length;
-    let sliceSize = 1;
-    for (let i = sliceRank; i < totalNd; ++i) {
-      sliceSize *= shape[i];
-    }
-    const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
-    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
-    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
-    const outputSize = sizeFromShape(shape);
-    return {sliceRank, numUpdates, sliceSize, strides, outputSize};
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
-  const slice_util_exports = {};
-  __export(slice_util_exports, {
-    assertParamsValid: () => assertParamsValid,
-    computeFlatOffset: () => computeFlatOffset,
-    computeOutShape: () => computeOutShape,
-    getNormalizedAxes: () => getNormalizedAxes,
-    isSliceContinous: () => isSliceContinous,
-    maskToAxes: () => maskToAxes,
-    parseSliceParams: () => parseSliceParams,
-    startForAxis: () => startForAxis,
-    startIndicesWithElidedDims: () => startIndicesWithElidedDims,
-    stopForAxis: () => stopForAxis,
-    stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
-    stridesForAxis: () => stridesForAxis,
-    stridesWithElidedDims: () => stridesWithElidedDims
-  });
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function assertParamsValid(input, begin, size) {
-    assert(input.rank === begin.length, () => `Error in slice${input.rank}D: Length of begin ${begin} must match the rank of the array (${input.rank}).`);
-    assert(input.rank === size.length, () => `Error in slice${input.rank}D: Length of size ${size} must match the rank of the array (${input.rank}).`);
-    for (let i = 0; i < input.rank; ++i) {
-      assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${input.rank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);
-    }
-  }
-  function maskToAxes(mask) {
-    const axes = [];
-    let axis = 0;
-    while (mask > 0) {
-      if (mask & 1) {
-        axes.push(axis);
-      }
-      mask /= 2;
-      axis++;
-    }
-    return axes;
-  }
-  function computeOutShape(begin, end, strides) {
-    const size = [];
-    for (let axis = 0; axis < begin.length; axis++) {
-      size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
-    }
-    return size;
-  }
-  function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
-    const newStrides = [...strides];
-    for (let i = newStrides.length; i < inputShape.length; i++) {
-      newStrides.push(1);
-    }
-    for (let i = 0; i < numElidedAxes; i++) {
-      if (i === 0) {
-        newStrides[ellipsisInsertionIndex] = 1;
-      } else {
-        newStrides.splice(ellipsisInsertionIndex, 0, 1);
-        newStrides.pop();
-      }
-    }
-    return newStrides;
-  }
-  function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
-    if (normalizedAxis <= ellipsisInsertionIndex) {
-      return normalizedAxis;
-    }
-    return normalizedAxis - (numElidedAxes - 1);
-  }
-  function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
-    const elidedAxes = [];
-    for (let i = 0; i < numElidedAxes; i++) {
-      elidedAxes.push(ellipsisInsertionIndex + i);
-    }
-    return elidedAxes;
-  }
-  function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
-    const inputRank = inputShape.length;
-    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
-    if (ellipsisAxes.length && numInterpolatedAxes > 0) {
-      const fullIndex = ellipsisAxes[0];
-      const numElidedAxes = numInterpolatedAxes + 1;
-      normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
-      normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
-      normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
-    } else {
-      for (let axis = 0; axis < inputRank; axis++) {
-        normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
-        normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
-        normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
-      }
-    }
-    return {
-      begin: normalizedBegin,
-      end: normalizedEnd,
-      strides: normalizedStrides
-    };
-  }
-  function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
-    const newIndices = [...inputShape];
-    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-    for (let axis = 0; axis < newIndices.length; axis++) {
-      if (elidedAxes.indexOf(axis) > -1) {
-        newIndices[axis] = 0;
-      } else {
-        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-        let originalValue = originalBegin[originalAxis];
-        if (beginMask & 1 << originalAxis) {
-          originalValue = 0;
-        }
-        newIndices[axis] = originalValue;
-      }
-    }
-    return newIndices;
-  }
-  function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
-    const newIndices = [...inputShape];
-    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-    for (let axis = 0; axis < newIndices.length; axis++) {
-      if (elidedAxes.indexOf(axis) > -1) {
-        newIndices[axis] = Number.MAX_SAFE_INTEGER;
-      } else {
-        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-        let originalValue = originalEnd[originalAxis];
-        if (endMask & 1 << originalAxis) {
-          originalValue = Number.MAX_SAFE_INTEGER;
-        }
-        newIndices[axis] = originalValue;
-      }
-    }
-    for (let i = 0; i < newIndices.length; i++) {
-      const axisSize = inputShape[i];
-      if (newIndices[i] < 0) {
-        newIndices[i] += axisSize;
-      }
-      newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
-    }
-    return newIndices;
-  }
-  function stridesForAxis(strides, axis, ellipsisMask) {
-    let stride = strides[axis];
-    if (ellipsisMask & 1 << axis || stride == null) {
-      stride = 1;
-    }
-    return stride;
-  }
-  function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
-    let start = startIndices[axis];
-    const stride = strides[axis] || 1;
-    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
-      if (stride > 0) {
-        start = Number.MIN_SAFE_INTEGER;
-      } else {
-        start = Number.MAX_SAFE_INTEGER;
-      }
-    }
-    const axisSize = inputShape[axis];
-    if (start < 0) {
-      start += axisSize;
-    }
-    start = clamp(0, start, axisSize - 1);
-    return start;
-  }
-  function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
-    let stop = stopIndices[axis];
-    const stride = strides[axis] || 1;
-    if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
-      if (stride > 0) {
-        stop = Number.MAX_SAFE_INTEGER;
-      } else {
-        stop = Number.MIN_SAFE_INTEGER;
-      }
-    }
-    const axisSize = inputShape[axis];
-    if (stop < 0) {
-      stop += axisSize;
-    }
-    if (stride > 0) {
-      stop = clamp(0, stop, axisSize);
-    } else {
-      stop = clamp(-1, stop, axisSize - 1);
-    }
-    return stop;
-  }
-  function isSliceContinous(shape, begin, size) {
-    let firstNonOneAxis = size.length;
-    for (let i = 0; i < size.length; i++) {
-      if (size[i] > 1) {
-        firstNonOneAxis = i;
-        break;
-      }
-    }
-    for (let i = firstNonOneAxis + 1; i < size.length; i++) {
-      if (begin[i] > 0 || size[i] !== shape[i]) {
-        return false;
-      }
-    }
-    return true;
-  }
-  function computeFlatOffset(begin, strides) {
-    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-    for (let i = 0; i < begin.length - 1; i++) {
-      flatOffset += begin[i] * strides[i];
-    }
-    return flatOffset;
-  }
-  function parseSliceParams(x, begin, size) {
-    let begin_;
-    if (typeof begin === "number") {
-      begin_ = [begin, ...new Array(x.rank - 1).fill(0)];
-    } else if (begin.length < x.rank) {
-      begin_ = begin.concat(new Array(x.rank - begin.length).fill(0));
-    } else {
-      begin_ = begin.slice();
-    }
-    begin_.forEach((d) => {
-      assert(d !== -1, () => "slice() does not support negative begin indexing.");
-    });
-    let size_;
-    if (size == null) {
-      size_ = new Array(x.rank).fill(-1);
-    } else if (typeof size === "number") {
-      size_ = [size, ...new Array(x.rank - 1).fill(-1)];
-    } else if (size.length < x.rank) {
-      size_ = size.concat(new Array(x.rank - size.length).fill(-1));
-    } else {
-      size_ = size;
-    }
-    size_ = size_.map((d, i) => {
-      if (d >= 0) {
-        return d;
-      } else {
-        assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
-        return x.shape[i] - begin_[i];
-      }
-    });
-    return [begin_, size_];
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/serialization.js
-  const serialization_exports = {};
-  __export(serialization_exports, {
-    Serializable: () => Serializable,
-    SerializationMap: () => SerializationMap,
-    registerClass: () => registerClass
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class Serializable {
-    getClassName() {
-      return this.constructor.className;
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2);
-    }
-  }
-  class SerializationMap {
-    constructor() {
-      this.classNameMap = {};
-    }
-    static getMap() {
-      if (SerializationMap.instance == null) {
-        SerializationMap.instance = new SerializationMap();
-      }
-      return SerializationMap.instance;
-    }
-    static register(cls) {
-      SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
-    }
-  }
-  function registerClass(cls) {
-    assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
-    assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
-    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
-    SerializationMap.register(cls);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/test_util.js
-  const test_util_exports = {};
-  __export(test_util_exports, {
-    TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
-    expectArrayBuffersEqual: () => expectArrayBuffersEqual,
-    expectArraysClose: () => expectArraysClose,
-    expectArraysEqual: () => expectArraysEqual,
-    expectNumbersClose: () => expectNumbersClose,
-    expectPromiseToFail: () => expectPromiseToFail,
-    expectValuesInRange: () => expectValuesInRange,
-    testEpsilon: () => testEpsilon
-  });
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const TEST_EPSILON_FLOAT32 = 1e-3;
-  const TEST_EPSILON_FLOAT16 = 0.1;
-  function expectArraysClose(actual, expected, epsilon2) {
-    if (epsilon2 == null) {
-      epsilon2 = testEpsilon();
-    }
-    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon2));
-  }
-  function testEpsilon() {
-    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
-  }
-  function expectArraysPredicate(actual, expected, predicate) {
-    let checkClassType = true;
-    if (isTypedArray(actual) || isTypedArray(expected)) {
-      checkClassType = false;
-    }
-    if (isTypedArray(actual) && isTypedArray(expected)) {
-      checkClassType = true;
-    }
-    if (checkClassType) {
-      const aType = actual.constructor.name;
-      const bType = expected.constructor.name;
-      if (aType !== bType) {
-        throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
-      }
-    }
-    if (Array.isArray(actual) && Array.isArray(expected)) {
-      const actualShape = inferShape(actual);
-      const expectedShape = inferShape(expected);
-      if (!arraysEqual(actualShape, expectedShape)) {
-        throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
-      }
-    }
-    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
-    const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
-    if (actualFlat.length !== expectedFlat.length) {
-      throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-    }
-    for (let i = 0; i < expectedFlat.length; ++i) {
-      const a = actualFlat[i];
-      const e = expectedFlat[i];
-      if (!predicate(a, e)) {
-        throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-      }
-    }
-  }
-  function expectPromiseToFail(fn, done) {
-    fn().then(() => done.fail(), () => done());
-  }
-  function expectArraysEqual(actual, expected) {
-    const exp11 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
-    if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
-      return expectArraysPredicate(actual, exp11, (a, b) => a == b);
-    }
-    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
-  }
-  function expectNumbersClose(a, e, epsilon2) {
-    if (epsilon2 == null) {
-      epsilon2 = testEpsilon();
-    }
-    if (!areClose(a, e, epsilon2)) {
-      throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
-    }
-  }
-  function areClose(a, e, epsilon2) {
-    if (!isFinite(a) && !isFinite(e)) {
-      return true;
-    }
-    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon2) {
-      return false;
-    }
-    return true;
-  }
-  function expectValuesInRange(actual, low, high) {
-    for (let i = 0; i < actual.length; i++) {
-      if (actual[i] < low || actual[i] > high) {
-        throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
-      }
-    }
-  }
-  function expectArrayBuffersEqual(actual, expected) {
-    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/version.js
-  /** @license See the LICENSE file. */
-  const version = "2.4.0";
-
-  // node_modules/@tensorflow/tfjs-core/dist/globals.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function enableProdMode() {
-    env().set("PROD", true);
-  }
-  function enableDebugMode() {
-    env().set("DEBUG", true);
-  }
-  function disableDeprecationWarnings() {
-    env().set("DEPRECATION_WARNINGS_ENABLED", false);
-    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
-  }
-  function deprecationWarn(msg) {
-    if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
-      console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
-    }
-  }
-  setDeprecationWarningFn(deprecationWarn);
-  function disposeVariables() {
-    ENGINE.disposeVariables();
-  }
-  function engine15() {
-    return ENGINE;
-  }
-  function memory() {
-    return ENGINE.memory();
-  }
-  function profile(f) {
-    return ENGINE.profile(f);
-  }
-  function tidy(nameOrFn, fn) {
-    return ENGINE.tidy(nameOrFn, fn);
-  }
-  function dispose(container) {
-    const tensors = getTensorsInContainer(container);
-    tensors.forEach((tensor17) => tensor17.dispose());
-  }
-  function keep(result) {
-    return ENGINE.keep(result);
-  }
-  function time(f) {
-    return ENGINE.time(f);
-  }
-  function setBackend(backendName) {
-    return ENGINE.setBackend(backendName);
-  }
-  function ready() {
-    return ENGINE.ready();
-  }
-  function getBackend() {
-    return ENGINE.backendName;
-  }
-  function removeBackend(name) {
-    ENGINE.removeBackend(name);
-  }
-  function findBackend(name) {
-    return ENGINE.findBackend(name);
-  }
-  function findBackendFactory(name) {
-    return ENGINE.findBackendFactory(name);
-  }
-  function registerBackend(name, factory, priority = 1) {
-    return ENGINE.registerBackend(name, factory, priority);
-  }
-  function backend() {
-    return ENGINE.backend;
-  }
-  function setPlatform(platformName, platform) {
-    env().setPlatform(platformName, platform);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/add.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function add_(a, b) {
-    let $a = convertToTensor(a, "a", "add");
-    let $b = convertToTensor(b, "b", "add");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.add($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Add);
-  }
-  const add2 = op({add_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function floorDiv_(a, b) {
-    let $a = convertToTensor(a, "a", "floorDiv");
-    let $b = convertToTensor(b, "b", "floorDiv");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.floorDiv($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, FloorDiv);
-  }
-  const floorDiv = op({floorDiv_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/div.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function div_(a, b) {
-    let $a = convertToTensor(a, "a", "div");
-    let $b = convertToTensor(b, "b", "div");
-    [$a, $b] = makeTypesMatch($a, $b);
-    if ($a.dtype === "int32" && $b.dtype === "int32") {
-      return floorDiv($a, $b);
-    }
-    const forward = (backend2, save) => {
-      const res = backend2.realDivide($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    const attrs = {};
-    return ENGINE.runKernelFunc(forward, inputs, null, Div, attrs);
-  }
-  const div = op({div_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function mul_(a, b) {
-    let $a = convertToTensor(a, "a", "mul");
-    let $b = convertToTensor(b, "b", "mul");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.multiply($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Multiply);
-  }
-  const mul = op({mul_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function abs_(x) {
-    const $x = convertToTensor(x, "x", "abs");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      save([$x]);
-      if ($x.dtype === "complex64") {
-        return backend2.complexAbs($x);
-      }
-      return backend2.abs($x);
-    }, inputs, null, Abs);
-  }
-  const abs = op({abs_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function acos_(x) {
-    const $x = convertToTensor(x, "x", "acos");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.acos($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Acos);
-  }
-  const acos = op({acos_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function acosh_(x) {
-    const $x = convertToTensor(x, "x", "acosh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.acosh($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Acosh);
-  }
-  const acosh = op({acosh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function addN_(tensors) {
-    assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
-    assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
-    const firstTensor = $tensors[0];
-    $tensors.forEach((t) => {
-      if (t.dtype !== firstTensor.dtype) {
-        throw new Error("All tensors passed to tf.addN() must have the same dtype");
-      }
-    });
-    $tensors.forEach((t) => {
-      if (!arraysEqual(t.shape, firstTensor.shape)) {
-        throw new Error("All tensors passed to tf.addN() must have the same shape");
-      }
-    });
-    const forward = (backend2, save) => {
-      const res = backend2.addN($tensors);
-      save($tensors);
-      return res;
-    };
-    const inputs = $tensors;
-    return ENGINE.runKernelFunc(forward, inputs, null, AddN);
-  }
-  const addN = op({addN_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function axesAreInnerMostDims(axes, rank) {
-    for (let i = 0; i < axes.length; ++i) {
-      if (axes[axes.length - i - 1] !== rank - 1 - i) {
-        return false;
-      }
-    }
-    return true;
-  }
-  function combineLocations(outputLoc, reduceLoc, axes) {
-    const rank = outputLoc.length + reduceLoc.length;
-    const loc = [];
-    let outIdx = 0;
-    let reduceIdx = 0;
-    for (let dim = 0; dim < rank; dim++) {
-      if (axes.indexOf(dim) === -1) {
-        loc.push(outputLoc[outIdx++]);
-      } else {
-        loc.push(reduceLoc[reduceIdx++]);
-      }
-    }
-    return loc;
-  }
-  function computeOutAndReduceShapes(aShape, axes) {
-    const outShape = [];
-    const rank = aShape.length;
-    for (let dim = 0; dim < rank; dim++) {
-      if (axes.indexOf(dim) === -1) {
-        outShape.push(aShape[dim]);
-      }
-    }
-    const reduceShape = axes.map((dim) => aShape[dim]);
-    return [outShape, reduceShape];
-  }
-  function expandShapeToKeepDim(shape, axes) {
-    const reduceSubShape = axes.map((x) => 1);
-    return combineLocations(shape, reduceSubShape, axes);
-  }
-  function assertAxesAreInnerMostDims(msg, axes, rank) {
-    assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
-  }
-  function getAxesPermutation(axes, rank) {
-    if (axesAreInnerMostDims(axes, rank)) {
-      return null;
-    }
-    const result = [];
-    for (let i = 0; i < rank; ++i) {
-      if (axes.indexOf(i) === -1) {
-        result.push(i);
-      }
-    }
-    axes.forEach((axis) => result.push(axis));
-    return result;
-  }
-  function getUndoAxesPermutation(axes) {
-    return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
-  }
-  function getInnerMostAxes(numAxes, rank) {
-    const res = [];
-    for (let i = rank - numAxes; i < rank; ++i) {
-      res.push(i);
-    }
-    return res;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/all.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function all_(x, axis = null, keepDims = false) {
-    let $x = convertToTensor(x, "x", "all", "bool");
-    const forward = (backend2) => {
-      const origAxes = parseAxisParam(axis, $x.shape);
-      let axes = origAxes;
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      if (permutedAxes != null) {
-        $x = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, $x.rank);
-      }
-      const res = backend2.all($x, axes);
-      if (keepDims) {
-        const newShape = expandShapeToKeepDim(res.shape, origAxes);
-        return reshape(res, newShape);
-      }
-      return res;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, All, attrs);
-  }
-  const all = op({all_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/any.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function any_(x, axis = null, keepDims = false) {
-    let $x = convertToTensor(x, "x", "any", "bool");
-    const forward = (backend2) => {
-      const origAxes = parseAxisParam(axis, $x.shape);
-      let axes = origAxes;
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      if (permutedAxes != null) {
-        $x = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, $x.rank);
-      }
-      const res = backend2.any($x, axes);
-      if (keepDims) {
-        const newShape = expandShapeToKeepDim(res.shape, origAxes);
-        return reshape(res, newShape);
-      }
-      return res;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, Any, attrs);
-  }
-  const any = op({any_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function argMax_(x, axis = 0) {
-    let $x = convertToTensor(x, "x", "argMax");
-    const forward = (backend2, save) => {
-      save([$x]);
-      let axes = parseAxisParam(axis, $x.shape);
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      if (permutedAxes != null) {
-        $x = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, $x.rank);
-      }
-      return backend2.argMax($x, axes[0]);
-    };
-    const inputs = {x: $x};
-    const attrs = {axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, ArgMax, attrs);
-  }
-  const argMax = op({argMax_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function argMin_(x, axis = 0) {
-    let $x = convertToTensor(x, "x", "argMin");
-    const forward = (backend2, save) => {
-      save([$x]);
-      if (axis == null) {
-        axis = 0;
-      }
-      let axes = parseAxisParam(axis, $x.shape);
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      if (permutedAxes != null) {
-        $x = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, $x.rank);
-      }
-      return backend2.argMin($x, axes[0]);
-    };
-    const inputs = {x: $x};
-    const attrs = {axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, ArgMin, attrs);
-  }
-  const argMin = op({argMin_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function asin_(x) {
-    const $x = convertToTensor(x, "x", "asin");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.asin($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Asin);
-  }
-  const asin = op({asin_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function asinh_(x) {
-    const $x = convertToTensor(x, "x", "asinh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.asinh($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Asinh);
-  }
-  const asinh = op({asinh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function atan_(x) {
-    const $x = convertToTensor(x, "x", "atan");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.atan($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Atan);
-  }
-  const atan = op({atan_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function atan2_(a, b) {
-    let $a = convertToTensor(a, "a", "atan2");
-    let $b = convertToTensor(b, "b", "atan2");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.atan2($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Atan2);
-  }
-  const atan2 = op({atan2_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function atanh_(x) {
-    const $x = convertToTensor(x, "x", "atanh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.atanh($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Atanh);
-  }
-  const atanh = op({atanh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function computeDilation2DInfo(inputShape, filterShape, strides, pad8, dataFormat = "NHWC", dilations) {
-    const inputChannels = inputShape[3];
-    const $filterShape = [...filterShape, inputChannels];
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad8, null, null, $dataFormat);
-  }
-  function computePool2DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "channelsLast") {
-    const [filterHeight, filterWidth] = parseTupleParam(filterSize);
-    let filterShape;
-    if (dataFormat === "channelsLast") {
-      filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
-    } else if (dataFormat === "channelsFirst") {
-      filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
-    } else {
-      throw new Error(`Unknown dataFormat ${dataFormat}`);
-    }
-    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, false, dataFormat);
-  }
-  function computePool3DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "NDHWC") {
-    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
-    let filterShape;
-    let $dataFormat;
-    if (dataFormat === "NDHWC") {
-      $dataFormat = "channelsLast";
-      filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
-    } else if (dataFormat === "NCDHW") {
-      $dataFormat = "channelsFirst";
-      filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
-    } else {
-      throw new Error(`Unknown dataFormat ${dataFormat}`);
-    }
-    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, false, $dataFormat, roundingMode);
-  }
-  function computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, depthwise = false, dataFormat = "channelsLast") {
-    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
-    if (dataFormat === "channelsLast") {
-      [batchSize, inHeight, inWidth, inChannels] = inShape;
-    } else if (dataFormat === "channelsFirst") {
-      [batchSize, inChannels, inHeight, inWidth] = inShape;
-    } else {
-      throw new Error(`Unknown dataFormat ${dataFormat}`);
-    }
-    const [filterHeight, filterWidth, , filterChannels] = filterShape;
-    const [strideHeight, strideWidth] = parseTupleParam(strides);
-    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
-    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-    const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
-    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-    let outShape;
-    if (dataFormat === "channelsFirst") {
-      outShape = [batchSize, outChannels, outHeight, outWidth];
-    } else if (dataFormat === "channelsLast") {
-      outShape = [batchSize, outHeight, outWidth, outChannels];
-    }
-    return {
-      batchSize,
-      dataFormat,
-      inHeight,
-      inWidth,
-      inChannels,
-      outHeight,
-      outWidth,
-      outChannels,
-      padInfo,
-      strideHeight,
-      strideWidth,
-      filterHeight,
-      filterWidth,
-      effectiveFilterHeight,
-      effectiveFilterWidth,
-      dilationHeight,
-      dilationWidth,
-      inShape,
-      outShape,
-      filterShape
-    };
-  }
-  function computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, depthwise = false, dataFormat = "channelsLast", roundingMode) {
-    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
-    if (dataFormat === "channelsLast") {
-      [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
-    } else if (dataFormat === "channelsFirst") {
-      [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
-    } else {
-      throw new Error(`Unknown dataFormat ${dataFormat}`);
-    }
-    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
-    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
-    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
-    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
-    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-    const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
-    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-    let outShape;
-    if (dataFormat === "channelsFirst") {
-      outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
-    } else if (dataFormat === "channelsLast") {
-      outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
-    }
-    return {
-      batchSize,
-      dataFormat,
-      inDepth,
-      inHeight,
-      inWidth,
-      inChannels,
-      outDepth,
-      outHeight,
-      outWidth,
-      outChannels,
-      padInfo,
-      strideDepth,
-      strideHeight,
-      strideWidth,
-      filterDepth,
-      filterHeight,
-      filterWidth,
-      effectiveFilterDepth,
-      effectiveFilterHeight,
-      effectiveFilterWidth,
-      dilationDepth,
-      dilationHeight,
-      dilationWidth,
-      inShape,
-      outShape,
-      filterShape
-    };
-  }
-  function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
-    if (zeroPad == null) {
-      zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-    }
-    const inputRows = inShape[0];
-    const inputCols = inShape[1];
-    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-    assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-    assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-    return [outputRows, outputCols];
-  }
-  function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
-    if (zeroPad == null) {
-      zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-    }
-    const inputDepth = inShape[0];
-    const inputRows = inShape[1];
-    const inputCols = inShape[2];
-    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-    assert(isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);
-    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-    assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-    assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-    return [outputDepths, outputRows, outputCols, outChannels];
-  }
-  function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
-    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
-    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
-  }
-  function parseTupleParam(param) {
-    if (typeof param === "number") {
-      return [param, param, param];
-    }
-    if (param.length === 2) {
-      return [param[0], param[1], 1];
-    }
-    return param;
-  }
-  function parse3TupleParam(param) {
-    return typeof param === "number" ? [param, param, param] : param;
-  }
-  function getEffectiveFilterSize(filterSize, dilation) {
-    if (dilation <= 1) {
-      return filterSize;
-    }
-    return filterSize + (filterSize - 1) * (dilation - 1);
-  }
-  function getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
-    let padInfo;
-    let outHeight;
-    let outWidth;
-    if (typeof pad8 === "number") {
-      const padType = pad8 === 0 ? "VALID" : "NUMBER";
-      padInfo = {top: pad8, bottom: pad8, left: pad8, right: pad8, type: padType};
-      const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad8, roundingMode);
-      outHeight = outShape[0];
-      outWidth = outShape[1];
-    } else if (pad8 === "same") {
-      outHeight = Math.ceil(inHeight / strideHeight);
-      outWidth = Math.ceil(inWidth / strideWidth);
-      const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
-      const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
-      const top = Math.floor(padAlongHeight / 2);
-      const bottom = padAlongHeight - top;
-      const left = Math.floor(padAlongWidth / 2);
-      const right = padAlongWidth - left;
-      padInfo = {top, bottom, left, right, type: "SAME"};
-    } else if (pad8 === "valid") {
-      padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
-      outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-      outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-    } else if (typeof pad8 === "object") {
-      const top = dataFormat === "channelsLast" ? pad8[1][0] : pad8[2][0];
-      const bottom = dataFormat === "channelsLast" ? pad8[1][1] : pad8[2][1];
-      const left = dataFormat === "channelsLast" ? pad8[2][0] : pad8[3][0];
-      const right = dataFormat === "channelsLast" ? pad8[2][1] : pad8[3][1];
-      const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
-      padInfo = {top, bottom, left, right, type: padType};
-      outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
-      outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
-    } else {
-      throw Error(`Unknown padding parameter: ${pad8}`);
-    }
-    return {padInfo, outHeight, outWidth};
-  }
-  function get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
-    let padInfo;
-    let outDepth;
-    let outHeight;
-    let outWidth;
-    if (typeof pad8 === "number") {
-      const padType = pad8 === 0 ? "VALID" : "NUMBER";
-      padInfo = {
-        top: pad8,
-        bottom: pad8,
-        left: pad8,
-        right: pad8,
-        front: pad8,
-        back: pad8,
-        type: padType
-      };
-      const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad8, roundingMode);
-      outDepth = outShape[0];
-      outHeight = outShape[1];
-      outWidth = outShape[2];
-    } else if (pad8 === "same") {
-      outDepth = Math.ceil(inDepth / strideDepth);
-      outHeight = Math.ceil(inHeight / strideHeight);
-      outWidth = Math.ceil(inWidth / strideWidth);
-      const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
-      const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
-      const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
-      const front = Math.floor(padAlongDepth / 2);
-      const back = padAlongDepth - front;
-      const top = Math.floor(padAlongHeight / 2);
-      const bottom = padAlongHeight - top;
-      const left = Math.floor(padAlongWidth / 2);
-      const right = padAlongWidth - left;
-      padInfo = {top, bottom, left, right, front, back, type: "SAME"};
-    } else if (pad8 === "valid") {
-      padInfo = {
-        top: 0,
-        bottom: 0,
-        left: 0,
-        right: 0,
-        front: 0,
-        back: 0,
-        type: "VALID"
-      };
-      outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
-      outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-      outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-    } else {
-      throw Error(`Unknown padding parameter: ${pad8}`);
-    }
-    return {padInfo, outDepth, outHeight, outWidth};
-  }
-  function conditionalRound(value, roundingMode) {
-    if (!roundingMode) {
-      return value;
-    }
-    switch (roundingMode) {
-      case "round":
-        return Math.round(value);
-      case "ceil":
-        return Math.ceil(value);
-      case "floor":
-        return Math.floor(value);
-      default:
-        throw new Error(`Unknown roundingMode ${roundingMode}`);
-    }
-  }
-  function tupleValuesAreOne(param) {
-    const [dimA, dimB, dimC] = parseTupleParam(param);
-    return dimA === 1 && dimB === 1 && dimC === 1;
-  }
-  function eitherStridesOrDilationsAreOne(strides, dilations) {
-    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
-  }
-  function convertConv2DDataFormat(dataFormat) {
-    if (dataFormat === "NHWC") {
-      return "channelsLast";
-    } else if (dataFormat === "NCHW") {
-      return "channelsFirst";
-    } else {
-      throw new Error(`Unknown dataFormat ${dataFormat}`);
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function avgPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "avgPool", "float32");
-    const dilations = 1;
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-      save([x4D]);
-      if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-        return x4D.clone();
-      }
-      return backend2.avgPool(x4D, convInfo);
-    };
-    const inputs = {x: x4D};
-    const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-    let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool, attrs);
-    res = cast(res, $x.dtype);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const avgPool = op({avgPool_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function avgPool3d_(x, filterSize, strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    } else {
-      deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-    }
-    const $x = convertToTensor(x, "x", "avgPool3d", "float32");
-    let x5D = $x;
-    let reshapedTo5D = false;
-    if ($x.rank === 4) {
-      reshapedTo5D = true;
-      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-    }
-    assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-    assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      if (dilations == null) {
-        dilations = [1, 1, 1];
-      }
-      const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-      save([x5D]);
-      return backend2.avgPool3d(x5D, convInfo);
-    };
-    const inputs = {x: x5D};
-    const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-    let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3D, attrs);
-    res = cast(res, x5D.dtype);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const avgPool3d = op({avgPool3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function assertParamsConsistent(shapes, axis) {
-    const rank = shapes[0].length;
-    shapes.forEach((shape, i) => {
-      assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
-    });
-    assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
-    const firstShape = shapes[0];
-    shapes.forEach((shape, i) => {
-      for (let r = 0; r < rank; r++) {
-        assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
-      }
-    });
-  }
-  function computeOutShape2(shapes, axis) {
-    const outputShape = shapes[0].slice();
-    for (let i = 1; i < shapes.length; i++) {
-      outputShape[axis] += shapes[i][axis];
-    }
-    return outputShape;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function concat_(tensors, axis = 0) {
-    assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
-    let $tensors = convertToTensorArray(tensors, "tensors", "concat");
-    if ($tensors[0].dtype === "complex64") {
-      $tensors.forEach((tensor17) => {
-        if (tensor17.dtype !== "complex64") {
-          throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor17.dtype}. `);
-        }
-      });
-    }
-    const $axis = parseAxisParam(axis, $tensors[0].shape)[0];
-    const outShape = computeOutShape2($tensors.map((t) => t.shape), $axis);
-    if (sizeFromShape(outShape) === 0) {
-      return tensor4([], outShape);
-    }
-    $tensors = $tensors.filter((t) => t.size > 0);
-    if ($tensors.length === 1) {
-      return $tensors[0];
-    }
-    const shapes = $tensors.map((t) => t.shape);
-    assertParamsConsistent(shapes, $axis);
-    const forward = (backend2, save) => {
-      const res = backend2.concat($tensors, $axis);
-      save($tensors);
-      return res;
-    };
-    const inputs = $tensors;
-    const attr = {axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, Concat, attr);
-  }
-  const concat = op({concat_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sigmoid_(x) {
-    const $x = convertToTensor(x, "x", "sigmoid");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.sigmoid($x);
-      save([res]);
-      return res;
-    }, inputs, null, Sigmoid);
-  }
-  const sigmoid = op({sigmoid_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function slice_(x, begin, size) {
-    const $x = convertToTensor(x, "x", "slice");
-    if ($x.rank === 0) {
-      throw new Error("Slicing scalar is not possible");
-    }
-    const [begin_, size_] = parseSliceParams($x, begin, size);
-    assertParamsValid($x, begin_, size_);
-    const forward = (backend2, save) => {
-      save([$x]);
-      return backend2.slice($x, begin_, size_);
-    };
-    const inputs = {x: $x};
-    const attrs = {begin, size};
-    return ENGINE.runKernelFunc(forward, inputs, null, Slice, attrs);
-  }
-  const slice = op({slice_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tanh_(x) {
-    const $x = convertToTensor(x, "x", "tanh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const y = backend2.tanh($x);
-      save([y]);
-      return y;
-    }, inputs, null, Tanh);
-  }
-  const tanh2 = op({tanh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
-    const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
-    const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
-    const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
-    const $data = convertToTensor(data, "data", "basicLSTMCell");
-    const $c = convertToTensor(c, "c", "basicLSTMCell");
-    const $h = convertToTensor(h, "h", "basicLSTMCell");
-    const combined = concat([$data, $h], 1);
-    const weighted = matMul(combined, $lstmKernel);
-    const res = add2(weighted, $lstmBias);
-    const batchSize = res.shape[0];
-    const sliceCols = res.shape[1] / 4;
-    const sliceSize = [batchSize, sliceCols];
-    const i = slice(res, [0, 0], sliceSize);
-    const j = slice(res, [0, sliceCols], sliceSize);
-    const f = slice(res, [0, sliceCols * 2], sliceSize);
-    const o = slice(res, [0, sliceCols * 3], sliceSize);
-    const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
-    const newH = mul(tanh2(newC), sigmoid(o));
-    return [newC, newH];
-  }
-  const basicLSTMCell = op({basicLSTMCell_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function batchToSpaceND_(x, blockShape, crops) {
-    const $x = convertToTensor(x, "x", "batchToSpaceND");
-    const prod3 = blockShape.reduce((a, b) => a * b);
-    assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
-    assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
-    assert($x.shape[0] % prod3 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod3}`);
-    const forward = (backend2) => {
-      return backend2.batchToSpaceND($x, blockShape, crops);
-    };
-    const inputs = {x: $x};
-    const attrs = {blockShape, crops};
-    return ENGINE.runKernelFunc(forward, inputs, null, BatchToSpaceND, attrs);
-  }
-  const batchToSpaceND = op({batchToSpaceND_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
-  function xAs4D(x) {
-    let x4D;
-    if (x.rank === 0 || x.rank === 1) {
-      x4D = reshape(x, [1, 1, 1, x.size]);
-    } else if (x.rank === 2) {
-      x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-    } else if (x.rank === 3) {
-      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-    } else {
-      x4D = x;
-    }
-    return x4D;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    if (varianceEpsilon == null) {
-      varianceEpsilon = 1e-3;
-    }
-    const $x = convertToTensor(x, "x", "batchNorm");
-    const $mean = convertToTensor(mean5, "mean", "batchNorm");
-    const $variance = convertToTensor(variance, "variance", "batchNorm");
-    let $scale;
-    if (scale2 != null) {
-      $scale = convertToTensor(scale2, "scale", "batchNorm");
-    }
-    let $offset;
-    if (offset != null) {
-      $offset = convertToTensor(offset, "offset", "batchNorm");
-    }
-    assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
-    assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
-    assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-    const x4D = xAs4D($x);
-    const forward = (backend2, save) => {
-      save([x4D, $mean, $variance, $scale]);
-      return backend2.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);
-    };
-    const inputs = {
-      x: x4D,
-      scale: $scale,
-      offset: $offset,
-      mean: $mean,
-      variance: $variance
-    };
-    const attrs = {varianceEpsilon};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, FusedBatchNorm, attrs);
-    return reshape(res, $x.shape);
-  }
-  function as1DOr4D(x) {
-    if (x == null) {
-      return null;
-    }
-    if (x.rank === 0) {
-      return reshape(x, [x.size]);
-    } else if (x.rank === 1) {
-      return x;
-    } else if (x.rank === 2) {
-      return reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-    } else if (x.rank === 3) {
-      return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-    }
-    return x;
-  }
-  const batchNorm = op({batchNorm_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
-  function batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    const $x = convertToTensor(x, "x", "batchNorm");
-    const $mean = convertToTensor(mean5, "mean", "batchNorm");
-    const $variance = convertToTensor(variance, "variance", "batchNorm");
-    let $scale;
-    if (scale2 != null) {
-      $scale = convertToTensor(scale2, "scale", "batchNorm");
-    }
-    let $offset;
-    if (offset != null) {
-      $offset = convertToTensor(offset, "offset", "batchNorm");
-    }
-    assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
-    assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
-    assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
-    if ($scale != null) {
-      assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
-    }
-    if ($offset != null) {
-      assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
-    }
-    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-  }
-  const batchNorm2d = op({batchNorm2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
-  function batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    const $x = convertToTensor(x, "x", "batchNorm");
-    const $mean = convertToTensor(mean5, "mean", "batchNorm");
-    const $variance = convertToTensor(variance, "variance", "batchNorm");
-    let $scale;
-    if (scale2 != null) {
-      $scale = convertToTensor(scale2, "scale", "batchNorm");
-    }
-    let $offset;
-    if (offset != null) {
-      $offset = convertToTensor(offset, "offset", "batchNorm");
-    }
-    assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
-    assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
-    assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
-    if ($scale != null) {
-      assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
-    }
-    if ($offset != null) {
-      assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
-    }
-    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-  }
-  const batchNorm3d = op({batchNorm3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
-  function batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    const $x = convertToTensor(x, "x", "batchNorm");
-    const $mean = convertToTensor(mean5, "mean", "batchNorm");
-    const $variance = convertToTensor(variance, "variance", "batchNorm");
-    let $scale;
-    if (scale2 != null) {
-      $scale = convertToTensor(scale2, "scale", "batchNorm");
-    }
-    let $offset;
-    if (offset != null) {
-      $offset = convertToTensor(offset, "offset", "batchNorm");
-    }
-    assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
-    assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
-    assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
-    if ($scale != null) {
-      assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
-    }
-    if ($offset != null) {
-      assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
-    }
-    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-  }
-  const batchNorm4d = op({batchNorm4d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function broadcastTo_(x, shape) {
-    let input = convertToTensor(x, "broadcastTo", "x");
-    const xShape = input.shape;
-    if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
-      throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
-    }
-    if (shape.length < input.rank) {
-      throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);
-    }
-    if (shape.length > input.rank) {
-      const newShape = input.shape.slice();
-      while (newShape.length < shape.length) {
-        newShape.unshift(1);
-      }
-      input = reshape(input, newShape);
-    }
-    const inputShape = input.shape;
-    const reps = Array.from(shape);
-    for (let i = shape.length - 1; i >= 0; i--) {
-      if (inputShape[i] === shape[i]) {
-        reps[i] = 1;
-      } else if (input.shape[i] !== 1) {
-        throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
-      }
-    }
-    const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
-    if (axes.length === 0) {
-      return clone(input);
-    }
-    const forward = (backend2) => backend2.tile(input, reps);
-    const inputs = {x: input};
-    const attrs = {shape, inputShape};
-    return ENGINE.runKernelFunc(forward, inputs, null, BroadcastTo, attrs);
-  }
-  const broadcastTo = op({broadcastTo_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function ceil_(x) {
-    const $x = convertToTensor(x, "x", "ceil");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.ceil($x), inputs, null, Ceil);
-  }
-  const ceil = op({ceil_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function clipByValue_(x, clipValueMin, clipValueMax) {
-    const $x = convertToTensor(x, "x", "clipByValue");
-    assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
-    const inputs = {x: $x};
-    const attrs = {clipValueMin, clipValueMax};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.clip($x, clipValueMin, clipValueMax);
-      save([$x]);
-      return res;
-    }, inputs, null, ClipByValue, attrs);
-  }
-  const clipByValue = op({clipByValue_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
-  function concat1d_(tensors) {
-    return concat(tensors, 0);
-  }
-  const concat1d = op({concat1d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
-  function concat2d_(tensors, axis) {
-    return concat(tensors, axis);
-  }
-  const concat2d = op({concat2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
-  function concat3d_(tensors, axis) {
-    return concat(tensors, axis);
-  }
-  const concat3d = op({concat3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
-  function concat4d_(tensors, axis) {
-    return concat(tensors, axis);
-  }
-  const concat4d = op({concat4d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "conv2d");
-    const $filter = convertToTensor(filter, "filter", "conv2d");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-    assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-    assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    const forward = (backend2, save) => {
-      const $dataFormat = convertConv2DDataFormat(dataFormat);
-      const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-      const res2 = backend2.conv2d(x4D, $filter, convInfo);
-      save([x4D, $filter]);
-      return res2;
-    };
-    const inputs = {x: x4D, filter: $filter};
-    const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2D, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const conv2d = op({conv2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
-  function conv1d_(x, filter, stride, pad8, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "conv1d");
-    const $filter = convertToTensor(filter, "filter", "conv1d");
-    let x3D = $x;
-    let reshapedTo3D = false;
-    if ($x.rank === 2) {
-      reshapedTo3D = true;
-      x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
-    }
-    assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
-    assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
-    assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
-    assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
-    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
-    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
-    const strides = [1, stride];
-    const dilations = [1, dilation];
-    const conv2dDataFormat = "NHWC";
-    const res = conv2d(input4D, filter4D, strides, pad8, conv2dDataFormat, dilations, dimRoundingMode);
-    if (reshapedTo3D) {
-      return reshape(res, [res.shape[2], res.shape[3]]);
-    }
-    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
-  }
-  const conv1d = op({conv1d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv2DBackpropInput_(xShape, dy, filter, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-    assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-    let xShape4D = xShape;
-    let dy4D = dy;
-    let reshapedTo4D = false;
-    if (dy.rank === 3) {
-      reshapedTo4D = true;
-      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-      xShape4D = [1, xShape[0], xShape[1], xShape[2]];
-    }
-    assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
-    assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
-    assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
-    const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
-    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-    assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
-    assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      const dilations = 1;
-      const $dataFormat = convertConv2DDataFormat(dataFormat);
-      const convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-      const res2 = backend2.conv2dDerInput(dy4D, filter, convInfo);
-      save([dy4D, filter]);
-      return res2;
-    };
-    const inputs = {dy: dy4D, filter};
-    const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape: xShape4D};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropInput, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const conv2DBackpropInput = op({conv2DBackpropInput_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
-  function conv2dTranspose_(x, filter, outputShape, strides, pad8, dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "conv2dTranspose");
-    const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
-    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad8, "NHWC", dimRoundingMode);
-  }
-  const conv2dTranspose = op({conv2dTranspose_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv3d_(x, filter, strides, pad8, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
-    const $x = convertToTensor(x, "x", "conv3d");
-    const $filter = convertToTensor(filter, "filter", "conv3d");
-    let x5D = $x;
-    let reshapedTo5D = false;
-    if ($x.rank === 4) {
-      reshapedTo5D = true;
-      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-    }
-    assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
-    assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
-    assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
-    const forward = (backend2, save) => {
-      const convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad8);
-      const res2 = backend2.conv3d(x5D, $filter, convInfo);
-      save([x5D, $filter]);
-      return res2;
-    };
-    const inputs = {x: x5D, filter: $filter};
-    const attrs = {strides, pad: pad8, dataFormat, dilations};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3D, attrs);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const conv3d = op({conv3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv3DBackpropInput_(xShape, dy, filter, strides, pad8) {
-    assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-    let xShape5D = xShape;
-    let dy5D = dy;
-    let reshapedTo5D = false;
-    if (dy.rank === 4) {
-      reshapedTo5D = true;
-      dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-      xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
-    }
-    const inDepth = xShape5D[4];
-    const outDepth = dy5D.shape[4];
-    assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
-    assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
-    assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
-    assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
-    assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
-    const forward = (backend2) => {
-      const dilations = 1;
-      const convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad8);
-      return backend2.conv3dDerInput(dy5D, filter, convInfo);
-    };
-    const inputs = {dy: dy5D};
-    const attrs = {pad: pad8};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const conv3DBackpropInput = op({conv3DBackpropInput_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
-  function conv3dTranspose_(x, filter, outputShape, strides, pad8) {
-    const $x = convertToTensor(x, "x", "conv3dTranspose");
-    const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
-    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad8);
-  }
-  const conv3dTranspose = op({conv3dTranspose_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function cos_(x) {
-    const $x = convertToTensor(x, "x", "cos");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.cos($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Cos);
-  }
-  const cos = op({cos_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function cosh_(x) {
-    const $x = convertToTensor(x, "x", "cosh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.cosh($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Cosh);
-  }
-  const cosh = op({cosh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function cumsum_(x, axis = 0, exclusive = false, reverse9 = false) {
-    const $x = convertToTensor(x, "x", "cumsum");
-    const forward = (backend2, save) => {
-      const permutation = getAxesPermutation([axis], $x.rank);
-      let permutedX = $x;
-      if (permutation != null) {
-        permutedX = transpose($x, permutation);
-      }
-      const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
-      let value = backend2.cumsum(permutedX, permutedAxis, exclusive, reverse9);
-      save([$x]);
-      if (permutation != null) {
-        const reversePermutation = getUndoAxesPermutation(permutation);
-        value = transpose(value, reversePermutation);
-      }
-      return value;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, exclusive, reverse: reverse9};
-    return ENGINE.runKernelFunc(forward, inputs, null, Cumsum, attrs);
-  }
-  const cumsum = op({cumsum_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
-    const $x = convertToTensor(x, "x", "depthToSpace");
-    const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
-    const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
-    const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
-    assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
-    ${$x.shape}`);
-    assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`);
-    assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
-    const forward = (backend2) => backend2.depthToSpace($x, blockSize, dataFormat);
-    const inputs = {x: $x};
-    const attrs = {blockSize, dataFormat};
-    return ENGINE.runKernelFunc(forward, inputs, null, DepthToSpace, attrs);
-  }
-  const depthToSpace = op({depthToSpace_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function depthwiseConv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "depthwiseConv2d");
-    const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-    assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-    assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      if (dilations == null) {
-        dilations = [1, 1];
-      }
-      assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-      const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-      const res2 = backend2.depthwiseConv2D(x4D, $filter, convInfo);
-      save([x4D, $filter]);
-      return res2;
-    };
-    const inputs = {x: x4D, filter: $filter};
-    const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNative, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const depthwiseConv2d = op({depthwiseConv2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function diag_(x) {
-    const $x = convertToTensor(x, "x", "diag");
-    const forward = (backend2) => {
-      const flat = reshape($x, [$x.size]);
-      const result = backend2.diag(flat);
-      const outShape = [...x.shape, ...x.shape];
-      return reshape(result, outShape);
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Diag);
-  }
-  const diag = op({diag_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function dilation2d_(x, filter, strides, pad8, dilations = [1, 1], dataFormat = "NHWC") {
-    const $x = convertToTensor(x, "x", "dilation2d");
-    const $filter = convertToTensor(filter, "filter", "dilation2d");
-    assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
-    assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
-    assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-      reshapedTo4D = true;
-    }
-    const inputs = {x: x4D, filter: $filter};
-    const attrs = {strides, pad: pad8, dilations};
-    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const dilation2d = op({dilation2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getBroadcastDims(inShape, outShape) {
-    const inRank = inShape.length;
-    const dims = [];
-    for (let i = 0; i < inRank; i++) {
-      const dim = inRank - 1 - i;
-      const a = inShape[dim] || 1;
-      const b = outShape[outShape.length - 1 - i] || 1;
-      if (b > 1 && a === 1) {
-        dims.unshift(dim);
-      }
-    }
-    return dims;
-  }
-  function getReductionAxes(inShape, outShape) {
-    const result = [];
-    for (let i = 0; i < outShape.length; i++) {
-      const inDim = inShape[inShape.length - i - 1];
-      const outAxis = outShape.length - i - 1;
-      const outDim = outShape[outAxis];
-      if (inDim == null || inDim === 1 && outDim > 1) {
-        result.unshift(outAxis);
-      }
-    }
-    return result;
-  }
-  function assertAndGetBroadcastShape(shapeA, shapeB) {
-    const result = [];
-    const l = Math.max(shapeA.length, shapeB.length);
-    for (let i = 0; i < l; i++) {
-      let a = shapeA[shapeA.length - i - 1];
-      if (a == null) {
-        a = 1;
-      }
-      let b = shapeB[shapeB.length - i - 1];
-      if (b == null) {
-        b = 1;
-      }
-      if (a === 1) {
-        result.unshift(b);
-      } else if (b === 1) {
-        result.unshift(a);
-      } else if (a !== b) {
-        const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
-        throw Error(errMsg);
-      } else {
-        result.unshift(a);
-      }
-    }
-    return result;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function equal_(a, b) {
-    let $a = convertToTensor(a, "a", "equal");
-    let $b = convertToTensor(b, "b", "equal");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2) => backend2.equal($a, $b);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Equal);
-  }
-  const equal = op({equal_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/where.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function where_(condition, a, b) {
-    const $a = convertToTensor(a, "a", "where");
-    const $b = convertToTensor(b, "b", "where");
-    const $condition = convertToTensor(condition, "condition", "where", "bool");
-    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
-    const $broadcastedA = broadcastTo($a, broadcastShape);
-    const $broadcastedB = broadcastTo($b, broadcastShape);
-    if ($condition.rank === 1) {
-      assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
-    }
-    if ($condition.rank !== 1) {
-      assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
-    }
-    const forward = (backend2, save) => {
-      const res = backend2.select($condition, $broadcastedA, $broadcastedB);
-      save([$condition]);
-      return res;
-    };
-    const inputs = {
-      condition: $condition,
-      t: $broadcastedA,
-      e: $broadcastedB
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, SelectV2);
-  }
-  const where = op({where_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function zerosLike_(x) {
-    const $x = convertToTensor(x, "x", "zerosLike");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.zerosLike($x), inputs, null, ZerosLike);
-  }
-  const zerosLike = op({zerosLike_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function divNoNan_(a, b) {
-    let $a = convertToTensor(a, "a", "div");
-    let $b = convertToTensor(b, "b", "div");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const divResult = div($a, $b);
-    const zeros9 = zerosLike(divResult);
-    const bEqualsZero = equal($b, zeros9);
-    return where(bEqualsZero, zeros9, divResult);
-  }
-  const divNoNan = op({divNoNan_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function dot_(t1, t2) {
-    const $t1 = convertToTensor(t1, "t1", "dot");
-    const $t2 = convertToTensor(t2, "t2", "dot");
-    assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
-    const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
-    const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
-    assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
-    if ($t1.rank === 1 && $t2.rank === 1) {
-      const t12D = reshape($t1, [1, -1]);
-      const t22D = reshape($t2, [-1, 1]);
-      const t1t2 = matMul(t12D, t22D);
-      return reshape(t1t2, []);
-    } else if ($t1.rank === 1 && $t2.rank === 2) {
-      const t12D = reshape($t1, [1, -1]);
-      const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-      const t1t2 = matMul(t12D, t22D);
-      return reshape(t1t2, [t1t2.size]);
-    } else if ($t1.rank === 2 && $t2.rank === 1) {
-      const t22D = reshape($t2, [-1, 1]);
-      const t1t2 = matMul($t1, t22D);
-      return reshape(t1t2, [t1t2.size]);
-    } else {
-      const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-      const t1t2 = matMul($t1, t22D);
-      return t1t2;
-    }
-  }
-  const dot = op({dot_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function elu_(x) {
-    const $x = convertToTensor(x, "x", "elu");
-    const forward = (backend2, save) => {
-      const y = backend2.elu($x);
-      save([y]);
-      return y;
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Elu);
-  }
-  const elu = op({elu_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function erf_(x) {
-    let $x = convertToTensor(x, "x", "erf");
-    assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
-    if ($x.dtype === "int32") {
-      $x = cast($x, "float32");
-    }
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.erf($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Erf);
-  }
-  const erf = op({erf_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function exp_(x) {
-    const $x = convertToTensor(x, "x", "exp");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.exp($x);
-      save([res]);
-      return res;
-    }, inputs, null, Exp);
-  }
-  const exp = op({exp_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function expandDims_(x, axis = 0) {
-    const parseAs = null;
-    const $x = convertToTensor(x, "x", "expandDims", parseAs);
-    assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
-    const newShape = $x.shape.slice();
-    if (axis < 0) {
-      assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);
-      axis = $x.rank + axis + 1;
-    }
-    newShape.splice(axis, 0, 1);
-    return reshape($x, newShape);
-  }
-  const expandDims = op({expandDims_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function expm1_(x) {
-    const $x = convertToTensor(x, "x", "expm1");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.expm1($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Expm1);
-  }
-  const expm1 = op({expm1_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tile_(x, reps) {
-    const parseAs = null;
-    const $x = convertToTensor(x, "x", "tile", parseAs);
-    assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
-    const forward = (backend2, save) => {
-      const res = backend2.tile($x, reps);
-      save([$x]);
-      return res;
-    };
-    const inputsToSave = [$x];
-    const inputs = {x: $x};
-    const attrs = {reps};
-    return ENGINE.runKernelFunc(forward, inputs, null, Tile, attrs, inputsToSave);
-  }
-  const tile = op({tile_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function eye_(numRows, numColumns, batchShape, dtype = "float32") {
-    if (numColumns == null) {
-      numColumns = numRows;
-    }
-    const buff = buffer([numRows, numColumns], dtype);
-    const n = numRows <= numColumns ? numRows : numColumns;
-    for (let i = 0; i < n; ++i) {
-      buff.set(1, i, i);
-    }
-    const out = reshape(buff.toTensor(), [numRows, numColumns]);
-    if (batchShape == null) {
-      return out;
-    } else {
-      if (batchShape.length === 1) {
-        return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
-      } else if (batchShape.length === 2) {
-        return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
-      } else if (batchShape.length === 3) {
-        return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
-          batchShape[0],
-          batchShape[1],
-          batchShape[2],
-          1,
-          1
-        ]);
-      } else {
-        throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
-      }
-    }
-  }
-  const eye = op({eye_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function fill(shape, value, dtype) {
-    const attrs = {shape, value, dtype};
-    return ENGINE.runKernelFunc((backend2) => backend2.fill(shape, value, dtype), {}, null, Fill, attrs);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function floor_(x) {
-    const $x = convertToTensor(x, "x", "floor");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.floor($x), inputs, null, Floor);
-  }
-  const floor = op({floor_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const PARALLELIZE_THRESHOLD = 30;
-  function computeOptimalWindowSize(inSize) {
-    if (inSize <= PARALLELIZE_THRESHOLD) {
-      return inSize;
-    }
-    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
-  const segment_util_exports = {};
-  __export(segment_util_exports, {
-    collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
-    computeOutShape: () => computeOutShape3,
-    segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function segOpComputeOptimalWindowSize(inSize, numSegments) {
-    let done = false;
-    let res;
-    if (inSize <= PARALLELIZE_THRESHOLD) {
-      res = inSize;
-      done = true;
-    } else {
-      res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-    }
-    while (!done) {
-      if (res > numSegments || res === inSize) {
-        done = true;
-      } else {
-        res = nearestDivisor(inSize, res + 1);
-      }
-    }
-    return res;
-  }
-  function computeOutShape3(aShape, axis, numSegments) {
-    const outShape = [];
-    const rank = aShape.length;
-    for (let dim = 0; dim < rank; dim++) {
-      if (dim !== axis) {
-        outShape.push(aShape[dim]);
-      } else {
-        outShape.push(numSegments);
-      }
-    }
-    return outShape;
-  }
-  function collectGatherOpShapeInfo(x, indices, axis) {
-    const dimSize = x.shape[axis];
-    const outputShape = [];
-    let batchSize = 1;
-    let sliceSize = 1;
-    for (let i = 0; i < axis; i++) {
-      outputShape.push(x.shape[i]);
-      batchSize *= x.shape[i];
-    }
-    for (let i = 0; i < indices.rank; i++) {
-      outputShape.push(indices.shape[i]);
-    }
-    for (let i = axis + 1; i < x.rank; i++) {
-      outputShape.push(x.shape[i]);
-      sliceSize *= x.shape[i];
-    }
-    return {batchSize, sliceSize, dimSize, outputShape};
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function gather_(x, indices, axis = 0) {
-    const $x = convertToTensor(x, "x", "gather");
-    const $indices = convertToTensor(indices, "indices", "gather", "int32");
-    const inputs = {x: $x, indices: $indices};
-    const attrs = {axis};
-    const forward = (backend2, save) => {
-      const parsedAxis = parseAxisParam(axis, $x.shape)[0];
-      const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);
-      const res = backend2.gather($x, reshape($indices, [$indices.size]), parsedAxis);
-      save([$x, $indices]);
-      return reshape(res, shapeInfo.outputShape);
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, GatherV2, attrs);
-  }
-  const gather = op({gather_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function greater_(a, b) {
-    let $a = convertToTensor(a, "a", "greater");
-    let $b = convertToTensor(b, "b", "greater");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2) => backend2.greater($a, $b);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Greater);
-  }
-  const greater = op({greater_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function greaterEqual_(a, b) {
-    let $a = convertToTensor(a, "a", "greaterEqual");
-    let $b = convertToTensor(b, "b", "greaterEqual");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2, save) => {
-      const res = backend2.greaterEqual($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, GreaterEqual);
-  }
-  const greaterEqual = op({greaterEqual_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function imag_(input) {
-    const $input = convertToTensor(input, "input", "imag");
-    const forward = (backend2) => {
-      return backend2.imag($input);
-    };
-    const inputs = {input: $input};
-    return ENGINE.runKernelFunc(forward, inputs, null, Imag);
-  }
-  const imag = op({imag_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function isFinite_(x) {
-    const $x = convertToTensor(x, "x", "isFinite");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.isFinite($x), inputs, null, IsFinite);
-  }
-  const isFinite2 = op({isFinite_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function isInf_(x) {
-    const $x = convertToTensor(x, "x", "isInf");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.isInf($x), inputs, null, IsInf);
-  }
-  const isInf = op({isInf_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function isNaN_(x) {
-    const $x = convertToTensor(x, "x", "isNaN");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.isNaN($x), inputs, null, IsNan);
-  }
-  const isNaN2 = op({isNaN_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maximum_(a, b) {
-    let $a = convertToTensor(a, "a", "maximum");
-    let $b = convertToTensor(b, "b", "maximum");
-    [$a, $b] = makeTypesMatch($a, $b);
-    if ($a.dtype === "bool") {
-      $a = cast($a, "int32");
-      $b = cast($b, "int32");
-    }
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2, save) => {
-      const res = backend2.maximum($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Maximum);
-  }
-  const maximum = op({maximum_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function scalar(value, dtype) {
-    if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
-      throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
-    }
-    if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
-      throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-    }
-    const shape = [];
-    const inferredShape = [];
-    return makeTensor(value, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function leakyRelu_(x, alpha = 0.2) {
-    const $x = convertToTensor(x, "x", "leakyRelu");
-    return maximum(mul(scalar(alpha), $x), $x);
-  }
-  const leakyRelu = op({leakyRelu_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/less.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function less_(a, b) {
-    let $a = convertToTensor(a, "a", "less");
-    let $b = convertToTensor(b, "b", "less");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2) => backend2.less($a, $b);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Less);
-  }
-  const less = op({less_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function lessEqual_(a, b) {
-    let $a = convertToTensor(a, "a", "lessEqual");
-    let $b = convertToTensor(b, "b", "lessEqual");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2, save) => {
-      const res = backend2.lessEqual($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, LessEqual);
-  }
-  const lessEqual = op({lessEqual_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function linspace(start, stop, num) {
-    if (num <= 0) {
-      throw new Error("The number of values should be positive.");
-    }
-    const attrs = {start, stop, num};
-    return ENGINE.runKernelFunc((backend2) => backend2.linspace(start, stop, num), {}, null, LinSpace, attrs);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-    const $x = convertToTensor(x, "x", "localResponseNormalization");
-    assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${$x.rank}.`);
-    assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    const forward = (backend2, save) => {
-      const y = backend2.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
-      save([x4D, y]);
-      return y;
-    };
-    const inputs = {x: x4D};
-    const attrs = {depthRadius, bias, alpha, beta};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, LRN, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    } else {
-      return res;
-    }
-  }
-  const localResponseNormalization = op({localResponseNormalization_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/log.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function log_(x) {
-    const $x = convertToTensor(x, "x", "log");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.log($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Log);
-  }
-  const log = op({log_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function log1p_(x) {
-    const $x = convertToTensor(x, "x", "log1p");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.log1p($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Log1p);
-  }
-  const log1p = op({log1p_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function grad(f) {
-    assert(isFunction(f), () => "The f passed in grad(f) must be a function");
-    return (x, dy) => {
-      const $x = convertToTensor(x, "x", "tf.grad", null);
-      const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
-      return ENGINE.tidy(() => {
-        const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
-        if ($dy != null) {
-          assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
-        }
-        checkGrads(grads2);
-        return grads2[0];
-      });
-    };
-  }
-  function grads(f) {
-    assert(isFunction(f), () => "The f passed in grads(f) must be a function");
-    return (args, dy) => {
-      assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-      const $args = convertToTensorArray(args, "args", "tf.grads", null);
-      const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
-      return ENGINE.tidy(() => {
-        const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
-        if ($dy != null) {
-          assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-        }
-        checkGrads(grads2);
-        return grads2;
-      });
-    };
-  }
-  function valueAndGrad(f) {
-    assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
-    return (x, dy) => {
-      assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
-      assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
-      const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
-      checkGrads(grads2);
-      return {grad: grads2[0], value};
-    };
-  }
-  function valueAndGrads(f) {
-    assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
-    return (args, dy) => {
-      assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
-      assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
-      const res = ENGINE.gradients(() => f(...args), args, dy);
-      if (dy != null) {
-        assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-      }
-      checkGrads(res.grads);
-      return res;
-    };
-  }
-  function variableGrads(f, varList) {
-    assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
-    assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
-    const specifiedVarList = varList != null;
-    if (!specifiedVarList) {
-      varList = [];
-      for (const varName in ENGINE.registeredVariables) {
-        varList.push(ENGINE.registeredVariables[varName]);
-      }
-    }
-    const specifiedNonTrainable = specifiedVarList ? varList.filter((variable3) => !variable3.trainable) : null;
-    const originalVarCount = varList.length;
-    varList = varList.filter((variable3) => variable3.trainable);
-    assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
-    const allowNoGradients = true;
-    const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
-    assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
-    assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
-    const namedGrads = {};
-    varList.forEach((v, i) => {
-      if (grads2[i] != null) {
-        namedGrads[v.name] = grads2[i];
-      }
-    });
-    if (specifiedNonTrainable != null) {
-      specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
-    }
-    return {value, grads: namedGrads};
-  }
-  function customGrad(f) {
-    return ENGINE.customGrad(f);
-  }
-  function checkGrads(grads2) {
-    const numNullGradients = grads2.filter((g) => g == null).length;
-    if (numNullGradients > 0) {
-      throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`);
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function neg_(x) {
-    const $x = convertToTensor(x, "x", "neg");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.neg($x), inputs, null, Negate);
-  }
-  const neg = op({neg_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function softplus_(x) {
-    const $x = convertToTensor(x, "x", "softplus");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.softplus($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Softplus);
-  }
-  const softplus = op({softplus_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logSigmoid_(x) {
-    const $x = convertToTensor(x, "x", "logSigmoid");
-    const customOp = customGrad((x2) => {
-      const value = neg(softplus(neg(x2)));
-      const gradFunc = (dy) => {
-        const derX = mul(dy, sigmoid(neg(x2)));
-        return derX;
-      };
-      return {value, gradFunc};
-    });
-    return customOp($x);
-  }
-  const logSigmoid = op({logSigmoid_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function max_(x, axis = null, keepDims = false) {
-    const $x = convertToTensor(x, "x", "max");
-    const forward = (backend2, save) => {
-      const origAxes = parseAxisParam(axis, $x.shape);
-      let axes = origAxes;
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      let maxInput = $x;
-      if (permutedAxes != null) {
-        maxInput = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, maxInput.rank);
-      }
-      const y = backend2.max(maxInput, axes);
-      if (permutedAxes != null) {
-        maxInput.dispose();
-      }
-      let res = y;
-      if (keepDims) {
-        const expandedShape = expandShapeToKeepDim(res.shape, parseAxisParam(axis, $x.shape));
-        res = reshape(res, expandedShape);
-        y.dispose();
-      }
-      save([$x, res]);
-      return res;
-    };
-    const inputs = {x: $x};
-    const attrs = {reductionIndices: axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, Max, attrs);
-  }
-  const max = op({max_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sub_(a, b) {
-    let $a = convertToTensor(a, "a", "sub");
-    let $b = convertToTensor(b, "b", "sub");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.subtract($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Sub);
-  }
-  const sub = op({sub_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sum_(x, axis = null, keepDims = false) {
-    let $x = convertToTensor(x, "x", "sum");
-    if ($x.dtype === "bool") {
-      $x = cast($x, "int32");
-    }
-    const forward = (backend2, save) => {
-      save([$x]);
-      const axes = parseAxisParam(axis, $x.shape);
-      const permutation = getAxesPermutation(axes, $x.rank);
-      let reductionAxes = axes;
-      let permutedX = $x;
-      if (permutation != null) {
-        permutedX = transpose($x, permutation);
-        reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-      }
-      let value = backend2.sum(permutedX, reductionAxes);
-      if (keepDims) {
-        const newShape = expandShapeToKeepDim(value.shape, axes);
-        value = reshape(value, newShape);
-      }
-      return value;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, Sum, attrs);
-  }
-  const sum2 = op({sum_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logSoftmax_(logits, axis = -1) {
-    const $logits = convertToTensor(logits, "logits", "logSoftmax");
-    if (axis === -1) {
-      axis = $logits.rank - 1;
-    }
-    if (axis !== $logits.rank - 1) {
-      throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
-    }
-    const forward = (backend2, save) => {
-      const keepDims = true;
-      const xMax = max(logits, axis, true);
-      const shifted = sub(logits, xMax);
-      const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
-      save([value]);
-      return value;
-    };
-    const inputs = {logits: $logits};
-    const attrs = {axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, LogSoftmax, attrs);
-  }
-  const logSoftmax = op({logSoftmax_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logSumExp_(x, axis = null, keepDims = false) {
-    const $x = convertToTensor(x, "x", "logSumExp");
-    const axes = parseAxisParam(axis, $x.shape);
-    const xMax = max($x, axes, true);
-    const a = sub($x, xMax);
-    const b = exp(a);
-    const c = sum2(b, axes);
-    const d = log(c);
-    const res = add2(reshape(xMax, d.shape), d);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, axes);
-      return reshape(res, newShape);
-    }
-    return res;
-  }
-  const logSumExp = op({logSumExp_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logicalAnd_(a, b) {
-    const $a = convertToTensor(a, "a", "logicalAnd", "bool");
-    const $b = convertToTensor(b, "b", "logicalAnd", "bool");
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc((backend2) => backend2.logicalAnd($a, $b), inputs, null, LogicalAnd);
-  }
-  const logicalAnd = op({logicalAnd_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logicalNot_(x) {
-    const $x = convertToTensor(x, "x", "logicalNot", "bool");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.logicalNot($x), inputs, null, LogicalNot);
-  }
-  const logicalNot = op({logicalNot_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logicalOr_(a, b) {
-    const $a = convertToTensor(a, "a", "logicalOr", "bool");
-    const $b = convertToTensor(b, "b", "logicalOr", "bool");
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc((backend2) => backend2.logicalOr($a, $b), inputs, null, LogicalOr);
-  }
-  const logicalOr = op({logicalOr_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logicalXor_(a, b) {
-    const $a = convertToTensor(a, "a", "logicalXor", "bool");
-    const $b = convertToTensor(b, "b", "logicalXor", "bool");
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
-  }
-  const logicalXor = op({logicalXor_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maxPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-    const $x = convertToTensor(x, "x", "maxPool");
-    const dilations = 1;
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-      let y;
-      if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-        y = x4D.clone();
-      } else {
-        y = backend2.maxPool(x4D, convInfo);
-      }
-      save([x4D, y]);
-      return y;
-    };
-    const inputs = {x: x4D};
-    const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const maxPool = op({maxPool_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    } else {
-      deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-    }
-    const $x = convertToTensor(x, "x", "maxPool3d");
-    let x5D = $x;
-    let reshapedTo5D = false;
-    if ($x.rank === 4) {
-      reshapedTo5D = true;
-      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-    }
-    assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-    assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2, save) => {
-      if (dilations == null) {
-        dilations = [1, 1, 1];
-      }
-      const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-      const y = backend2.maxPool3d(x5D, convInfo);
-      save([x5D, y]);
-      return y;
-    };
-    const inputs = {x: x5D};
-    const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3D, attrs);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const maxPool3d = op({maxPool3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maxPoolWithArgmax_(x, filterSize, strides, pad8, includeBatchInIndex = false) {
-    const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
-    const inputs = {x: $x};
-    const attrs = {filterSize, strides, pad: pad8, includeBatchInIndex};
-    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
-    return {result: result[0], indexes: result[1]};
-  }
-  const maxPoolWithArgmax = op({maxPoolWithArgmax_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function zeros(shape, dtype = "float32") {
-    if (dtype === "complex64") {
-      const real6 = zeros(shape, "float32");
-      const imag6 = zeros(shape, "float32");
-      return complex(real6, imag6);
-    }
-    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
-    return ENGINE.makeTensor(values, shape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function ones2(shape, dtype = "float32") {
-    if (dtype === "complex64") {
-      const real6 = ones2(shape, "float32");
-      const imag6 = zeros(shape, "float32");
-      return complex(real6, imag6);
-    }
-    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
-    return ENGINE.makeTensor(values, shape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function mean_(x, axis = null, keepDims = false) {
-    const $x = convertToTensor(x, "x", "mean");
-    const axes = parseAxisParam(axis, $x.shape);
-    const shapes = computeOutAndReduceShapes($x.shape, axes);
-    const reduceShape = shapes[1];
-    const reduceSize = sizeFromShape(reduceShape);
-    const customOp = customGrad((x2) => {
-      const reduceSizeScalar = scalar(reduceSize);
-      const xReduce = reduceSizeScalar.dtype === x2.dtype ? x2 : cast(x2, reduceSizeScalar.dtype);
-      const res = div(xReduce, reduceSizeScalar);
-      const value = sum2(res, axis, keepDims);
-      const gradFunc = (dy) => {
-        const expandedDyShape = x2.shape.slice();
-        axes.forEach((axis2) => {
-          expandedDyShape[axis2] = 1;
-        });
-        const expandedDy = reshape(dy, expandedDyShape);
-        const derX = div(mul(expandedDy, ones2(x2.shape, "float32")), reduceSize);
-        return derX;
-      };
-      return {value, gradFunc};
-    });
-    return customOp($x);
-  }
-  const mean = op({mean_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/min.js
-  function min_(x, axis = null, keepDims = false) {
-    const $x = convertToTensor(x, "x", "min");
-    const forward = (backend2, save) => {
-      const origAxes = parseAxisParam(axis, $x.shape);
-      let axes = origAxes;
-      const permutedAxes = getAxesPermutation(axes, $x.rank);
-      let minInput = $x;
-      if (permutedAxes != null) {
-        minInput = transpose($x, permutedAxes);
-        axes = getInnerMostAxes(axes.length, $x.rank);
-      }
-      const y = backend2.min(minInput, axes);
-      if (permutedAxes != null) {
-        minInput.dispose();
-      }
-      let res = y;
-      if (keepDims) {
-        const expandedShape = expandShapeToKeepDim(res.shape, origAxes);
-        res = reshape(y, expandedShape);
-        y.dispose();
-      }
-      save([$x, res]);
-      return res;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, Min, attrs);
-  }
-  const min = op({min_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function minimum_(a, b) {
-    let $a = convertToTensor(a, "a", "minimum");
-    let $b = convertToTensor(b, "b", "minimum");
-    [$a, $b] = makeTypesMatch($a, $b);
-    if ($a.dtype === "bool") {
-      $a = cast($a, "int32");
-      $b = cast($b, "int32");
-    }
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2, save) => {
-      const res = backend2.minimum($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Minimum);
-  }
-  const minimum = op({minimum_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function mod_(a, b) {
-    let $a = convertToTensor(a, "a", "mod");
-    let $b = convertToTensor(b, "b", "mod");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const forward = (backend2, save) => {
-      const res = backend2.mod($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, Mod);
-  }
-  const mod = op({mod_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/square.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function square_(x) {
-    const $x = convertToTensor(x, "x", "square");
-    const attrs = {};
-    const inputsToSave = [$x];
-    const outputsToSave = [];
-    return ENGINE.runKernelFunc((backend2, save) => {
-      save([$x]);
-      return backend2.square($x);
-    }, {x: $x}, null, "Square", attrs, inputsToSave, outputsToSave);
-  }
-  const square = op({square_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function moments_(x, axis = null, keepDims = false) {
-    x = convertToTensor(x, "x", "moments");
-    const axes = parseAxisParam(axis, x.shape);
-    const xMean = mean(x, axes, keepDims);
-    let keepDimsShape = xMean.shape;
-    if (!keepDims) {
-      keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
-    }
-    const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
-    const variance = mean(devSquared, axes, keepDims);
-    return {mean: xMean, variance};
-  }
-  const moments = op({moments_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
-  function multiRNNCell_(lstmCells, data, c, h) {
-    const $data = convertToTensor(data, "data", "multiRNNCell");
-    const $c = convertToTensorArray(c, "c", "multiRNNCell");
-    const $h = convertToTensorArray(h, "h", "multiRNNCell");
-    let input = $data;
-    const newStates = [];
-    for (let i = 0; i < lstmCells.length; i++) {
-      const output = lstmCells[i](input, $c[i], $h[i]);
-      newStates.push(output[0]);
-      newStates.push(output[1]);
-      input = output[1];
-    }
-    const newC = [];
-    const newH = [];
-    for (let i = 0; i < newStates.length; i += 2) {
-      newC.push(newStates[i]);
-      newH.push(newStates[i + 1]);
-    }
-    return [newC, newH];
-  }
-  const multiRNNCell = op({multiRNNCell_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function multinomial_(logits, numSamples, seed, normalized = false) {
-    const $logits = convertToTensor(logits, "logits", "multinomial");
-    const numOutcomes = $logits.size;
-    const origRank = $logits.rank;
-    if (numOutcomes < 2) {
-      throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
-    }
-    if (origRank > 2) {
-      throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
-    }
-    seed = seed || Math.random();
-    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
-    const res = ENGINE.runKernelFunc((backend2) => backend2.multinomial(logits2D, normalized, numSamples, seed), {logits2D});
-    return origRank === 1 ? reshape(res, [res.size]) : res;
-  }
-  const multinomial = op({multinomial_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function notEqual_(a, b) {
-    let $a = convertToTensor(a, "a", "notEqual");
-    let $b = convertToTensor(b, "b", "notEqual");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2) => backend2.notEqual($a, $b);
-    const inputs = {a: $a, b: $b};
-    return ENGINE.runKernelFunc(forward, inputs, null, NotEqual);
-  }
-  const notEqual = op({notEqual_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/real.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function real_(input) {
-    const $input = convertToTensor(input, "input", "real");
-    const forward = (backend2) => {
-      return backend2.real($input);
-    };
-    const inputs = {input: $input};
-    return ENGINE.runKernelFunc(forward, inputs, null, Real);
-  }
-  const real = op({real_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function onesLike_(x) {
-    const $x = convertToTensor(x, "x", "onesLike");
-    const forward = (backend2, save) => {
-      if ($x.dtype === "complex64") {
-        const r = onesLike(real($x));
-        const i = zerosLike(imag($x));
-        return complex(r, i);
-      }
-      return backend2.onesLike($x);
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, OnesLike);
-  }
-  const onesLike = op({onesLike_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
-  function outerProduct_(v1, v2) {
-    const $v1 = convertToTensor(v1, "v1", "outerProduct");
-    const $v2 = convertToTensor(v2, "v2", "outerProduct");
-    assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
-    const v12D = reshape($v1, [-1, 1]);
-    const v22D = reshape($v2, [1, -1]);
-    return matMul(v12D, v22D);
-  }
-  const outerProduct = op({outerProduct_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function pad_(x, paddings, constantValue = 0) {
-    const $x = convertToTensor(x, "x", "pad");
-    if ($x.rank === 0) {
-      throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
-    }
-    const forward = (backend2, save) => {
-      save([$x]);
-      return backend2.pad($x, paddings, constantValue);
-    };
-    const attrs = {paddings, constantValue};
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, PadV2, attrs);
-  }
-  const pad = op({pad_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
-  function pad1d_(x, paddings, constantValue = 0) {
-    assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
-    return pad(x, [paddings], constantValue);
-  }
-  const pad1d = op({pad1d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
-  function pad2d_(x, paddings, constantValue = 0) {
-    assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-    return pad(x, paddings, constantValue);
-  }
-  const pad2d = op({pad2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
-  function pad3d_(x, paddings, constantValue = 0) {
-    assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-    return pad(x, paddings, constantValue);
-  }
-  const pad3d = op({pad3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
-  function pad4d_(x, paddings, constantValue = 0) {
-    assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-    return pad(x, paddings, constantValue);
-  }
-  const pad4d = op({pad4d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function spaceToBatchND_(x, blockShape, paddings) {
-    const $x = convertToTensor(x, "x", "spaceToBatchND");
-    assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
-    assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-    assert($x.shape.reduce((a, b, i) => {
-      if (i > 0 && i <= blockShape.length) {
-        return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
-      }
-      return a;
-    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
-    const forward = (backend2) => backend2.spaceToBatchND($x, blockShape, paddings);
-    const inputs = {x: $x};
-    const attrs = {blockShape, paddings};
-    return ENGINE.runKernelFunc(forward, inputs, null, SpaceToBatchND, attrs);
-  }
-  const spaceToBatchND = op({spaceToBatchND_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function pool_(input, windowShape, poolingType, pad8, dilations, strides) {
-    if (dilations == null) {
-      dilations = [1, 1];
-    }
-    if (strides == null) {
-      strides = 1;
-    }
-    if (pad8 === 0) {
-      pad8 = "valid";
-    }
-    const $x = convertToTensor(input, "x", "maxPool");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad8);
-    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
-    let basePadding;
-    if (pad8 === "same") {
-      basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
-    } else {
-      basePadding = [[0, 0], [0, 0]];
-    }
-    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
-    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
-    const convertedPad = isDilationOne ? pad8 : "valid";
-    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
-    const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
-    const y = forwardOp();
-    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
-    const padStart = basePadding.map((b) => b[0]);
-    const origPadEnd = basePadding.map((b) => b[1]);
-    const fullInputShape = inputShape.concat(padStart, origPadEnd);
-    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
-    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
-    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
-    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
-    return [paddings, crops];
-  }
-  function withSpaceToBatchBasePaddings(filterShape, dilation) {
-    const dilatedFilterShape = filterShape.map((s, i) => {
-      return s + (s - 1) * (dilation[i] - 1);
-    });
-    const padExtraShape = dilatedFilterShape.map((s) => s - 1);
-    const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
-    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
-    return padExtraShape.map((_, i) => {
-      return [padExtraStart[i], padExtraEnd[i]];
-    });
-  }
-  const pool = op({pool_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function pow_(base, exp11) {
-    let $base = convertToTensor(base, "base", "pow");
-    let $exp = convertToTensor(exp11, "exp", "pow");
-    [$base, $exp] = makeTypesMatch($base, $exp);
-    const inputs = {a: $base, b: $exp};
-    const forward = (backend2, save) => {
-      const y = backend2.pow($base, $exp);
-      save([$base, $exp, y]);
-      return y;
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, Pow);
-  }
-  const pow = op({pow_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function prelu_(x, alpha) {
-    const $x = convertToTensor(x, "x", "prelu");
-    const $alpha = convertToTensor(alpha, "alpha", "prelu");
-    const forward = (backend2, save) => {
-      const res = backend2.prelu($x, $alpha);
-      save([$x, $alpha]);
-      return res;
-    };
-    const inputs = {x: $x, alpha: $alpha};
-    return ENGINE.runKernelFunc(forward, inputs, null, Prelu);
-  }
-  const prelu = op({prelu_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function prod_(x, axis = null, keepDims = false) {
-    let $x = convertToTensor(x, "x", "prod");
-    const forward = (backend2) => {
-      if ($x.dtype === "bool") {
-        $x = cast($x, "int32");
-      }
-      const axes = parseAxisParam(axis, $x.shape);
-      const permutation = getAxesPermutation(axes, $x.rank);
-      let reductionAxes = axes;
-      let permutedX = $x;
-      if (permutation != null) {
-        permutedX = transpose($x, permutation);
-        reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-      }
-      let value = backend2.prod(permutedX, reductionAxes);
-      if (keepDims) {
-        const newShape = expandShapeToKeepDim(value.shape, axes);
-        value = reshape(value, newShape);
-      }
-      return value;
-    };
-    const inputs = {x: $x};
-    const attrs = {axis, keepDims};
-    return ENGINE.runKernelFunc(forward, inputs, null, Prod, attrs);
-  }
-  const prod = op({prod_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function rand_(shape, randFunction, dtype) {
-    const size = sizeFromShape(shape);
-    let values = null;
-    if (dtype == null || dtype === "float32") {
-      values = new Float32Array(size);
-    } else if (dtype === "int32") {
-      values = new Int32Array(size);
-    } else if (dtype === "bool") {
-      values = new Uint8Array(size);
-    } else {
-      throw new Error(`Unknown data type ${dtype}`);
-    }
-    for (let i = 0; i < size; i++) {
-      values[i] = randFunction();
-    }
-    return ENGINE.makeTensor(values, shape, dtype);
-  }
-  const rand = op({rand_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
-  const seedrandom = __toModule(require_seedrandom2());
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class MPRandGauss {
-    constructor(mean5, stdDeviation, dtype, truncated, seed) {
-      this.mean = mean5;
-      this.stdDev = stdDeviation;
-      this.dtype = dtype;
-      this.nextVal = NaN;
-      this.truncated = truncated;
-      if (this.truncated) {
-        this.upper = this.mean + this.stdDev * 2;
-        this.lower = this.mean - this.stdDev * 2;
-      }
-      const seedValue = seed ? seed : Math.random();
-      this.random = seedrandom.alea(seedValue.toString());
-    }
-    nextValue() {
-      if (!isNaN(this.nextVal)) {
-        const value = this.nextVal;
-        this.nextVal = NaN;
-        return value;
-      }
-      let resultX, resultY;
-      let isValid = false;
-      while (!isValid) {
-        let v1, v2, s;
-        do {
-          v1 = 2 * this.random() - 1;
-          v2 = 2 * this.random() - 1;
-          s = v1 * v1 + v2 * v2;
-        } while (s >= 1 || s === 0);
-        const mul62 = Math.sqrt(-2 * Math.log(s) / s);
-        resultX = this.mean + this.stdDev * v1 * mul62;
-        resultY = this.mean + this.stdDev * v2 * mul62;
-        if (!this.truncated || this.isValidTruncated(resultX)) {
-          isValid = true;
-        }
-      }
-      if (!this.truncated || this.isValidTruncated(resultY)) {
-        this.nextVal = this.convertValue(resultY);
-      }
-      return this.convertValue(resultX);
-    }
-    convertValue(value) {
-      if (this.dtype == null || this.dtype === "float32") {
-        return value;
-      }
-      return Math.round(value);
-    }
-    isValidTruncated(value) {
-      return value <= this.upper && value >= this.lower;
-    }
-  }
-  class RandGamma {
-    constructor(alpha, beta, dtype, seed) {
-      this.alpha = alpha;
-      this.beta = 1 / beta;
-      this.dtype = dtype;
-      const seedValue = seed ? seed : Math.random();
-      this.randu = seedrandom.alea(seedValue.toString());
-      this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
-      if (alpha < 1) {
-        this.d = alpha + 2 / 3;
-      } else {
-        this.d = alpha - 1 / 3;
-      }
-      this.c = 1 / Math.sqrt(9 * this.d);
-    }
-    nextValue() {
-      let x2, v0, v1, x, u, v;
-      while (true) {
-        do {
-          x = this.randn.nextValue();
-          v = 1 + this.c * x;
-        } while (v <= 0);
-        v *= v * v;
-        x2 = x * x;
-        v0 = 1 - 0.331 * x2 * x2;
-        v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
-        u = this.randu();
-        if (u < v0 || Math.log(u) < v1) {
-          break;
-        }
-      }
-      v = 1 / this.beta * this.d * v;
-      if (this.alpha < 1) {
-        v *= Math.pow(this.randu(), 1 / this.alpha);
-      }
-      return this.convertValue(v);
-    }
-    convertValue(value) {
-      if (this.dtype === "float32") {
-        return value;
-      }
-      return Math.round(value);
-    }
-  }
-  class UniformRandom {
-    constructor(min4 = 0, max6 = 1, dtype, seed) {
-      this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
-      this.min = min4;
-      this.range = max6 - min4;
-      this.dtype = dtype;
-      if (seed == null) {
-        seed = Math.random();
-      }
-      if (typeof seed === "number") {
-        seed = seed.toString();
-      }
-      if (!this.canReturnFloat() && this.range <= 1) {
-        throw new Error(`The difference between ${min4} - ${max6} <= 1 and dtype is not float`);
-      }
-      this.random = seedrandom.alea(seed);
-    }
-    convertValue(value) {
-      if (this.canReturnFloat()) {
-        return value;
-      }
-      return Math.round(value);
-    }
-    nextValue() {
-      return this.convertValue(this.min + this.range * this.random());
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
-    if (beta == null) {
-      beta = 1;
-    }
-    if (dtype == null) {
-      dtype = "float32";
-    }
-    if (dtype !== "float32" && dtype !== "int32") {
-      throw new Error(`Unsupported data type ${dtype}`);
-    }
-    const rgamma = new RandGamma(alpha, beta, dtype, seed);
-    const res = buffer(shape, dtype);
-    for (let i = 0; i < res.values.length; i++) {
-      res.values[i] = rgamma.nextValue();
-    }
-    return res.toTensor();
-  }
-  const randomGamma = op({randomGamma_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-    if (dtype != null && dtype === "bool") {
-      throw new Error(`Unsupported data type ${dtype}`);
-    }
-    const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
-    const res = buffer(shape, dtype);
-    for (let i = 0; i < res.values.length; i++) {
-      res.values[i] = randGauss.nextValue();
-    }
-    return res.toTensor();
-  }
-  const randomNormal = op({randomNormal_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
-    const res = buffer(shape, dtype);
-    const random = new UniformRandom(minval, maxval, null, seed);
-    for (let i = 0; i < res.values.length; i++) {
-      res.values[i] = random.nextValue();
-    }
-    return res.toTensor();
-  }
-  const randomUniform = op({randomUniform_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor1d(values, dtype) {
-    assertNonNull(values);
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 1) {
-      throw new Error("tensor1d() requires values to be a flat/TypedArray");
-    }
-    const shape = null;
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/range.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function range(start, stop, step7 = 1, dtype = "float32") {
-    if (step7 === 0) {
-      throw new Error("Cannot have a step of zero");
-    }
-    const forward = () => {
-      const sameStartStop = start === stop;
-      const increasingRangeNegativeStep = start < stop && step7 < 0;
-      const decreasingRangePositiveStep = stop < start && step7 > 1;
-      if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
-        return zeros([0], dtype);
-      }
-      const numElements = Math.abs(Math.ceil((stop - start) / step7));
-      const values = makeZerosTypedArray(numElements, dtype);
-      if (stop < start && step7 === 1) {
-        step7 = -1;
-      }
-      values[0] = start;
-      for (let i = 1; i < values.length; i++) {
-        values[i] = values[i - 1] + step7;
-      }
-      return tensor1d(values, dtype);
-    };
-    const attrs = {start, stop, step: step7, dtype};
-    return ENGINE.runKernelFunc(forward, {}, null, Range, attrs);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reciprocal_(x) {
-    const $x = convertToTensor(x, "x", "reciprocal");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.reciprocal($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Reciprocal);
-  }
-  const reciprocal = op({reciprocal_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function relu_(x) {
-    const $x = convertToTensor(x, "x", "relu");
-    const forward = (backend2, save) => {
-      save([$x]);
-      if ($x.dtype === "bool") {
-        return cast($x, "int32");
-      }
-      return backend2.relu($x);
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Relu);
-  }
-  const relu = op({relu_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function relu6_(x) {
-    const $x = convertToTensor(x, "x", "relu6");
-    const forward = (backend2, save) => {
-      save([$x]);
-      if ($x.dtype === "bool") {
-        return cast($x, "int32");
-      }
-      return backend2.relu6($x);
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Relu6);
-  }
-  const relu6 = op({relu6_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reverse_(x, axis) {
-    const $x = convertToTensor(x, "x", "reverse");
-    const forward = (backend2) => {
-      const axes = parseAxisParam(axis, $x.shape);
-      if ($x.rank === 0) {
-        return clone($x);
-      }
-      const res = backend2.reverse($x, axes);
-      return reshape(res, $x.shape);
-    };
-    const inputs = {x: $x};
-    const attrs = {dims: axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, Reverse, attrs);
-  }
-  const reverse = op({reverse_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reverse1d_(x) {
-    const $x = convertToTensor(x, "x", "reverse");
-    assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
-    return reverse($x, 0);
-  }
-  const reverse1d = op({reverse1d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reverse2d_(x, axis) {
-    const $x = convertToTensor(x, "x", "reverse");
-    assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
-    return reverse($x, axis);
-  }
-  const reverse2d = op({reverse2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reverse3d_(x, axis) {
-    const $x = convertToTensor(x, "x", "reverse");
-    assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
-    return reverse($x, axis);
-  }
-  const reverse3d = op({reverse3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function reverse4d_(x, axis) {
-    const $x = convertToTensor(x, "x", "reverse");
-    assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
-    return reverse($x, axis);
-  }
-  const reverse4d = op({reverse4d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/round.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function round_(x) {
-    const $x = convertToTensor(x, "x", "round");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.round($x), inputs, null, Round);
-  }
-  const round = op({round_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function rsqrt_(x) {
-    const $x = convertToTensor(x, "x", "rsqrt");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.rsqrt($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Rsqrt);
-  }
-  const rsqrt = op({rsqrt_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function selu_(x) {
-    const $x = convertToTensor(x, "x", "selu");
-    const forward = (backend2, save) => {
-      const res = backend2.selu($x);
-      save([$x]);
-      return res;
-    };
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc(forward, inputs, null, Selu);
-  }
-  const selu = op({selu_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
-  function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad8, dilation = [1, 1], dataFormat = "NHWC") {
-    const $x = convertToTensor(x, "x", "separableConv2d");
-    const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
-    const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    if (dataFormat === "NCHW") {
-      throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
-    }
-    assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-    assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-    assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-    assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
-    assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
-    const inChannels = $depthwiseFilter.shape[2];
-    const channelMultiplier = $depthwiseFilter.shape[3];
-    assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
-    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad8, dataFormat, dilation);
-    const pointwiseStride = 1;
-    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const separableConv2d = op({separableConv2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function setdiff1dAsync_(x, y) {
-    const $x = convertToTensor(x, "x", "setdiff1d");
-    const $y = convertToTensor(y, "y", "setdiff1d");
-    assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
-    assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
-    assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
-    const xVals = await $x.data();
-    const yVals = await $y.data();
-    const ySet = new Set(yVals);
-    let outputSize = 0;
-    for (let i = 0; i < xVals.length; i++) {
-      if (!ySet.has(xVals[i])) {
-        outputSize++;
-      }
-    }
-    const buffer11 = new TensorBuffer([outputSize], $x.dtype);
-    const indices = new TensorBuffer([outputSize], "int32");
-    for (let i = 0, p = 0; i < xVals.length; i++) {
-      if (!ySet.has(xVals[i])) {
-        buffer11.values[p] = xVals[i];
-        indices.values[p] = i;
-        p++;
-      }
-    }
-    return [buffer11.toTensor(), indices.toTensor()];
-  }
-  const setdiff1dAsync = setdiff1dAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sign_(x) {
-    const $x = convertToTensor(x, "x", "sign");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2) => backend2.sign($x), inputs, null, Sign);
-  }
-  const sign = op({sign_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sin_(x) {
-    const $x = convertToTensor(x, "x", "sin");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.sin($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Sin);
-  }
-  const sin = op({sin_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sinh_(x) {
-    const $x = convertToTensor(x, "x", "sinh");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.sinh($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Sinh);
-  }
-  const sinh = op({sinh_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function slice1d_(x, begin, size) {
-    const $x = convertToTensor(x, "x", "slice1d");
-    assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
-    return slice($x, [begin], [size]);
-  }
-  const slice1d = op({slice1d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function slice2d_(x, begin, size) {
-    const $x = convertToTensor(x, "x", "slice2d");
-    assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
-    return slice($x, begin, size);
-  }
-  const slice2d = op({slice2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function slice3d_(x, begin, size) {
-    const $x = convertToTensor(x, "x", "slice3d");
-    assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
-    return slice($x, begin, size);
-  }
-  const slice3d = op({slice3d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function slice4d_(x, begin, size) {
-    const $x = convertToTensor(x, "x", "slice4d");
-    assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
-    return slice($x, begin, size);
-  }
-  const slice4d = op({slice4d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function softmax_(logits, dim = -1) {
-    const $logits = convertToTensor(logits, "logits", "softmax", "float32");
-    if (dim === -1) {
-      dim = $logits.rank - 1;
-    }
-    if (dim !== $logits.rank - 1) {
-      throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
-    }
-    const inputs = {logits: $logits};
-    const attrs = {dim};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const y = backend2.softmax($logits, dim);
-      save([y]);
-      return y;
-    }, inputs, null, Softmax, attrs);
-  }
-  const softmax = op({softmax_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function fft_(input) {
-    assert(input.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input.dtype}.`);
-    const inputs = {input};
-    return ENGINE.runKernelFunc((backend2) => {
-      const innerDimensionSize = input.shape[input.shape.length - 1];
-      const batch = input.size / innerDimensionSize;
-      const input2D = input.as2D(batch, innerDimensionSize);
-      const result = backend2.fft(input2D);
-      return result.reshape(input.shape);
-    }, inputs, null, FFT);
-  }
-  const fft = op({fft_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function ifft_(input) {
-    assert(input.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input.dtype}.`);
-    const inputs = {input};
-    return ENGINE.runKernelFunc((backend2) => {
-      const innerDimensionSize = input.shape[input.shape.length - 1];
-      const batch = input.size / innerDimensionSize;
-      const input2D = reshape(input, [batch, innerDimensionSize]);
-      const result = backend2.ifft(input2D);
-      return reshape(result, input.shape);
-    }, inputs, null, IFFT);
-  }
-  const ifft = op({ifft_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function irfft_(input) {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    let ret;
-    if (innerDimensionSize <= 2) {
-      const complexInput = reshape(input, [batch, innerDimensionSize]);
-      ret = ifft(complexInput);
-    } else {
-      const outputShape = [batch, 2 * (innerDimensionSize - 1)];
-      const realInput = reshape(real(input), [batch, innerDimensionSize]);
-      const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
-      const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
-      const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-      const r = concat([realInput, realConjugate], 1);
-      const i = concat([imagInput, imagConjugate], 1);
-      const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
-      ret = ifft(complexInput);
-    }
-    ret = real(ret);
-    if (input.rank === 3 && input.shape[0] !== 0) {
-      const temp = ret;
-      const batch2 = input.shape[0];
-      ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
-      temp.dispose();
-    }
-    return ret;
-  }
-  const irfft = op({irfft_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
-  function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
-    let splitSizes = [];
-    if (typeof numOrSizeSplits === "number") {
-      assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
-      splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
-    } else {
-      const numOfNegs = numOrSizeSplits.reduce((count, value) => {
-        if (value === -1) {
-          count += 1;
-        }
-        return count;
-      }, 0);
-      assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
-      const negIndex = numOrSizeSplits.indexOf(-1);
-      if (negIndex !== -1) {
-        const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
-        numOrSizeSplits[negIndex] = x.shape[axis] - total;
-      }
-      assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
-      splitSizes = numOrSizeSplits;
-    }
-    return splitSizes;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/split.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function split_(x, numOrSizeSplits, axis = 0) {
-    const $x = convertToTensor(x, "x", "split");
-    const forward = (backend2, _) => {
-      const $axis = parseAxisParam(axis, $x.shape)[0];
-      const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);
-      return backend2.split($x, splitSizes, $axis);
-    };
-    const inputs = {x: $x};
-    const attr = {numOrSizeSplits, axis};
-    return ENGINE.runKernelFunc(forward, inputs, null, SplitV, attr);
-  }
-  const split = op({split_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function rfft_(input, fftLength) {
-    assert(input.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input.dtype}`);
-    let innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    let adjustedInput;
-    if (fftLength != null && fftLength < innerDimensionSize) {
-      const begin = input.shape.map((v) => 0);
-      const size = input.shape.map((v) => v);
-      size[input.shape.length - 1] = fftLength;
-      adjustedInput = slice(input, begin, size);
-      innerDimensionSize = fftLength;
-    } else if (fftLength != null && fftLength > innerDimensionSize) {
-      const zerosShape = input.shape.map((v) => v);
-      zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
-      adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
-      innerDimensionSize = fftLength;
-    } else {
-      adjustedInput = input;
-    }
-    const zerosInput = zerosLike(adjustedInput);
-    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
-    const ret = fft(complexInput);
-    const half = Math.floor(innerDimensionSize / 2) + 1;
-    const realValues = real(ret);
-    const imagValues = imag(ret);
-    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
-    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
-    const outputShape = adjustedInput.shape.slice();
-    outputShape[adjustedInput.shape.length - 1] = half;
-    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
-  }
-  const rfft = op({rfft_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sqrt_(x) {
-    const $x = convertToTensor(x, "x", "sqrt");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.sqrt($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Sqrt);
-  }
-  const sqrt = op({sqrt_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function squaredDifference_(a, b) {
-    let $a = convertToTensor(a, "a", "squaredDifference");
-    let $b = convertToTensor(b, "b", "squaredDifference");
-    [$a, $b] = makeTypesMatch($a, $b);
-    assertAndGetBroadcastShape($a.shape, $b.shape);
-    const forward = (backend2, save) => {
-      const res = backend2.squaredDifference($a, $b);
-      save([$a, $b]);
-      return res;
-    };
-    const inputs = {a: $a, b: $b};
-    const attrs = {};
-    return ENGINE.runKernelFunc(forward, inputs, null, SquaredDifference, attrs);
-  }
-  const squaredDifference = op({squaredDifference_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function squeeze_(x, axis) {
-    const $x = convertToTensor(x, "x", "squeeze");
-    return reshape($x, squeezeShape($x.shape, axis).newShape);
-  }
-  const squeeze = op({squeeze_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function stack_(tensors, axis = 0) {
-    const $tensors = convertToTensorArray(tensors, "tensors", "stack");
-    assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
-    if ($tensors.length === 1) {
-      return expandDims($tensors[0], axis);
-    }
-    const rank = $tensors[0].rank;
-    const shape = $tensors[0].shape;
-    const dtype = $tensors[0].dtype;
-    assert(axis <= rank, () => "Axis must be <= rank of the tensor");
-    $tensors.forEach((t) => {
-      assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
-      assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
-    });
-    const expandedTensors = $tensors.map((t) => expandDims(t, axis));
-    return concat(expandedTensors, axis);
-  }
-  const stack = op({stack_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/step.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function step_(x, alpha = 0) {
-    const $x = convertToTensor(x, "x", "step");
-    const inputs = {x: $x};
-    const attrs = {alpha};
-    return ENGINE.runKernelFunc((backend2) => backend2.step($x, alpha), inputs, null, Step, attrs);
-  }
-  const step = op({step_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
-    let $x = convertToTensor(x, "x", "stridedSlice");
-    const forward = (backend2) => {
-      if (strides == null) {
-        strides = new Array(begin.length);
-      }
-      const ellipsisAxes = maskToAxes(ellipsisMask);
-      if (ellipsisAxes.length > 1) {
-        throw new Error("Multiple ellipses in slice is not allowed.");
-      }
-      if (ellipsisMask !== 0 && newAxisMask !== 0) {
-        throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
-      }
-      if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
-        throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
-      }
-      const numInterpolatedAxes = $x.rank - begin.length;
-      const expandAxes = maskToAxes(newAxisMask);
-      const newShape = $x.shape.slice();
-      expandAxes.forEach((axis) => {
-        begin[axis] = 0;
-        end[axis] = 1;
-        newShape.splice(axis, 0, 1);
-      });
-      $x = reshape($x, newShape);
-      const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
-      begin = normalizedBegin;
-      end = normalizedEnd;
-      strides = normalizedStrides;
-      const shrinkAxes = maskToAxes(shrinkAxisMask);
-      shrinkAxes.forEach((axis) => {
-        end[axis] = begin[axis] + 1;
-        strides[axis] = 1;
-      });
-      const size = computeOutShape(begin, end, strides);
-      const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
-      const nonStrided = strides.every((v) => v === 1);
-      if (nonStrided) {
-        return reshape(slice($x, begin, size), outShape);
-      }
-      const res = backend2.stridedSlice($x, begin, end, strides);
-      return reshape(res, outShape);
-    };
-    const inputs = {x: $x};
-    const attrs = {
-      begin,
-      end,
-      strides,
-      beginMask,
-      endMask,
-      ellipsisMask,
-      newAxisMask,
-      shrinkAxisMask
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, StridedSlice, attrs);
-  }
-  const stridedSlice = op({stridedSlice_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tan_(x) {
-    const $x = convertToTensor(x, "x", "tan");
-    const inputs = {x: $x};
-    return ENGINE.runKernelFunc((backend2, save) => {
-      const res = backend2.tan($x);
-      save([$x]);
-      return res;
-    }, inputs, null, Tan);
-  }
-  const tan = op({tan_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor2d(values, shape, dtype) {
-    assertNonNull(values);
-    if (shape != null && shape.length !== 2) {
-      throw new Error("tensor2d() requires shape to have two numbers");
-    }
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 2 && inferredShape.length !== 1) {
-      throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
-    }
-    if (inferredShape.length === 1 && shape == null) {
-      throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-    }
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor4d(values, shape, dtype) {
-    assertNonNull(values);
-    if (shape != null && shape.length !== 4) {
-      throw new Error("tensor4d() requires shape to have four numbers");
-    }
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 4 && inferredShape.length !== 1) {
-      throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
-    }
-    if (inferredShape.length === 1 && shape == null) {
-      throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-    }
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor5d(values, shape, dtype) {
-    assertNonNull(values);
-    if (shape != null && shape.length !== 5) {
-      throw new Error("tensor5d() requires shape to have five numbers");
-    }
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 5 && inferredShape.length !== 1) {
-      throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
-    }
-    if (inferredShape.length === 1 && shape == null) {
-      throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-    }
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tensor6d(values, shape, dtype) {
-    assertNonNull(values);
-    if (shape != null && shape.length !== 6) {
-      throw new Error("tensor6d() requires shape to have six numbers");
-    }
-    const inferredShape = inferShape(values, dtype);
-    if (inferredShape.length !== 6 && inferredShape.length !== 1) {
-      throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
-    }
-    if (inferredShape.length === 1 && shape == null) {
-      throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-    }
-    shape = shape || inferredShape;
-    return makeTensor(values, shape, inferredShape, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function topk_(x, k = 1, sorted = true) {
-    const $x = convertToTensor(x, "x", "topk");
-    if ($x.rank === 0) {
-      throw new Error("topk() expects the input to be of rank 1 or higher");
-    }
-    const lastDim = $x.shape[$x.shape.length - 1];
-    if (k > lastDim) {
-      throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
-    }
-    const inputs = {x: $x};
-    const attrs = {k, sorted};
-    const [values, indices] = ENGINE.runKernelFunc((b) => b.topk($x, k, sorted), inputs, null, TopK, attrs);
-    return {values, indices};
-  }
-  const topk = op({topk_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-    if (dtype != null && dtype === "bool") {
-      throw new Error(`Unsupported data type $ { dtype }`);
-    }
-    const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
-    const res = buffer(shape, dtype);
-    for (let i = 0; i < res.values.length; i++) {
-      res.values[i] = randGauss.nextValue();
-    }
-    return res.toTensor();
-  }
-  const truncatedNormal = op({truncatedNormal_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function unsortedSegmentSum_(x, segmentIds, numSegments) {
-    const $x = convertToTensor(x, "x", "unsortedSegmentSum");
-    const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
-    assert(isInt(numSegments), () => "numSegments must be of dtype int");
-    const inputs = {x: $x, segmentIds: $segmentIds};
-    const attrs = {numSegments};
-    const forward = (backend2, save) => {
-      const res = backend2.unsortedSegmentSum($x, $segmentIds, numSegments);
-      save([$segmentIds]);
-      return res;
-    };
-    return ENGINE.runKernelFunc(forward, inputs, null, UnsortedSegmentSum, attrs);
-  }
-  const unsortedSegmentSum = op({unsortedSegmentSum_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function unstack_(x, axis = 0) {
-    const $x = convertToTensor(x, "x", "unstack");
-    assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
-    if (axis < 0) {
-      axis += $x.shape.length;
-    }
-    const inputs = {value: $x};
-    const attrs = {axis};
-    const forward = (backend2) => backend2.unstack($x, axis);
-    return ENGINE.runKernelFunc(forward, inputs, null, Unpack, attrs);
-  }
-  const unstack = op({unstack_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function variable(initialValue, trainable = true, name, dtype) {
-    return ENGINE.makeVariable(initialValue, trainable, name, dtype);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function whereImpl(condShape, condVals) {
-    const indices = [];
-    for (let i = 0; i < condVals.length; i++) {
-      if (condVals[i]) {
-        indices.push(i);
-      }
-    }
-    const inBuffer = buffer(condShape, "int32");
-    const out = buffer([indices.length, condShape.length], "int32");
-    for (let i = 0; i < indices.length; i++) {
-      const loc = inBuffer.indexToLoc(indices[i]);
-      const offset = i * condShape.length;
-      out.values.set(loc, offset);
-    }
-    return out.toTensor();
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function whereAsync_(condition) {
-    const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
-    const vals = await $condition.data();
-    const res = whereImpl($condition.shape, vals);
-    if (condition !== $condition) {
-      $condition.dispose();
-    }
-    return res;
-  }
-  const whereAsync = whereAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function booleanMaskAsync_(tensor17, mask, axis) {
-    const $tensor = convertToTensor(tensor17, "tensor", "boolMask");
-    const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
-    const axisFrom = axis == null ? 0 : axis;
-    const maskDim = $mask.rank;
-    const tensorShape = $tensor.shape;
-    assert(maskDim > 0, () => "mask cannot be scalar");
-    assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
-    let leadingSize = 1;
-    for (let i = axisFrom; i < axisFrom + maskDim; i++) {
-      leadingSize *= tensorShape[i];
-    }
-    const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
-    const reshapedTensor = reshape($tensor, targetTensorShape);
-    const reshapedMask = reshape($mask, [-1]);
-    const positivePositions = await whereAsync(reshapedMask);
-    const indices = squeeze(positivePositions, [1]);
-    const res = gather(reshapedTensor, indices, axisFrom);
-    if (tensor17 !== $tensor) {
-      $tensor.dispose();
-    }
-    if (mask !== $mask) {
-      $mask.dispose();
-    }
-    indices.dispose();
-    reshapedTensor.dispose();
-    reshapedMask.dispose();
-    positivePositions.dispose();
-    return res;
-  }
-  const booleanMaskAsync = booleanMaskAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function notEqualStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "notEqualStrict");
-    const $b = convertToTensor(b, "b", "notEqualStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in notEqualStrict: ");
-    return notEqual($a, $b);
-  }
-  function lessStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "lessStrict");
-    const $b = convertToTensor(b, "b", "lessStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in lessStrict: ");
-    return less($a, $b);
-  }
-  function equalStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "equalStrict");
-    const $b = convertToTensor(b, "b", "equalStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in equalStrict: ");
-    return equal($a, $b);
-  }
-  function lessEqualStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "lessEqualStrict");
-    const $b = convertToTensor(b, "b", "lessEqualStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in lessEqualStrict: ");
-    return lessEqual($a, $b);
-  }
-  function greaterStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "greaterStrict");
-    const $b = convertToTensor(b, "b", "greaterStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in greaterStrict: ");
-    return greater($a, $b);
-  }
-  function greaterEqualStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "greaterEqualStrict");
-    const $b = convertToTensor(b, "b", "greaterEqualStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in greaterEqualStrict: ");
-    return greaterEqual($a, $b);
-  }
-  const equalStrict = op({equalStrict_});
-  const greaterEqualStrict = op({greaterEqualStrict_});
-  const greaterStrict = op({greaterStrict_});
-  const lessEqualStrict = op({lessEqualStrict_});
-  const lessStrict = op({lessStrict_});
-  const notEqualStrict = op({notEqualStrict_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function addStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "addStrict");
-    const $b = convertToTensor(b, "b", "addStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in addStrict: ");
-    return add2($a, $b);
-  }
-  function subStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "subStrict");
-    const $b = convertToTensor(b, "b", "subStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in subStrict: ");
-    return sub($a, $b);
-  }
-  function powStrict_(base, exp11) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    assertShapesMatch(base.shape, exp11.shape, "Error in powStrict: ");
-    return pow(base, exp11);
-  }
-  function mulStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "mul");
-    const $b = convertToTensor(b, "b", "mul");
-    assertShapesMatch($a.shape, $b.shape, "Error in multiplyStrict: ");
-    return mul($a, $b);
-  }
-  function divStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "div");
-    const $b = convertToTensor(b, "b", "div");
-    assertShapesMatch($a.shape, $b.shape, "Error in divideStrict: ");
-    return div($a, $b);
-  }
-  function modStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "modStrict");
-    const $b = convertToTensor(b, "b", "modStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in modStrict: ");
-    return mod($a, $b);
-  }
-  function minimumStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "minimumStrict");
-    const $b = convertToTensor(b, "b", "minimumStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in minimumStrict: ");
-    return minimum($a, $b);
-  }
-  function maximumStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "maximumStrict");
-    const $b = convertToTensor(b, "b", "maximumStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in maximumStrict: ");
-    return maximum($a, $b);
-  }
-  function squaredDifferenceStrict_(a, b) {
-    deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-    const $a = convertToTensor(a, "a", "squaredDifferenceStrict");
-    const $b = convertToTensor(b, "b", "squaredDifferenceStrict");
-    assertShapesMatch($a.shape, $b.shape, "Error in squaredDifferenceStrict: ");
-    return squaredDifference($a, $b);
-  }
-  const addStrict = op({addStrict_});
-  const divStrict = op({divStrict_});
-  const maximumStrict = op({maximumStrict_});
-  const minimumStrict = op({minimumStrict_});
-  const modStrict = op({modStrict_});
-  const mulStrict = op({mulStrict_});
-  const powStrict = op({powStrict_});
-  const squaredDifferenceStrict = op({squaredDifferenceStrict_});
-  const subStrict = op({subStrict_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
-    x = convertToTensor(x, "x", "norm");
-    const norm4 = normImpl(x, ord, axis);
-    let keepDimsShape = norm4.shape;
-    if (keepDims) {
-      const axes = parseAxisParam(axis, x.shape);
-      keepDimsShape = expandShapeToKeepDim(norm4.shape, axes);
-    }
-    return reshape(norm4, keepDimsShape);
-  }
-  function normImpl(x, p, axis = null) {
-    if (x.rank === 0) {
-      return abs(x);
-    }
-    if (x.rank !== 1 && axis === null) {
-      return normImpl(reshape(x, [-1]), p, axis);
-    }
-    if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
-      if (p === 1) {
-        return sum2(abs(x), axis);
-      }
-      if (p === Infinity) {
-        return max(abs(x), axis);
-      }
-      if (p === -Infinity) {
-        return min(abs(x), axis);
-      }
-      if (p === "euclidean" || p === 2) {
-        return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
-      }
-      throw new Error(`Error in norm: invalid ord value: ${p}`);
-    }
-    if (Array.isArray(axis) && axis.length === 2) {
-      if (p === 1) {
-        return max(sum2(abs(x), axis[0]), axis[1] - 1);
-      }
-      if (p === Infinity) {
-        return max(sum2(abs(x), axis[1]), axis[0]);
-      }
-      if (p === -Infinity) {
-        return min(sum2(abs(x), axis[1]), axis[0]);
-      }
-      if (p === "fro" || p === "euclidean") {
-        return sqrt(sum2(square(x), axis));
-      }
-      throw new Error(`Error in norm: invalid ord value: ${p}`);
-    }
-    throw new Error(`Error in norm: invalid axis: ${axis}`);
-  }
-  const norm = op({norm_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function movingAverage_(v, x, decay, step7, zeroDebias = true) {
-    const $v = convertToTensor(v, "v", "movingAverage");
-    const $x = convertToTensor(x, "x", "movingAverage");
-    const $decay = convertToTensor(decay, "decay", "movingAverage");
-    assertTypesMatch($v, $x);
-    assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
-    const one = scalar(1);
-    const oneMinusDecay = sub(one, $decay);
-    let update = mul(sub($x, $v), oneMinusDecay);
-    if (zeroDebias) {
-      assert(step7 != null, () => "When using zeroDebias: true, step is required.");
-      const $step = convertToTensor(step7, "step", "movingAverage");
-      update = div(update, sub(one, pow($decay, $step)));
-    }
-    return add2($v, update);
-  }
-  const movingAverage = op({movingAverage_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function scatterND_(indices, updates, shape) {
-    const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
-    const $updates = convertToTensor(updates, "updates", "scatterND");
-    validateInput($updates, $indices, shape);
-    const forward = (backend2) => {
-      return backend2.scatterND($indices, $updates, shape);
-    };
-    const inputs = {indices: $indices, updates: $updates};
-    const attrs = {shape};
-    return ENGINE.runKernelFunc(forward, inputs, null, ScatterNd, attrs);
-  }
-  const scatterND = op({scatterND_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
-  function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
-    if (sparseIndices.dtype !== "int32") {
-      throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
-    }
-    if (sparseIndices.rank > 2) {
-      throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
-    }
-    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
-    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
-    if (outputShape.length !== numDims) {
-      throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
-    }
-    const numValues = sparseValues.size;
-    if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
-      throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
-    }
-    if (sparseValues.dtype !== defaultValues.dtype) {
-      throw new Error("sparseValues.dtype must match defaultValues.dtype");
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
-    const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
-    const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
-    const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
-    validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
-    const inputs = {
-      sparseIndices: $sparseIndices,
-      sparseValues: $sparseValues,
-      defaultValue: $defaultValue
-    };
-    const attrs = {outputShape};
-    return ENGINE.runKernelFunc((backend2) => backend2.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null, SparseToDense, attrs);
-  }
-  const sparseToDense = op({sparseToDense_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function gatherND_(x, indices) {
-    const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
-    const $x = convertToTensor(x, "x", "gatherND");
-    const forward = (backend2) => {
-      return backend2.gatherND($x, $indices);
-    };
-    const inputs = {params: $x, indices: $indices};
-    return ENGINE.runKernelFunc(forward, inputs, null, GatherNd);
-  }
-  const gatherND = op({gatherND_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getNoiseShape(x, noiseShape) {
-    if (noiseShape == null) {
-      return x.shape.slice();
-    }
-    if (arraysEqual(x.shape, noiseShape)) {
-      return noiseShape;
-    }
-    if (x.shape.length === noiseShape.length) {
-      const newDimension = [];
-      for (let i = 0; i < x.shape.length; i++) {
-        if (noiseShape[i] == null && x.shape[i] != null) {
-          newDimension.push(x.shape[i]);
-        } else {
-          newDimension.push(noiseShape[i]);
-        }
-      }
-      return newDimension;
-    }
-    return noiseShape;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function dropout_(x, rate, noiseShape, seed) {
-    const $x = convertToTensor(x, "x", "dropout");
-    assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
-    assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
-    if (rate === 0) {
-      return x instanceof Tensor ? $x.clone() : $x;
-    }
-    const $noiseShape = getNoiseShape($x, noiseShape);
-    const keepProb = 1 - rate;
-    const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
-    return mul($x, multiplier);
-  }
-  const dropout = op({dropout_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function enclosingPowerOfTwo(value) {
-    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
-  }
-  function cosineWindow(windowLength, a, b) {
-    const even = 1 - windowLength % 2;
-    const newValues = new Float32Array(windowLength);
-    for (let i = 0; i < windowLength; ++i) {
-      const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
-      newValues[i] = a - b * Math.cos(cosArg);
-    }
-    return tensor1d(newValues, "float32");
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function inTopKAsync_(predictions, targets, k = 1) {
-    const $predictions = convertToTensor(predictions, "predictions", "inTopK");
-    const $targets = convertToTensor(targets, "targets", "inTopK");
-    assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
-    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
-    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
-    const lastDim = $predictions.shape[$predictions.shape.length - 1];
-    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
-    const predictionsVals = await $predictions.data();
-    const targetsVals = await $targets.data();
-    const [batch, size] = [predictionsVals.length / lastDim, lastDim];
-    const precision = getTypedArrayFromDType("bool", batch);
-    for (let b = 0; b < batch; b++) {
-      const offset = b * size;
-      const vals = predictionsVals.subarray(offset, offset + size);
-      const valAndInd = [];
-      for (let i = 0; i < vals.length; i++) {
-        valAndInd.push({value: vals[i], index: i});
-      }
-      valAndInd.sort((a, b2) => b2.value - a.value);
-      precision[b] = 0;
-      for (let i = 0; i < k; i++) {
-        if (valAndInd[i].index === targetsVals[b]) {
-          precision[b] = 1;
-          break;
-        }
-      }
-    }
-    if (predictions !== $predictions) {
-      $predictions.dispose();
-    }
-    if (targets !== $targets) {
-      $targets.dispose();
-    }
-    return tensor4(precision, $targets.shape, "bool");
-  }
-  const inTopKAsync = inTopKAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv2DBackpropFilter_(x, dy, filterShape, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-    let x4D = x;
-    if (x.rank === 3) {
-      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-    }
-    let dy4D = dy;
-    if (dy4D.rank === 3) {
-      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
-    assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
-    assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
-    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-    assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
-    assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2) => {
-      const dilations = 1;
-      const $dataFormat = convertConv2DDataFormat(dataFormat);
-      const convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-      return backend2.conv2dDerFilter(x4D, dy4D, convInfo);
-    };
-    const inputs = {x: x4D, dy: dy4D};
-    const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode};
-    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);
-  }
-  const conv2DBackpropFilter = op({conv2DBackpropFilter_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getFusedDyActivation(dy, y, activation) {
-    if (activation == null || activation === "linear") {
-      return dy;
-    }
-    if (activation === "relu") {
-      return mul(dy, step(y));
-    }
-    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
-  }
-  function getFusedBiasGradient(bias, dyActivation) {
-    let res = dyActivation;
-    const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
-    if (reduceAxes.length > 0) {
-      res = sum2(res, reduceAxes);
-    }
-    return reshape(res, bias.shape);
-  }
-  function applyActivation(x, activation, preluActivationWeights) {
-    if (activation === "linear") {
-      return x;
-    } else if (activation === "relu") {
-      return relu(x);
-    } else if (activation === "elu") {
-      return elu(x);
-    } else if (activation === "relu6") {
-      return relu6(x);
-    } else if (activation === "prelu") {
-      return prelu(x, preluActivationWeights);
-    }
-    throw new Error(`Unknown fused activation ${activation}.`);
-  }
-  const shouldFuse = (gradientDepth, activation) => {
-    const gradientMode = gradientDepth > 0;
-    return !gradientMode || activation === "linear";
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function fusedConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-    activation = activation || "linear";
-    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-      let result = conv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-      if (bias != null) {
-        result = add2(result, bias);
-      }
-      return applyActivation(result, activation, preluActivationWeights);
-    }
-    const $x = convertToTensor(x, "x", "conv2d");
-    const $filter = convertToTensor(filter, "filter", "conv2d");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-    assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode);
-    let $bias;
-    if (bias != null) {
-      $bias = convertToTensor(bias, "bias", "fused conv2d");
-      [$bias] = makeTypesMatch($bias, $x);
-      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-    }
-    let $preluActivationWeights;
-    if (preluActivationWeights != null) {
-      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
-    }
-    const grad2 = (dy, saved) => {
-      const [$filter2, x4D2, y, $bias2] = saved;
-      const dyActivation = getFusedDyActivation(dy, y, activation);
-      assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-      const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8);
-      const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8);
-      const der = [xDer, filterDer];
-      if ($bias2 != null) {
-        const biasDer = getFusedBiasGradient($bias2, dyActivation);
-        der.push(biasDer);
-      }
-      return der;
-    };
-    const forward = (backend2) => {
-      const res = backend2.fusedConv2d({
-        input: x4D,
-        filter: $filter,
-        convInfo,
-        bias: $bias,
-        activation,
-        preluActivationWeights: $preluActivationWeights
-      });
-      return res;
-    };
-    const inputs = {
-      x: x4D,
-      filter: $filter,
-      bias: $bias,
-      preluActivationWeights: $preluActivationWeights
-    };
-    const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-    if (bias == null) {
-      const customOp = customGrad((x4D2, filter2, save) => {
-        let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-        save([filter2, x4D2, res]);
-        if (reshapedTo4D) {
-          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-        }
-        return {value: res, gradFunc: grad2};
-      });
-      return customOp(x4D, $filter);
-    } else {
-      const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-        let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-        save([filter2, x4D2, res, bias2]);
-        if (reshapedTo4D) {
-          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-        }
-        return {value: res, gradFunc: grad2};
-      });
-      return customOpWithBias(x4D, $filter, $bias);
-    }
-  }
-  const conv2d5 = op({fusedConv2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {
-    let x4D = x;
-    if (x.rank === 3) {
-      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-    }
-    let dy4D = dy;
-    if (dy4D.rank === 3) {
-      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    }
-    const forward = (backend2) => backend2.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);
-    const inputs = {x: x4D, dy: dy4D};
-    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);
-  }
-  const depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {
-    let dy4D = dy;
-    let reshapedTo4D = false;
-    if (dy.rank === 3) {
-      reshapedTo4D = true;
-      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    }
-    const forward = (backend2) => backend2.depthwiseConv2DDerInput(dy4D, filter, convInfo);
-    const inputs = {dy: dy4D};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function fusedDepthwiseConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-      let result = depthwiseConv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-      if (bias != null) {
-        result = add2(result, bias);
-      }
-      return applyActivation(result, activation, preluActivationWeights);
-    }
-    const $x = convertToTensor(x, "x", "depthwiseConv2d");
-    const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-    let x4D = $x;
-    let reshapedTo4D = false;
-    if ($x.rank === 3) {
-      reshapedTo4D = true;
-      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    }
-    assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-    assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-    assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-    if (dilations == null) {
-      dilations = [1, 1];
-    }
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-    let $bias;
-    if (bias != null) {
-      $bias = convertToTensor(bias, "bias", "fused conv2d");
-      [$bias] = makeTypesMatch($bias, $x);
-      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-    }
-    let $preluActivationWeights;
-    if (preluActivationWeights != null) {
-      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
-    }
-    const grad2 = (dy, saved) => {
-      assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
-      const [$filter2, x4D2, y, bias2] = saved;
-      const dyActivation = getFusedDyActivation(dy, y, activation);
-      const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, convInfo);
-      const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, convInfo);
-      if (bias2 != null) {
-        const biasDer = getFusedBiasGradient($bias, dyActivation);
-        return [xDer, filterDer, biasDer];
-      }
-      return [xDer, filterDer];
-    };
-    const forward = (backend2) => {
-      const res = backend2.fusedDepthwiseConv2D({
-        input: x4D,
-        filter: $filter,
-        convInfo,
-        bias: $bias,
-        activation,
-        preluActivationWeights: $preluActivationWeights
-      });
-      return res;
-    };
-    const inputs = {
-      x: x4D,
-      filter: $filter,
-      bias: $bias,
-      preluActivationWeights: $preluActivationWeights
-    };
-    const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-    if (bias == null) {
-      const customOp = customGrad((x4D2, filter2, save) => {
-        let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-        save([filter2, x4D2, res]);
-        if (reshapedTo4D) {
-          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-        }
-        return {value: res, gradFunc: grad2};
-      });
-      return customOp(x4D, $filter);
-    } else {
-      const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-        let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-        save([filter2, x4D2, res, bias2]);
-        if (reshapedTo4D) {
-          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-        }
-        return {value: res, gradFunc: grad2};
-      });
-      return customOpWithBias(x4D, $filter, $bias);
-    }
-  }
-  const depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights}) {
-    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-      let result = matMul(a, b, transposeA, transposeB);
-      if (bias != null) {
-        result = add2(result, bias);
-      }
-      return applyActivation(result, activation, preluActivationWeights);
-    }
-    let $a = convertToTensor(a, "a", "fused matMul");
-    let $b = convertToTensor(b, "b", "fused matMul");
-    [$a, $b] = makeTypesMatch($a, $b);
-    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-    const outerDimsA = $a.shape.slice(0, -2);
-    const outerDimsB = $b.shape.slice(0, -2);
-    const batchDimA = sizeFromShape(outerDimsA);
-    const batchDimB = sizeFromShape(outerDimsB);
-    assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-    assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-    assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-    let $bias;
-    if (bias != null) {
-      $bias = convertToTensor(bias, "bias", "fused matMul");
-      [$bias] = makeTypesMatch($bias, $a);
-      assertAndGetBroadcastShape(outShape, $bias.shape);
-    }
-    let $preluActivationWeights;
-    if (preluActivationWeights != null) {
-      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
-    }
-    const grad2 = (dy, saved) => {
-      const [a3D2, b3D2, y, $bias2] = saved;
-      const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
-      let aDer;
-      let bDer;
-      if (!transposeA && !transposeB) {
-        aDer = matMul(dyActivation, b3D2, false, true);
-        bDer = matMul(a3D2, dyActivation, true, false);
-      } else if (!transposeA && transposeB) {
-        aDer = matMul(dyActivation, b3D2, false, false);
-        bDer = matMul(dyActivation, a3D2, true, false);
-      } else if (transposeA && !transposeB) {
-        aDer = matMul(b3D2, dyActivation, false, true);
-        bDer = matMul(a3D2, dyActivation, false, false);
-      } else {
-        aDer = matMul(b3D2, dyActivation, true, true);
-        bDer = matMul(dyActivation, a3D2, true, true);
-      }
-      if (bias != null) {
-        const biasDer = getFusedBiasGradient($bias2, dyActivation);
-        return [aDer, bDer, biasDer];
-      } else {
-        return [aDer, bDer];
-      }
-    };
-    const forward = (backend2) => {
-      const y = backend2.fusedBatchMatMul({
-        a: a3D,
-        b: b3D,
-        transposeA,
-        transposeB,
-        bias: $bias,
-        activation,
-        preluActivationWeights: $preluActivationWeights
-      });
-      return y;
-    };
-    const inputs = {
-      a: a3D,
-      b: b3D,
-      bias: $bias,
-      preluActivationWeights: $preluActivationWeights
-    };
-    const attrs = {transposeA, transposeB, activation};
-    if (bias == null) {
-      const customOp = customGrad((a3D2, b3D2, save) => {
-        const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-        save([a3D2, b3D2, res]);
-        return {value: reshape(res, outShape), gradFunc: grad2};
-      });
-      return customOp(a3D, b3D);
-    } else {
-      const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
-        const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-        save([a3D2, b3D2, res, $bias2]);
-        return {value: reshape(res, outShape), gradFunc: grad2};
-      });
-      return customOpWithBias(a3D, b3D, $bias);
-    }
-  }
-  const matMul2 = op({fusedMatMul_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
-  const fused_ops_exports = {};
-  __export(fused_ops_exports, {
-    conv2d: () => conv2d5,
-    depthwiseConv2d: () => depthwiseConv2d2,
-    matMul: () => matMul2
-  });
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function hammingWindow_(windowLength) {
-    return cosineWindow(windowLength, 0.54, 0.46);
-  }
-  const hammingWindow = op({hammingWindow_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function hannWindow_(windowLength) {
-    return cosineWindow(windowLength, 0.5, 0.5);
-  }
-  const hannWindow = op({hannWindow_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
-    let start = 0;
-    const output = [];
-    while (start + frameLength <= signal2.size) {
-      output.push(slice(signal2, start, frameLength));
-      start += frameStep;
-    }
-    if (padEnd) {
-      while (start < signal2.size) {
-        const padLen = start + frameLength - signal2.size;
-        const pad8 = concat([
-          slice(signal2, start, frameLength - padLen),
-          fill([padLen], padValue)
-        ]);
-        output.push(pad8);
-        start += frameStep;
-      }
-    }
-    if (output.length === 0) {
-      return tensor2d([], [0, frameLength]);
-    }
-    return reshape(concat(output), [output.length, frameLength]);
-  }
-  const frame = op({frame_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
-    if (fftLength == null) {
-      fftLength = enclosingPowerOfTwo(frameLength);
-    }
-    const framedSignal = frame(signal2, frameLength, frameStep);
-    const windowedSignal = mul(framedSignal, windowFn(frameLength));
-    const output = [];
-    for (let i = 0; i < framedSignal.shape[0]; i++) {
-      output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
-    }
-    return concat(output);
-  }
-  const stft = op({stft_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function cropAndResize_(image2, boxes, boxInd, cropSize, method, extrapolationValue) {
-    const $image = convertToTensor(image2, "image", "cropAndResize");
-    const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
-    const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
-    method = method || "bilinear";
-    extrapolationValue = extrapolationValue || 0;
-    const numBoxes = $boxes.shape[0];
-    assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
-    assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
-    assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
-    assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
-    assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-    assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
-    const forward = (backend2) => backend2.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
-    const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
-    const attrs = {method, extrapolationValue, cropSize};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, CropAndResize, attrs);
-    return res;
-  }
-  const cropAndResize = op({cropAndResize_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function flipLeftRight_(image2) {
-    const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
-    assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
-    const inputs = {image: $image};
-    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
-    return res;
-  }
-  const flipLeftRight = op({flipLeftRight_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
-    const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
-    assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
-    const inputs = {image: $image};
-    const attrs = {radians, fillValue, center};
-    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
-    return res;
-  }
-  const rotateWithOffset = op({rotateWithOffset_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-    if (iouThreshold == null) {
-      iouThreshold = 0.5;
-    }
-    if (scoreThreshold == null) {
-      scoreThreshold = Number.NEGATIVE_INFINITY;
-    }
-    if (softNmsSigma == null) {
-      softNmsSigma = 0;
-    }
-    const numBoxes = boxes.shape[0];
-    maxOutputSize = Math.min(maxOutputSize, numBoxes);
-    assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
-    assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
-    assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
-    assert(scores.rank === 1, () => "scores must be a 1D tensor");
-    assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
-    assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
-    return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-    maxOutputSize = inputs.maxOutputSize;
-    iouThreshold = inputs.iouThreshold;
-    scoreThreshold = inputs.scoreThreshold;
-    const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
-    return ENGINE.runKernelFunc((b) => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), {boxes: $boxes, scores: $scores}, null, NonMaxSuppressionV3, attrs);
-  }
-  const nonMaxSuppression = op({nonMaxSuppression_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/array_util.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function binaryInsert(arr, element, comparator) {
-    const index = binarySearch(arr, element, comparator);
-    const insertionPoint = index < 0 ? -(index + 1) : index;
-    arr.splice(insertionPoint, 0, element);
-  }
-  function binarySearch(arr, target, comparator) {
-    return binarySearch_(arr, target, comparator || defaultComparator);
-  }
-  function defaultComparator(a, b) {
-    return a > b ? 1 : a < b ? -1 : 0;
-  }
-  function binarySearch_(arr, target, comparator) {
-    let left = 0;
-    let right = arr.length;
-    let middle = 0;
-    let found = false;
-    while (left < right) {
-      middle = left + (right - left >>> 1);
-      const compareResult = comparator(target, arr[middle]);
-      if (compareResult > 0) {
-        left = middle + 1;
-      } else {
-        right = middle;
-        found = !compareResult;
-      }
-    }
-    return found ? left : -left - 1;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0).selectedIndices;
-  }
-  function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
-    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
-  }
-  function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
-  }
-  function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
-    const candidates = [];
-    for (let i = 0; i < scores.length; i++) {
-      if (scores[i] > scoreThreshold) {
-        candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
-      }
-    }
-    candidates.sort(ascendingComparator);
-    const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
-    const selectedIndices = [];
-    const selectedScores = [];
-    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
-      const candidate = candidates.pop();
-      const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
-      if (originalScore < scoreThreshold) {
-        break;
-      }
-      let ignoreCandidate = false;
-      for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
-        const iou3 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
-        if (iou3 >= iouThreshold) {
-          ignoreCandidate = true;
-          break;
-        }
-        candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou3);
-        if (candidate.score <= scoreThreshold) {
-          break;
-        }
-      }
-      candidate.suppressBeginIndex = selectedIndices.length;
-      if (!ignoreCandidate) {
-        if (candidate.score === originalScore) {
-          selectedIndices.push(boxIndex);
-          selectedScores.push(candidate.score);
-        } else if (candidate.score > scoreThreshold) {
-          binaryInsert(candidates, candidate, ascendingComparator);
-        }
-      }
-    }
-    const validOutputs = selectedIndices.length;
-    const elemsToPad = maxOutputSize - validOutputs;
-    if (padToMaxOutputSize && elemsToPad > 0) {
-      selectedIndices.push(...new Array(elemsToPad).fill(0));
-      selectedScores.push(...new Array(elemsToPad).fill(0));
-    }
-    const result = {selectedIndices: tensor1d(selectedIndices, "int32")};
-    if (returnScoresTensor) {
-      result["selectedScores"] = tensor1d(selectedScores, "float32");
-    }
-    if (returnValidOutputs) {
-      result["validOutputs"] = scalar(validOutputs, "int32");
-    }
-    return result;
-  }
-  function intersectionOverUnion(boxes, i, j) {
-    const iCoord = boxes.subarray(i * 4, i * 4 + 4);
-    const jCoord = boxes.subarray(j * 4, j * 4 + 4);
-    const yminI = Math.min(iCoord[0], iCoord[2]);
-    const xminI = Math.min(iCoord[1], iCoord[3]);
-    const ymaxI = Math.max(iCoord[0], iCoord[2]);
-    const xmaxI = Math.max(iCoord[1], iCoord[3]);
-    const yminJ = Math.min(jCoord[0], jCoord[2]);
-    const xminJ = Math.min(jCoord[1], jCoord[3]);
-    const ymaxJ = Math.max(jCoord[0], jCoord[2]);
-    const xmaxJ = Math.max(jCoord[1], jCoord[3]);
-    const areaI = (ymaxI - yminI) * (xmaxI - xminI);
-    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-    if (areaI <= 0 || areaJ <= 0) {
-      return 0;
-    }
-    const intersectionYmin = Math.max(yminI, yminJ);
-    const intersectionXmin = Math.max(xminI, xminJ);
-    const intersectionYmax = Math.min(ymaxI, ymaxJ);
-    const intersectionXmax = Math.min(xmaxI, xmaxJ);
-    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
-    return intersectionArea / (areaI + areaJ - intersectionArea);
-  }
-  function suppressWeight(iouThreshold, scale2, iou3) {
-    const weight = Math.exp(scale2 * iou3 * iou3);
-    return iou3 <= iouThreshold ? weight : 0;
-  }
-  function ascendingComparator(c1, c2) {
-    return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-    maxOutputSize = inputs.maxOutputSize;
-    iouThreshold = inputs.iouThreshold;
-    scoreThreshold = inputs.scoreThreshold;
-    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-    const boxesVals = boxesAndScores[0];
-    const scoresVals = boxesAndScores[1];
-    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-    if ($boxes !== boxes) {
-      $boxes.dispose();
-    }
-    if ($scores !== scores) {
-      $scores.dispose();
-    }
-    return res;
-  }
-  const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-    maxOutputSize = params.maxOutputSize;
-    iouThreshold = params.iouThreshold;
-    scoreThreshold = params.scoreThreshold;
-    softNmsSigma = params.softNmsSigma;
-    const inputs = {boxes: $boxes, scores: $scores};
-    const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
-    return {selectedIndices: result[0], selectedScores: result[1]};
-  }
-  const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-    maxOutputSize = params.maxOutputSize;
-    iouThreshold = params.iouThreshold;
-    scoreThreshold = params.scoreThreshold;
-    softNmsSigma = params.softNmsSigma;
-    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-    const boxesVals = boxesAndScores[0];
-    const scoresVals = boxesAndScores[1];
-    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-    if ($boxes !== boxes) {
-      $boxes.dispose();
-    }
-    if ($scores !== scores) {
-      $scores.dispose();
-    }
-    return res;
-  }
-  const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-    const $maxOutputSize = params.maxOutputSize;
-    const $iouThreshold = params.iouThreshold;
-    const $scoreThreshold = params.scoreThreshold;
-    const inputs = {boxes: $boxes, scores: $scores};
-    const attrs = {
-      maxOutputSize: $maxOutputSize,
-      iouThreshold: $iouThreshold,
-      scoreThreshold: $scoreThreshold,
-      padToMaxOutputSize
-    };
-    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
-    return {selectedIndices: result[0], validOutputs: result[1]};
-  }
-  const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-    const $maxOutputSize = params.maxOutputSize;
-    const $iouThreshold = params.iouThreshold;
-    const $scoreThreshold = params.scoreThreshold;
-    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
-    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
-    if ($boxes !== boxes) {
-      $boxes.dispose();
-    }
-    if ($scores !== scores) {
-      $scores.dispose();
-    }
-    return res;
-  }
-  const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function resizeBilinear_(images, size, alignCorners = false) {
-    const $images = convertToTensor(images, "images", "resizeBilinear");
-    assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-    assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
-    let batchImages = $images;
-    let reshapedTo4D = false;
-    if ($images.rank === 3) {
-      reshapedTo4D = true;
-      batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-    }
-    const [newHeight, newWidth] = size;
-    const forward = (backend2, save) => {
-      save([batchImages]);
-      return backend2.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
-    };
-    const inputs = {images: batchImages};
-    const attrs = {alignCorners, size};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeBilinear, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const resizeBilinear = op({resizeBilinear_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function resizeNearestNeighbor_(images, size, alignCorners = false) {
-    const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
-    assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-    assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
-    assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
-    let batchImages = $images;
-    let reshapedTo4D = false;
-    if ($images.rank === 3) {
-      reshapedTo4D = true;
-      batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-    }
-    const [newHeight, newWidth] = size;
-    const inputs = {images: batchImages};
-    const attrs = {alignCorners, size};
-    const forward = (backend2, save) => {
-      save([batchImages]);
-      return backend2.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
-    };
-    const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeNearestNeighbor, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const resizeNearestNeighbor = op({resizeNearestNeighbor_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function bandPart_(a, numLower, numUpper) {
-    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
-    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
-    const $a = convertToTensor(a, "a", "bandPart");
-    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
-    const shape = $a.shape;
-    const [M, N] = $a.shape.slice(-2);
-    if (!(numLower <= M)) {
-      throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
-    }
-    if (!(numUpper <= N)) {
-      throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
-    }
-    if (numLower < 0) {
-      numLower = M;
-    }
-    if (numUpper < 0) {
-      numUpper = N;
-    }
-    const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
-    const j = range(0, N, 1, "int32");
-    const ij = sub(i, j);
-    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
-    const zero = zeros([M, N], $a.dtype);
-    return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
-  }
-  const bandPart = op({bandPart_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function gramSchmidt_(xs) {
-    let inputIsTensor2D;
-    if (Array.isArray(xs)) {
-      inputIsTensor2D = false;
-      assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
-      const dim = xs[0].shape[0];
-      for (let i = 1; i < xs.length; ++i) {
-        assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
-      }
-    } else {
-      inputIsTensor2D = true;
-      xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
-    }
-    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
-    const ys = [];
-    const xs1d = xs;
-    for (let i = 0; i < xs.length; ++i) {
-      ys.push(ENGINE.tidy(() => {
-        let x = xs1d[i];
-        if (i > 0) {
-          for (let j = 0; j < i; ++j) {
-            const proj = mul(sum2(mul(ys[j], x)), ys[j]);
-            x = sub(x, proj);
-          }
-        }
-        return div(x, norm(x, "euclidean"));
-      }));
-    }
-    if (inputIsTensor2D) {
-      return stack(ys, 0);
-    } else {
-      return ys;
-    }
-  }
-  const gramSchmidt = op({gramSchmidt_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function qr_(x, fullMatrices = false) {
-    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
-    if (x.rank === 2) {
-      return qr2d(x, fullMatrices);
-    } else {
-      const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
-      const x2ds = unstack(reshape(x, [
-        outerDimsProd,
-        x.shape[x.shape.length - 2],
-        x.shape[x.shape.length - 1]
-      ]), 0);
-      const q2ds = [];
-      const r2ds = [];
-      x2ds.forEach((x2d) => {
-        const [q2d, r2d] = qr2d(x2d, fullMatrices);
-        q2ds.push(q2d);
-        r2ds.push(r2d);
-      });
-      const q = reshape(stack(q2ds, 0), x.shape);
-      const r = reshape(stack(r2ds, 0), x.shape);
-      return [q, r];
-    }
-  }
-  function qr2d(x, fullMatrices = false) {
-    return ENGINE.tidy(() => {
-      assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
-      const m = x.shape[0];
-      const n = x.shape[1];
-      let q = eye(m);
-      let r = clone(x);
-      const one2D = tensor2d([[1]], [1, 1]);
-      let w = clone(one2D);
-      const iters = m >= n ? n : m;
-      for (let j = 0; j < iters; ++j) {
-        const rTemp = r;
-        const wTemp = w;
-        const qTemp = q;
-        [w, r, q] = ENGINE.tidy(() => {
-          const rjEnd1 = slice(r, [j, j], [m - j, 1]);
-          const normX = norm(rjEnd1);
-          const rjj = slice(r, [j, j], [1, 1]);
-          const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-          const u1 = sub(rjj, mul(s, normX));
-          const wPre = div(rjEnd1, u1);
-          if (wPre.shape[0] === 1) {
-            w = clone(one2D);
-          } else {
-            w = concat([
-              one2D,
-              slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
-            ], 0);
-          }
-          const tau = neg(div(matMul(s, u1), normX));
-          const rjEndAll = slice(r, [j, 0], [m - j, n]);
-          const tauTimesW = mul(tau, w);
-          const wT = transpose(w);
-          if (j === 0) {
-            r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          } else {
-            const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-            r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
-          }
-          const tawTimesWT = transpose(tauTimesW);
-          const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
-          if (j === 0) {
-            q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-          } else {
-            const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-            q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
-          }
-          return [w, r, q];
-        });
-        dispose([rTemp, wTemp, qTemp]);
-      }
-      if (!fullMatrices && m > n) {
-        q = slice(q, [0, 0], [m, n]);
-        r = slice(r, [0, 0], [n, n]);
-      }
-      return [q, r];
-    });
-  }
-  const qr = op({qr_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  var Reduction;
-  (function(Reduction2) {
-    Reduction2[Reduction2["NONE"] = 0] = "NONE";
-    Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
-    Reduction2[Reduction2["SUM"] = 2] = "SUM";
-    Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
-  })(Reduction || (Reduction = {}));
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
-  function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
-    }
-    const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
-    if (reduction === Reduction.NONE) {
-      return weightedLoss;
-    }
-    if (reduction === Reduction.SUM) {
-      return sum2(weightedLoss);
-    }
-    if (reduction === Reduction.MEAN) {
-      if ($weights == null) {
-        return mean(weightedLoss);
-      } else {
-        const broadcastFactor = $losses.size / $weights.size;
-        const result = div(sum2(weightedLoss), sum2($weights));
-        return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
-      }
-    }
-    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
-      if ($weights == null) {
-        return div(sum2(weightedLoss), scalar($losses.size));
-      } else {
-        const broadcastedWeights = mul($weights, ones2($losses.shape));
-        const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
-        return div(sum2(weightedLoss), numNonZeros);
-      }
-    }
-    throw Error(`Unknown reduction: ${reduction}`);
-  }
-  const computeWeightedLoss = op({computeWeightedLoss_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $labels = convertToTensor(labels, "labels", "absoluteDifference");
-    const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "absoluteDifference");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
-    const losses2 = abs(sub($labels, $predictions));
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const absoluteDifference = op({absoluteDifference_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
-  function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $labels = convertToTensor(labels, "labels", "cosineDistance");
-    const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "cosineDistance");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
-    const one = scalar(1);
-    const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const cosineDistance = op({cosineDistance_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
-  function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    let $labels = convertToTensor(labels, "labels", "hingeLoss");
-    const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "hingeLoss");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
-    const one = scalar(1);
-    $labels = sub(mul(scalar(2), $labels), one);
-    const losses2 = relu(sub(one, mul($labels, $predictions)));
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const hingeLoss = op({hingeLoss_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $labels = convertToTensor(labels, "labels", "huberLoss");
-    const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "huberLoss");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
-    const deltaScalar = scalar(delta);
-    const error = abs(sub($predictions, $labels));
-    const quadratic = minimum(error, deltaScalar);
-    const linear = sub(error, quadratic);
-    const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const huberLoss = op({huberLoss_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function logLoss_(labels, predictions, weights, epsilon2 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $labels = convertToTensor(labels, "labels", "logLoss");
-    const $predictions = convertToTensor(predictions, "predictions", "logLoss");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "logLoss");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
-    const one = scalar(1);
-    const epsilonScalar = scalar(epsilon2);
-    const l1 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
-    const l2 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
-    const losses2 = sub(l1, l2);
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const logLoss = op({logLoss_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    const $labels = convertToTensor(labels, "labels", "meanSquaredError");
-    const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "meanSquaredError");
-    }
-    assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
-    const losses2 = squaredDifference($labels, $predictions);
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const meanSquaredError = op({meanSquaredError_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function sigmoidCrossEntropyWithLogits_(labels, logits) {
-    const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
-    const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
-    assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-    const maxOutput = relu($logits);
-    const outputXTarget = mul($logits, $labels);
-    const sigmoidOutput = log1p(exp(neg(abs($logits))));
-    return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
-  }
-  function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
-    const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
-    }
-    assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
-    if (labelSmoothing > 0) {
-      const labelSmoothingScalar = scalar(labelSmoothing);
-      const one = scalar(1);
-      const half = scalar(0.5);
-      $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
-    }
-    const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
-    if (dim === -1) {
-      dim = logits.rank - 1;
-    }
-    if (dim !== logits.rank - 1) {
-      throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
-    }
-    const customOp = customGrad((labels2, logits2, save) => {
-      const keepDims = true;
-      const lse = logSumExp(logits2, [dim], keepDims);
-      const logResult = sub(cast(logits2, "float32"), lse);
-      save([labels2, logResult]);
-      const costVector = neg(mul(logResult, labels2));
-      const value = sum2(costVector, [dim]);
-      const gradFunc = (dy, saved) => {
-        const [labels3, logResult2] = saved;
-        const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
-        return [
-          mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
-          mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
-        ];
-      };
-      return {value, gradFunc};
-    });
-    return customOp(labels, logits);
-  }
-  function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
-    const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
-    let $weights = null;
-    if (weights != null) {
-      $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
-    }
-    assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
-    if (labelSmoothing > 0) {
-      const labelSmoothingScalar = scalar(labelSmoothing);
-      const one = scalar(1);
-      const numClasses = scalar($onehotLabels.shape[1]);
-      $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
-    }
-    const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
-    return computeWeightedLoss(losses2, $weights, reduction);
-  }
-  const softmaxCrossEntropy = op({softmaxCrossEntropy_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const spectral = {
-    fft,
-    ifft,
-    rfft,
-    irfft
-  };
-  const signal = {
-    hammingWindow,
-    hannWindow,
-    frame,
-    stft
-  };
-  const image = {
-    flipLeftRight,
-    resizeNearestNeighbor,
-    resizeBilinear,
-    rotateWithOffset,
-    cropAndResize,
-    nonMaxSuppression,
-    nonMaxSuppressionAsync,
-    nonMaxSuppressionWithScore,
-    nonMaxSuppressionWithScoreAsync,
-    nonMaxSuppressionPadded,
-    nonMaxSuppressionPaddedAsync
-  };
-  const linalg = {
-    bandPart,
-    gramSchmidt,
-    qr
-  };
-  const losses = {
-    absoluteDifference,
-    computeWeightedLoss,
-    cosineDistance,
-    hingeLoss,
-    huberLoss,
-    logLoss,
-    meanSquaredError,
-    sigmoidCrossEntropy,
-    softmaxCrossEntropy
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class Optimizer extends Serializable {
-    minimize(f, returnCost = false, varList) {
-      const {value, grads: grads2} = this.computeGradients(f, varList);
-      if (varList != null) {
-        const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
-        this.applyGradients(gradArray);
-      } else {
-        this.applyGradients(grads2);
-      }
-      dispose(grads2);
-      if (returnCost) {
-        return value;
-      } else {
-        value.dispose();
-        return null;
-      }
-    }
-    get iterations() {
-      if (this.iterations_ == null) {
-        this.iterations_ = 0;
-      }
-      return this.iterations_;
-    }
-    incrementIterations() {
-      this.iterations_ = this.iterations + 1;
-    }
-    computeGradients(f, varList) {
-      return variableGrads(f, varList);
-    }
-    dispose() {
-      if (this.iterations_ != null) {
-        dispose(this.iterations_);
-      }
-    }
-    async saveIterations() {
-      if (this.iterations_ == null) {
-        this.iterations_ = 0;
-      }
-      return {
-        name: "iter",
-        tensor: scalar(this.iterations_, "int32")
-      };
-    }
-    async getWeights() {
-      throw new Error("getWeights() is not implemented for this optimizer yet.");
-    }
-    async setWeights(weightValues) {
-      throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
-    }
-    async extractIterations(weightValues) {
-      this.iterations_ = (await weightValues[0].tensor.data())[0];
-      return weightValues.slice(1);
-    }
-  }
-  Object.defineProperty(Optimizer, Symbol.hasInstance, {
-    value: (instance) => {
-      return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
-    }
-  });
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class AdadeltaOptimizer extends Optimizer {
-    constructor(learningRate, rho, epsilon2 = null) {
-      super();
-      this.learningRate = learningRate;
-      this.rho = rho;
-      this.epsilon = epsilon2;
-      this.accumulatedGrads = [];
-      this.accumulatedUpdates = [];
-      if (epsilon2 == null) {
-        this.epsilon = ENGINE.backend.epsilon();
-      }
-    }
-    applyGradients(variableGradients) {
-      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedGrads[i] == null) {
-          this.accumulatedGrads[i] = {
-            originalName: `${name}/accum_grad`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedUpdates[i] == null) {
-          this.accumulatedUpdates[i] = {
-            originalName: `${name}/accum_var`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const accumulatedGrad = this.accumulatedGrads[i].variable;
-        const accumulatedUpdate = this.accumulatedUpdates[i].variable;
-        tidy(() => {
-          const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
-          const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
-          const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
-          accumulatedGrad.assign(newAccumulatedGrad);
-          accumulatedUpdate.assign(newAccumulatedUpdate);
-          const newValue = add2(mul(updates, -this.learningRate), value);
-          value.assign(newValue);
-        });
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      if (this.accumulatedUpdates != null) {
-        dispose(this.accumulatedGrads.map((v) => v.variable));
-        dispose(this.accumulatedUpdates.map((v) => v.variable));
-      }
-    }
-    async getWeights() {
-      const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
-      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      const variableCount = weightValues.length / 2;
-      const trainable = false;
-      this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-      this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        rho: this.rho,
-        epsilon: this.epsilon
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["rho"], config2["epsilon"]);
-    }
-  }
-  AdadeltaOptimizer.className = "Adadelta";
-  registerClass(AdadeltaOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class AdagradOptimizer extends Optimizer {
-    constructor(learningRate, initialAccumulatorValue = 0.1) {
-      super();
-      this.learningRate = learningRate;
-      this.initialAccumulatorValue = initialAccumulatorValue;
-      this.accumulatedGrads = [];
-    }
-    applyGradients(variableGradients) {
-      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        if (this.accumulatedGrads[i] == null) {
-          const trainable = false;
-          this.accumulatedGrads[i] = {
-            originalName: `${name}/accumulator`,
-            variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const accumulatedGrad = this.accumulatedGrads[i].variable;
-        tidy(() => {
-          const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
-          accumulatedGrad.assign(newAccumulatedGrad);
-          const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
-          value.assign(newValue);
-        });
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      if (this.accumulatedGrads != null) {
-        dispose(this.accumulatedGrads.map((v) => v.variable));
-      }
-    }
-    async getWeights() {
-      return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      const trainable = false;
-      this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        initialAccumulatorValue: this.initialAccumulatorValue
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["initialAccumulatorValue"]);
-    }
-  }
-  AdagradOptimizer.className = "Adagrad";
-  registerClass(AdagradOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class AdamOptimizer extends Optimizer {
-    constructor(learningRate, beta1, beta2, epsilon2 = null) {
-      super();
-      this.learningRate = learningRate;
-      this.beta1 = beta1;
-      this.beta2 = beta2;
-      this.epsilon = epsilon2;
-      this.accumulatedFirstMoment = [];
-      this.accumulatedSecondMoment = [];
-      tidy(() => {
-        this.accBeta1 = scalar(beta1).variable();
-        this.accBeta2 = scalar(beta2).variable();
-      });
-      if (epsilon2 == null) {
-        this.epsilon = ENGINE.backend.epsilon();
-      }
-    }
-    applyGradients(variableGradients) {
-      const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-      tidy(() => {
-        const oneMinusAccBeta1 = sub(1, this.accBeta1);
-        const oneMinusAccBeta2 = sub(1, this.accBeta2);
-        varNames.forEach((name, i) => {
-          const value = ENGINE.registeredVariables[name];
-          const trainable = false;
-          if (this.accumulatedFirstMoment[i] == null) {
-            this.accumulatedFirstMoment[i] = {
-              originalName: `${name}/m`,
-              variable: tidy(() => zerosLike(value).variable(trainable))
-            };
-          }
-          if (this.accumulatedSecondMoment[i] == null) {
-            this.accumulatedSecondMoment[i] = {
-              originalName: `${name}/v`,
-              variable: tidy(() => zerosLike(value).variable(trainable))
-            };
-          }
-          const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-          if (gradient == null) {
-            return;
-          }
-          const firstMoment = this.accumulatedFirstMoment[i].variable;
-          const secondMoment = this.accumulatedSecondMoment[i].variable;
-          const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-          const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
-          const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
-          const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
-          firstMoment.assign(newFirstMoment);
-          secondMoment.assign(newSecondMoment);
-          const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
-          value.assign(newValue);
-        });
-        this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-        this.accBeta2.assign(mul(this.accBeta2, this.beta2));
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      this.accBeta1.dispose();
-      this.accBeta2.dispose();
-      if (this.accumulatedFirstMoment != null) {
-        dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-      }
-      if (this.accumulatedSecondMoment != null) {
-        dispose(this.accumulatedSecondMoment.map((v) => v.variable));
-      }
-    }
-    async getWeights() {
-      const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
-      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      tidy(() => {
-        this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
-        this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
-      });
-      const variableCount = weightValues.length / 2;
-      const trainable = false;
-      this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-      this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        beta1: this.beta1,
-        beta2: this.beta2,
-        epsilon: this.epsilon
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"]);
-    }
-  }
-  AdamOptimizer.className = "Adam";
-  registerClass(AdamOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class AdamaxOptimizer extends Optimizer {
-    constructor(learningRate, beta1, beta2, epsilon2 = null, decay = 0) {
-      super();
-      this.learningRate = learningRate;
-      this.beta1 = beta1;
-      this.beta2 = beta2;
-      this.epsilon = epsilon2;
-      this.decay = decay;
-      this.accumulatedFirstMoment = [];
-      this.accumulatedWeightedInfNorm = [];
-      tidy(() => {
-        this.iteration = scalar(0).variable();
-        this.accBeta1 = scalar(beta1).variable();
-      });
-      if (epsilon2 == null) {
-        this.epsilon = ENGINE.backend.epsilon();
-      }
-    }
-    applyGradients(variableGradients) {
-      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-      tidy(() => {
-        const oneMinusAccBeta1 = sub(1, this.accBeta1);
-        const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-        variableNames.forEach((name, i) => {
-          const value = ENGINE.registeredVariables[name];
-          const trainable = false;
-          if (this.accumulatedFirstMoment[i] == null) {
-            this.accumulatedFirstMoment[i] = {
-              originalName: `${name}/m`,
-              variable: zerosLike(value).variable(trainable)
-            };
-          }
-          if (this.accumulatedWeightedInfNorm[i] == null) {
-            this.accumulatedWeightedInfNorm[i] = {
-              originalName: `${name}/v`,
-              variable: zerosLike(value).variable(trainable)
-            };
-          }
-          const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-          if (gradient == null) {
-            return;
-          }
-          const firstMoment = this.accumulatedFirstMoment[i].variable;
-          const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
-          const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-          const ut0 = mul(weightedInfNorm, this.beta2);
-          const ut1 = abs(gradient);
-          const newWeightedInfNorm = maximum(ut0, ut1);
-          firstMoment.assign(newFirstMoment);
-          weightedInfNorm.assign(newWeightedInfNorm);
-          const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
-          value.assign(newValue);
-        });
-        this.iteration.assign(add2(this.iteration, 1));
-        this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      this.accBeta1.dispose();
-      this.iteration.dispose();
-      if (this.accumulatedFirstMoment != null) {
-        dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-      }
-      if (this.accumulatedWeightedInfNorm != null) {
-        dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
-      }
-    }
-    async getWeights() {
-      throw new Error("getWeights() is not implemented for Adamax yet.");
-    }
-    async setWeights(weightValues) {
-      throw new Error("setWeights() is not implemented for Adamax yet.");
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        beta1: this.beta1,
-        beta2: this.beta2,
-        epsilon: this.epsilon,
-        decay: this.decay
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"], config2["decay"]);
-    }
-  }
-  AdamaxOptimizer.className = "Adamax";
-  registerClass(AdamaxOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class SGDOptimizer extends Optimizer {
-    constructor(learningRate) {
-      super();
-      this.learningRate = learningRate;
-      this.setLearningRate(learningRate);
-    }
-    applyGradients(variableGradients) {
-      const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-      varNames.forEach((name, i) => {
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const value = ENGINE.registeredVariables[name];
-        tidy(() => {
-          const newValue = add2(mul(this.c, gradient), value);
-          value.assign(newValue);
-        });
-      });
-      this.incrementIterations();
-    }
-    setLearningRate(learningRate) {
-      this.learningRate = learningRate;
-      if (this.c != null) {
-        this.c.dispose();
-      }
-      this.c = keep(scalar(-learningRate));
-    }
-    dispose() {
-      this.c.dispose();
-    }
-    async getWeights() {
-      return [await this.saveIterations()];
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      if (weightValues.length !== 0) {
-        throw new Error("SGD optimizer does not have settable weights.");
-      }
-    }
-    getConfig() {
-      return {learningRate: this.learningRate};
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"]);
-    }
-  }
-  SGDOptimizer.className = "SGD";
-  registerClass(SGDOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class MomentumOptimizer extends SGDOptimizer {
-    constructor(learningRate, momentum, useNesterov = false) {
-      super(learningRate);
-      this.learningRate = learningRate;
-      this.momentum = momentum;
-      this.useNesterov = useNesterov;
-      this.accumulations = [];
-      this.m = scalar(this.momentum);
-    }
-    applyGradients(variableGradients) {
-      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        if (this.accumulations[i] == null) {
-          const trainable = false;
-          this.accumulations[i] = {
-            originalName: `${name}/momentum`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const accumulation = this.accumulations[i].variable;
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        tidy(() => {
-          let newValue;
-          const newAccumulation = add2(mul(this.m, accumulation), gradient);
-          if (this.useNesterov) {
-            newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
-          } else {
-            newValue = add2(mul(this.c, newAccumulation), value);
-          }
-          accumulation.assign(newAccumulation);
-          value.assign(newValue);
-        });
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      this.m.dispose();
-      if (this.accumulations != null) {
-        dispose(this.accumulations.map((v) => v.variable));
-      }
-    }
-    setMomentum(momentum) {
-      this.momentum = momentum;
-    }
-    async getWeights() {
-      return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      const trainable = false;
-      this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        momentum: this.momentum,
-        useNesterov: this.useNesterov
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["momentum"], config2["useNesterov"]);
-    }
-  }
-  MomentumOptimizer.className = "Momentum";
-  registerClass(MomentumOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class RMSPropOptimizer extends Optimizer {
-    constructor(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-      super();
-      this.learningRate = learningRate;
-      this.decay = decay;
-      this.momentum = momentum;
-      this.epsilon = epsilon2;
-      this.accumulatedMeanSquares = [];
-      this.accumulatedMoments = [];
-      this.accumulatedMeanGrads = [];
-      this.centered = centered;
-      if (epsilon2 == null) {
-        this.epsilon = ENGINE.backend.epsilon();
-      }
-      if (learningRate == null) {
-        throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
-      }
-    }
-    applyGradients(variableGradients) {
-      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedMeanSquares[i] == null) {
-          this.accumulatedMeanSquares[i] = {
-            originalName: `${name}/rms`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedMoments[i] == null) {
-          this.accumulatedMoments[i] = {
-            originalName: `${name}/momentum`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedMeanGrads[i] == null && this.centered) {
-          this.accumulatedMeanGrads[i] = {
-            originalName: `${name}/mg`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
-        const accumulatedMoments = this.accumulatedMoments[i].variable;
-        tidy(() => {
-          const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-          if (this.centered) {
-            const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
-            const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
-            const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
-            const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
-            accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-            accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
-            accumulatedMoments.assign(newAccumulatedMoments);
-            const newValue = sub(value, newAccumulatedMoments);
-            value.assign(newValue);
-          } else {
-            const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-            const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
-            accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
-            accumulatedMoments.assign(newAccumulatedMoments);
-            const newValue = sub(value, newAccumulatedMoments);
-            value.assign(newValue);
-          }
-        });
-      });
-      this.incrementIterations();
-    }
-    dispose() {
-      if (this.accumulatedMeanSquares != null) {
-        dispose(this.accumulatedMeanSquares.map((v) => v.variable));
-      }
-      if (this.accumulatedMeanGrads != null && this.centered) {
-        dispose(this.accumulatedMeanGrads.map((v) => v.variable));
-      }
-      if (this.accumulatedMoments != null) {
-        dispose(this.accumulatedMoments.map((v) => v.variable));
-      }
-    }
-    async getWeights() {
-      const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
-      if (this.centered) {
-        variables.push(...this.accumulatedMeanGrads);
-      }
-      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-    }
-    async setWeights(weightValues) {
-      weightValues = await this.extractIterations(weightValues);
-      const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
-      const trainable = false;
-      this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-      this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-      if (this.centered) {
-        this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
-          originalName: v.name,
-          variable: v.tensor.variable(trainable)
-        }));
-      }
-    }
-    getConfig() {
-      return {
-        learningRate: this.learningRate,
-        decay: this.decay,
-        momentum: this.momentum,
-        epsilon: this.epsilon,
-        centered: this.centered
-      };
-    }
-    static fromConfig(cls, config2) {
-      return new cls(config2["learningRate"], config2["decay"], config2["momentum"], config2["epsilon"], config2["centered"]);
-    }
-  }
-  RMSPropOptimizer.className = "RMSProp";
-  registerClass(RMSPropOptimizer);
-
-  // node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  class OptimizerConstructors {
-    static sgd(learningRate) {
-      return new SGDOptimizer(learningRate);
-    }
-    static momentum(learningRate, momentum, useNesterov = false) {
-      return new MomentumOptimizer(learningRate, momentum, useNesterov);
-    }
-    static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-      return new RMSPropOptimizer(learningRate, decay, momentum, epsilon2, centered);
-    }
-    static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null) {
-      return new AdamOptimizer(learningRate, beta1, beta2, epsilon2);
-    }
-    static adadelta(learningRate = 1e-3, rho = 0.95, epsilon2 = null) {
-      return new AdadeltaOptimizer(learningRate, rho, epsilon2);
-    }
-    static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null, decay = 0) {
-      return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon2, decay);
-    }
-    static adagrad(learningRate, initialAccumulatorValue = 0.1) {
-      return new AdagradOptimizer(learningRate, initialAccumulatorValue);
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/train.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  [
-    MomentumOptimizer,
-    SGDOptimizer,
-    AdadeltaOptimizer,
-    AdagradOptimizer,
-    RMSPropOptimizer,
-    AdamaxOptimizer,
-    AdamOptimizer
-  ];
-  const train = {
-    sgd: OptimizerConstructors.sgd,
-    momentum: OptimizerConstructors.momentum,
-    adadelta: OptimizerConstructors.adadelta,
-    adagrad: OptimizerConstructors.adagrad,
-    rmsprop: OptimizerConstructors.rmsprop,
-    adamax: OptimizerConstructors.adamax,
-    adam: OptimizerConstructors.adam
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/browser_util.js
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const delayCallback = (() => {
-    if (typeof requestAnimationFrame !== "undefined") {
-      return requestAnimationFrame;
-    } else if (typeof setImmediate !== "undefined") {
-      return setImmediate;
-    }
-    return (f) => f();
-  })();
-  function nextFrame() {
-    return new Promise((resolve) => delayCallback(() => resolve()));
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getImageCenter(center, imageHeight, imageWidth) {
-    const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
-    const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
-    return [centerX, centerY];
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function getReshaped(inputShape, blockShape, prod3, batchToSpace = true) {
-    let reshaped = [];
-    if (batchToSpace) {
-      reshaped = reshaped.concat(blockShape.slice(0));
-      reshaped.push(inputShape[0] / prod3);
-      reshaped = reshaped.concat(inputShape.slice(1));
-    } else {
-      reshaped = reshaped.concat(inputShape[0]);
-      const spatialLength = blockShape.length;
-      for (let i = 0; i < spatialLength; ++i) {
-        reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
-      }
-      reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
-    }
-    return reshaped;
-  }
-  function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
-    const permuted = [];
-    if (batchToSpace) {
-      permuted.push(blockShapeRank);
-      for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
-        if (i <= 2 * blockShapeRank) {
-          permuted.push(i);
-          permuted.push(i - (blockShapeRank + 1));
-        } else {
-          permuted.push(i);
-        }
-      }
-    } else {
-      const permutedBeforeBatch = [];
-      const permutedAfterBatch = [];
-      for (let i = 1; i < reshapedRank; ++i) {
-        if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
-          permutedAfterBatch.push(i);
-        } else {
-          permutedBeforeBatch.push(i);
-        }
-      }
-      permuted.push(...permutedBeforeBatch);
-      permuted.push(0);
-      permuted.push(...permutedAfterBatch);
-    }
-    return permuted;
-  }
-  function getReshapedPermuted(inputShape, blockShape, prod3, batchToSpace = true) {
-    const reshapedPermuted = [];
-    if (batchToSpace) {
-      reshapedPermuted.push(inputShape[0] / prod3);
-    } else {
-      reshapedPermuted.push(inputShape[0] * prod3);
-    }
-    for (let i = 1; i < inputShape.length; ++i) {
-      if (i <= blockShape.length) {
-        if (batchToSpace) {
-          reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
-        } else {
-          reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
-        }
-      } else {
-        reshapedPermuted.push(inputShape[i]);
-      }
-    }
-    return reshapedPermuted;
-  }
-  function getSliceBeginCoords(crops, blockShape) {
-    const sliceBeginCoords = [0];
-    for (let i = 0; i < blockShape; ++i) {
-      sliceBeginCoords.push(crops[i][0]);
-    }
-    return sliceBeginCoords;
-  }
-  function getSliceSize(uncroppedShape, crops, blockShape) {
-    const sliceSize = uncroppedShape.slice(0, 1);
-    for (let i = 0; i < blockShape; ++i) {
-      sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
-    }
-    return sliceSize;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const SELU_SCALEALPHA = 1.7580993408473768;
-  const SELU_SCALE = 1.0507009873554805;
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const ERF_P = 0.3275911;
-  const ERF_A1 = 0.254829592;
-  const ERF_A2 = -0.284496736;
-  const ERF_A3 = 1.421413741;
-  const ERF_A4 = -1.453152027;
-  const ERF_A5 = 1.061405429;
-
-  // node_modules/@tensorflow/tfjs-core/dist/log.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function warn(...msg) {
-    if (!env().getBool("IS_TEST")) {
-      console.warn(...msg);
-    }
-  }
-  function log6(...msg) {
-    if (!env().getBool("IS_TEST")) {
-      console.log(...msg);
-    }
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function mergeRealAndImagArrays(real6, imag6) {
-    if (real6.length !== imag6.length) {
-      throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real6.length}, imag: ${imag6.length}.`);
-    }
-    const result = new Float32Array(real6.length * 2);
-    for (let i = 0; i < result.length; i += 2) {
-      result[i] = real6[i / 2];
-      result[i + 1] = imag6[i / 2];
-    }
-    return result;
-  }
-  function splitRealAndImagArrays(complex9) {
-    const real6 = new Float32Array(complex9.length / 2);
-    const imag6 = new Float32Array(complex9.length / 2);
-    for (let i = 0; i < complex9.length; i += 2) {
-      real6[i / 2] = complex9[i];
-      imag6[i / 2] = complex9[i + 1];
-    }
-    return {real: real6, imag: imag6};
-  }
-  function complexWithEvenIndex(complex9) {
-    const len = Math.ceil(complex9.length / 4);
-    const real6 = new Float32Array(len);
-    const imag6 = new Float32Array(len);
-    for (let i = 0; i < complex9.length; i += 4) {
-      real6[Math.floor(i / 4)] = complex9[i];
-      imag6[Math.floor(i / 4)] = complex9[i + 1];
-    }
-    return {real: real6, imag: imag6};
-  }
-  function complexWithOddIndex(complex9) {
-    const len = Math.floor(complex9.length / 4);
-    const real6 = new Float32Array(len);
-    const imag6 = new Float32Array(len);
-    for (let i = 2; i < complex9.length; i += 4) {
-      real6[Math.floor(i / 4)] = complex9[i];
-      imag6[Math.floor(i / 4)] = complex9[i + 1];
-    }
-    return {real: real6, imag: imag6};
-  }
-  function getComplexWithIndex(complex9, index) {
-    const real6 = complex9[index * 2];
-    const imag6 = complex9[index * 2 + 1];
-    return {real: real6, imag: imag6};
-  }
-  function assignToTypedArray(data, real6, imag6, index) {
-    data[index * 2] = real6;
-    data[index * 2 + 1] = imag6;
-  }
-  function exponents(n, inverse) {
-    const real6 = new Float32Array(n / 2);
-    const imag6 = new Float32Array(n / 2);
-    for (let i = 0; i < Math.ceil(n / 2); i++) {
-      const x = (inverse ? 2 : -2) * Math.PI * (i / n);
-      real6[i] = Math.cos(x);
-      imag6[i] = Math.sin(x);
-    }
-    return {real: real6, imag: imag6};
-  }
-  function exponent(k, n, inverse) {
-    const x = (inverse ? 2 : -2) * Math.PI * (k / n);
-    const real6 = Math.cos(x);
-    const imag6 = Math.sin(x);
-    return {real: real6, imag: imag6};
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
-  const backend_util_exports = {};
-  __export(backend_util_exports, {
-    ERF_A1: () => ERF_A1,
-    ERF_A2: () => ERF_A2,
-    ERF_A3: () => ERF_A3,
-    ERF_A4: () => ERF_A4,
-    ERF_A5: () => ERF_A5,
-    ERF_P: () => ERF_P,
-    PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
-    SELU_SCALE: () => SELU_SCALE,
-    SELU_SCALEALPHA: () => SELU_SCALEALPHA,
-    applyActivation: () => applyActivation,
-    assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
-    assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
-    assertParamsConsistent: () => assertParamsConsistent,
-    assignToTypedArray: () => assignToTypedArray,
-    axesAreInnerMostDims: () => axesAreInnerMostDims,
-    calculateShapes: () => calculateShapes,
-    castTensor: () => castTensor,
-    combineLocations: () => combineLocations,
-    complexWithEvenIndex: () => complexWithEvenIndex,
-    complexWithOddIndex: () => complexWithOddIndex,
-    computeConv2DInfo: () => computeConv2DInfo,
-    computeConv3DInfo: () => computeConv3DInfo,
-    computeDefaultPad: () => computeDefaultPad,
-    computeDilation2DInfo: () => computeDilation2DInfo,
-    computeOptimalWindowSize: () => computeOptimalWindowSize,
-    computeOutAndReduceShapes: () => computeOutAndReduceShapes,
-    computeOutShape: () => computeOutShape2,
-    computePool2DInfo: () => computePool2DInfo,
-    computePool3DInfo: () => computePool3DInfo,
-    convertConv2DDataFormat: () => convertConv2DDataFormat,
-    eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
-    expandShapeToKeepDim: () => expandShapeToKeepDim,
-    exponent: () => exponent,
-    exponents: () => exponents,
-    getAxesPermutation: () => getAxesPermutation,
-    getBroadcastDims: () => getBroadcastDims,
-    getComplexWithIndex: () => getComplexWithIndex,
-    getFusedBiasGradient: () => getFusedBiasGradient,
-    getFusedDyActivation: () => getFusedDyActivation,
-    getImageCenter: () => getImageCenter,
-    getInnerMostAxes: () => getInnerMostAxes,
-    getPermuted: () => getPermuted,
-    getReductionAxes: () => getReductionAxes,
-    getReshaped: () => getReshaped,
-    getReshapedPermuted: () => getReshapedPermuted,
-    getSliceBeginCoords: () => getSliceBeginCoords,
-    getSliceSize: () => getSliceSize,
-    getUndoAxesPermutation: () => getUndoAxesPermutation,
-    linspaceImpl: () => linspaceImpl,
-    log: () => log6,
-    mergeRealAndImagArrays: () => mergeRealAndImagArrays,
-    prepareAndValidate: () => prepareAndValidate,
-    prepareSplitSize: () => prepareSplitSize,
-    reshapeTensor: () => reshapeTensor,
-    segment_util: () => segment_util_exports,
-    shouldFuse: () => shouldFuse,
-    slice_util: () => slice_util_exports,
-    splitRealAndImagArrays: () => splitRealAndImagArrays,
-    tupleValuesAreOne: () => tupleValuesAreOne,
-    upcastType: () => upcastType,
-    validateInput: () => validateInput,
-    validateUpdateShape: () => validateUpdateShape,
-    warn: () => warn
-  });
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function castTensor(x, dtype, backend2) {
-    if (dtype === "complex64") {
-      if (x.dtype === "complex64") {
-        return x.clone();
-      }
-      const zerosTensor = zeros(x.shape);
-      const floatX = cast(x, "float32");
-      const result = backend2.complex(floatX, zerosTensor);
-      zerosTensor.dispose();
-      floatX.dispose();
-      return result;
-    }
-    if (!hasEncodingLoss(x.dtype, dtype)) {
-      return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
-    }
-    if (x.dtype === "complex64") {
-      const real6 = backend2.real(x);
-      const result = cast(real6, dtype);
-      real6.dispose();
-      return result;
-    }
-    if (dtype === "int32") {
-      return backend2.int(x);
-    } else if (dtype === "bool") {
-      const zero = scalar(0, x.dtype);
-      const result = backend2.notEqual(x, zero);
-      zero.dispose();
-      return result;
-    } else {
-      throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-    }
-  }
-  function reshapeTensor(x, shape) {
-    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
-  }
-  function linspaceImpl(start, stop, num) {
-    const step7 = (stop - start) / (num - 1);
-    const values = makeZerosTypedArray(num, "float32");
-    values[0] = start;
-    for (let i = 1; i < values.length; i++) {
-      values[i] = values[i - 1] + step7;
-    }
-    return tensor1d(values, "float32");
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/split_shared.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function split5(x, sizeSplits, axis) {
-    const begin = new Array(x.rank).fill(0);
-    const size = x.shape.slice();
-    return sizeSplits.map((s) => {
-      const sliceSize = [...size];
-      sliceSize[axis] = s;
-      const sliceT = slice(x, begin, sliceSize);
-      begin[axis] += s;
-      return sliceT;
-    });
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/tile_impl.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function tile4(xBuf, reps) {
-    const newShape = new Array(xBuf.rank);
-    for (let i = 0; i < newShape.length; i++) {
-      newShape[i] = xBuf.shape[i] * reps[i];
-    }
-    const result = buffer(newShape, xBuf.dtype);
-    for (let i = 0; i < result.values.length; ++i) {
-      const newLoc = result.indexToLoc(i);
-      const originalLoc = new Array(xBuf.rank);
-      for (let j = 0; j < originalLoc.length; j++) {
-        originalLoc[j] = newLoc[j] % xBuf.shape[j];
-      }
-      const originalIndex = xBuf.locToIndex(originalLoc);
-      result.values[i] = xBuf.values[originalIndex];
-    }
-    return result.toTensor();
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/topk_impl.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function topkImpl(x, xShape, xDtype, k, sorted) {
-    const lastDim = xShape[xShape.length - 1];
-    const [batch, size] = [x.length / lastDim, lastDim];
-    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
-    const allTopKIndices = getTypedArrayFromDType("int32", batch * k);
-    for (let b = 0; b < batch; b++) {
-      const offset = b * size;
-      const vals = x.subarray(offset, offset + size);
-      const valAndInd = [];
-      for (let i = 0; i < vals.length; i++) {
-        valAndInd.push({value: vals[i], index: i});
-      }
-      valAndInd.sort((a, b2) => b2.value - a.value);
-      const outOffset = b * k;
-      const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
-      const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-      for (let i = 0; i < k; i++) {
-        topKVals[i] = valAndInd[i].value;
-        topKIndices[i] = valAndInd[i].index;
-      }
-    }
-    const outputShape = xShape.slice();
-    outputShape[outputShape.length - 1] = k;
-    return [
-      tensor4(allTopKVals, outputShape, xDtype),
-      tensor4(allTopKIndices, outputShape, "int32")
-    ];
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
-  const kernel_impls_exports = {};
-  __export(kernel_impls_exports, {
-    nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
-    nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
-    nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
-    split: () => split5,
-    tile: () => tile4,
-    topkImpl: () => topkImpl,
-    whereImpl: () => whereImpl
-  });
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-
-  // node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
-  /**
-   * @license
-   * Copyright 2018 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const EPSILON_FLOAT32 = 1e-7;
-  const EPSILON_FLOAT16 = 1e-4;
-  class DataStorage {
-    constructor(backend2, dataMover) {
-      this.backend = backend2;
-      this.dataMover = dataMover;
-      this.data = new WeakMap();
-      this.dataIdsCount = 0;
-    }
-    get(dataId) {
-      if (!this.data.has(dataId)) {
-        this.dataMover.moveData(this.backend, dataId);
-      }
-      return this.data.get(dataId);
-    }
-    set(dataId, value) {
-      this.dataIdsCount++;
-      this.data.set(dataId, value);
-    }
-    has(dataId) {
-      return this.data.has(dataId);
-    }
-    delete(dataId) {
-      this.dataIdsCount--;
-      return this.data.delete(dataId);
-    }
-    numDataIds() {
-      return this.dataIdsCount;
-    }
-  }
-  class KernelBackend {
-    time(f) {
-      return notYetImplemented("time");
-    }
-    read(dataId) {
-      return notYetImplemented("read");
-    }
-    readSync(dataId) {
-      return notYetImplemented("readSync");
-    }
-    numDataIds() {
-      return notYetImplemented("numDataIds");
-    }
-    disposeData(dataId) {
-      return notYetImplemented("disposeData");
-    }
-    write(values, shape, dtype) {
-      return notYetImplemented("write");
-    }
-    move(dataId, values, shape, dtype) {
-      return notYetImplemented("move");
-    }
-    memory() {
-      return notYetImplemented("memory");
-    }
-    floatPrecision() {
-      return notYetImplemented("floatPrecision");
-    }
-    epsilon() {
-      return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-    }
-    batchMatMul(a, b, transposeA, transposeB) {
-      return notYetImplemented("batchMatMul");
-    }
-    fusedBatchMatMul({a, b, transposeA, transposeB, bias, activation, preluActivationWeights}) {
-      return notYetImplemented("fusedBatchMatMul");
-    }
-    slice(x, begin, size) {
-      return notYetImplemented("slice");
-    }
-    stridedSlice(x, begin, end, strides) {
-      return notYetImplemented("stridedSlice");
-    }
-    unstack(x, axis) {
-      return notYetImplemented("unstack");
-    }
-    reverse(a, axis) {
-      return notYetImplemented("reverse");
-    }
-    concat(tensors, axis) {
-      return notYetImplemented("concat");
-    }
-    neg(a) {
-      return notYetImplemented("neg");
-    }
-    add(a, b) {
-      return notYetImplemented("add");
-    }
-    addN(tensors) {
-      return notYetImplemented("addN");
-    }
-    subtract(a, b) {
-      return notYetImplemented("subtract");
-    }
-    multiply(a, b) {
-      return notYetImplemented("multiply");
-    }
-    realDivide(a, b) {
-      return notYetImplemented("realDivide");
-    }
-    floorDiv(a, b) {
-      return notYetImplemented("floorDiv");
-    }
-    sum(x, axes) {
-      return notYetImplemented("sum");
-    }
-    prod(x, axes) {
-      return notYetImplemented("prod");
-    }
-    unsortedSegmentSum(x, segmentIds, numSegments) {
-      return notYetImplemented("unsortedSegmentSum");
-    }
-    argMin(x, axis) {
-      return notYetImplemented("argMin");
-    }
-    argMax(x, axis) {
-      return notYetImplemented("argMax");
-    }
-    equal(a, b) {
-      return notYetImplemented("equal");
-    }
-    notEqual(a, b) {
-      return notYetImplemented("notEqual");
-    }
-    less(a, b) {
-      return notYetImplemented("less");
-    }
-    lessEqual(a, b) {
-      return notYetImplemented("lessEqual");
-    }
-    greater(a, b) {
-      return notYetImplemented("greater");
-    }
-    greaterEqual(a, b) {
-      return notYetImplemented("greaterEqual");
-    }
-    logicalNot(a) {
-      return notYetImplemented("logicalNot");
-    }
-    logicalAnd(a, b) {
-      return notYetImplemented("logicalAnd");
-    }
-    logicalOr(a, b) {
-      return notYetImplemented("logicalOr");
-    }
-    where(condition) {
-      return notYetImplemented("where");
-    }
-    select(condition, a, b) {
-      return notYetImplemented("select");
-    }
-    topk(x, k, sorted) {
-      return notYetImplemented("topk");
-    }
-    min(x, axes) {
-      return notYetImplemented("min");
-    }
-    minimum(a, b) {
-      return notYetImplemented("minimum");
-    }
-    mod(a, b) {
-      return notYetImplemented("mod");
-    }
-    max(x, axes) {
-      return notYetImplemented("max");
-    }
-    maximum(a, b) {
-      return notYetImplemented("maximum");
-    }
-    all(x, axes) {
-      return notYetImplemented("all");
-    }
-    any(x, axes) {
-      return notYetImplemented("any");
-    }
-    squaredDifference(a, b) {
-      return notYetImplemented("squaredDifference");
-    }
-    ceil(x) {
-      return notYetImplemented("ceil");
-    }
-    floor(x) {
-      return notYetImplemented("floor");
-    }
-    round(x) {
-      return notYetImplemented("round");
-    }
-    sign(x) {
-      return notYetImplemented("sign");
-    }
-    isNaN(x) {
-      return notYetImplemented("isNaN");
-    }
-    isInf(x) {
-      return notYetImplemented("isInf");
-    }
-    isFinite(x) {
-      return notYetImplemented("isFinite");
-    }
-    pow(a, b) {
-      return notYetImplemented("pow");
-    }
-    exp(x) {
-      return notYetImplemented("exp");
-    }
-    expm1(x) {
-      return notYetImplemented("expm1");
-    }
-    softmax(x, dim) {
-      return notYetImplemented("softmax");
-    }
-    log(x) {
-      return notYetImplemented("log");
-    }
-    log1p(x) {
-      return notYetImplemented("log1p");
-    }
-    sqrt(x) {
-      return notYetImplemented("sqrt");
-    }
-    rsqrt(x) {
-      return notYetImplemented("rsqrt");
-    }
-    square(x) {
-      return notYetImplemented("square");
-    }
-    reciprocal(x) {
-      return notYetImplemented("reciprocal");
-    }
-    relu(x) {
-      return notYetImplemented("relu");
-    }
-    relu6(x) {
-      return notYetImplemented("relu6");
-    }
-    prelu(x, a) {
-      return notYetImplemented("prelu");
-    }
-    elu(x) {
-      return notYetImplemented("elu");
-    }
-    eluDer(dy, y) {
-      return notYetImplemented("eluDer");
-    }
-    selu(x) {
-      return notYetImplemented("selu");
-    }
-    int(x) {
-      return notYetImplemented("int");
-    }
-    clip(x, min4, max6) {
-      return notYetImplemented("clip");
-    }
-    abs(x) {
-      return notYetImplemented("abs");
-    }
-    complexAbs(x) {
-      return notYetImplemented("complexAbs");
-    }
-    sigmoid(x) {
-      return notYetImplemented("sigmoid");
-    }
-    softplus(x) {
-      return notYetImplemented("softplus");
-    }
-    sin(x) {
-      return notYetImplemented("sin");
-    }
-    cos(x) {
-      return notYetImplemented("cos");
-    }
-    tan(x) {
-      return notYetImplemented("tan");
-    }
-    asin(x) {
-      return notYetImplemented("asin");
-    }
-    acos(x) {
-      return notYetImplemented("acos");
-    }
-    atan(x) {
-      return notYetImplemented("atan");
-    }
-    atan2(a, b) {
-      return notYetImplemented("atan2");
-    }
-    sinh(x) {
-      return notYetImplemented("sinh");
-    }
-    cosh(x) {
-      return notYetImplemented("cosh");
-    }
-    tanh(x) {
-      return notYetImplemented("tanh");
-    }
-    asinh(x) {
-      return notYetImplemented("asinh");
-    }
-    acosh(x) {
-      return notYetImplemented("acosh");
-    }
-    atanh(x) {
-      return notYetImplemented("atanh");
-    }
-    erf(x) {
-      return notYetImplemented("erf");
-    }
-    step(x, alpha) {
-      return notYetImplemented("step");
-    }
-    fusedConv2d({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-      return notYetImplemented("fusedConv2d");
-    }
-    conv2d(x, filter, convInfo) {
-      return notYetImplemented("conv2d");
-    }
-    conv2dDerInput(dy, filter, convInfo) {
-      return notYetImplemented("conv2dDerInput");
-    }
-    conv2dDerFilter(x, dY, convInfo) {
-      return notYetImplemented("conv2dDerFilter");
-    }
-    fusedDepthwiseConv2D({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-      return notYetImplemented("fusedDepthwiseConv2D");
-    }
-    depthwiseConv2D(input, filter, convInfo) {
-      return notYetImplemented("depthwiseConv2D");
-    }
-    depthwiseConv2DDerInput(dy, filter, convInfo) {
-      return notYetImplemented("depthwiseConv2DDerInput");
-    }
-    depthwiseConv2DDerFilter(x, dY, convInfo) {
-      return notYetImplemented("depthwiseConv2DDerFilter");
-    }
-    conv3d(x, filter, convInfo) {
-      return notYetImplemented("conv3d");
-    }
-    conv3dDerInput(dy, filter, convInfo) {
-      return notYetImplemented("conv3dDerInput");
-    }
-    conv3dDerFilter(x, dY, convInfo) {
-      return notYetImplemented("conv3dDerFilter");
-    }
-    maxPool(x, convInfo) {
-      return notYetImplemented("maxPool");
-    }
-    maxPoolBackprop(dy, x, y, convInfo) {
-      return notYetImplemented("maxPoolBackprop");
-    }
-    avgPool(x, convInfo) {
-      return notYetImplemented("avgPool");
-    }
-    avgPoolBackprop(dy, x, convInfo) {
-      return notYetImplemented("avgPoolBackprop");
-    }
-    avgPool3d(x, convInfo) {
-      return notYetImplemented("avgPool3d");
-    }
-    avgPool3dBackprop(dy, x, convInfo) {
-      return notYetImplemented("avgPool3dBackprop");
-    }
-    maxPool3d(x, convInfo) {
-      return notYetImplemented("maxPool3d");
-    }
-    maxPool3dBackprop(dy, x, y, convInfo) {
-      return notYetImplemented("maxPool3dBackprop");
-    }
-    reshape(x, shape) {
-      return notYetImplemented("reshape");
-    }
-    cast(x, dtype) {
-      return notYetImplemented("cast");
-    }
-    tile(x, reps) {
-      return notYetImplemented("tile");
-    }
-    pad(x, paddings, constantValue) {
-      return notYetImplemented("pad");
-    }
-    transpose(x, perm) {
-      return notYetImplemented("transpose");
-    }
-    gather(x, indices, axis) {
-      return notYetImplemented("gather");
-    }
-    gatherND(x, indices) {
-      return notYetImplemented("gatherND");
-    }
-    scatterND(indices, updates, shape) {
-      return notYetImplemented("scatterND");
-    }
-    batchToSpaceND(x, blockShape, crops) {
-      return notYetImplemented("batchToSpaceND");
-    }
-    spaceToBatchND(x, blockShape, paddings) {
-      return notYetImplemented("spaceToBatchND");
-    }
-    resizeBilinear(x, newHeight, newWidth, alignCorners) {
-      return notYetImplemented("resizeBilinear");
-    }
-    resizeBilinearBackprop(dy, x, alignCorners) {
-      return notYetImplemented("resizeBilinearBackprop");
-    }
-    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {
-      return notYetImplemented("resizeNearestNeighbor");
-    }
-    resizeNearestNeighborBackprop(dy, x, alignCorners) {
-      return notYetImplemented("resizeNearestNeighborBackprop");
-    }
-    batchNorm(x, mean5, variance, offset, scale2, varianceEpsilon) {
-      return notYetImplemented("batchNorm");
-    }
-    localResponseNormalization4D(x, radius, bias, alpha, beta) {
-      return notYetImplemented("localResponseNormalization4D");
-    }
-    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {
-      return notYetImplemented("LRNGrad");
-    }
-    multinomial(logits, normalized, numSamples, seed) {
-      return notYetImplemented("multinomial");
-    }
-    oneHot(indices, depth, onValue, offValue) {
-      return notYetImplemented("oneHot");
-    }
-    cumsum(x, axis, exclusive, reverse9) {
-      return notYetImplemented("cumsum");
-    }
-    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-      return notYetImplemented("nonMaxSuppression");
-    }
-    fft(x) {
-      return notYetImplemented("fft");
-    }
-    ifft(x) {
-      return notYetImplemented("ifft");
-    }
-    complex(real6, imag6) {
-      return notYetImplemented("complex");
-    }
-    real(input) {
-      return notYetImplemented("real");
-    }
-    imag(input) {
-      return notYetImplemented("imag");
-    }
-    cropAndResize(image2, boxes, boxIndex, cropSize, method, extrapolationValue) {
-      return notYetImplemented("cropAndResize");
-    }
-    depthToSpace(x, blockSize, dataFormat) {
-      return notYetImplemented("depthToSpace");
-    }
-    split(value, sizeSplits, axis) {
-      return notYetImplemented("split");
-    }
-    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {
-      return notYetImplemented("sparseToDense");
-    }
-    diag(x) {
-      return notYetImplemented("diag");
-    }
-    fill(shape, value, dtype) {
-      return notYetImplemented("fill");
-    }
-    onesLike(x) {
-      return notYetImplemented("onesLike");
-    }
-    zerosLike(x) {
-      return notYetImplemented("zerosLike");
-    }
-    linspace(start, stop, num) {
-      return notYetImplemented("linspace");
-    }
-    dispose() {
-      return notYetImplemented("dispose");
-    }
-  }
-  function notYetImplemented(kernelName) {
-    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. Did you forget to import the kernel?`);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const absGradConfig = {
-    kernelName: Abs,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(dy, step(cast(x, "float32"), -1))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const acosGradConfig = {
-    kernelName: Acos,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {
-        x: () => {
-          const a = square(cast(x, "float32"));
-          const b = sqrt(sub(scalar(1), a));
-          return neg(div(dy, b));
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const acoshGradConfig = {
-    kernelName: Acosh,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {
-        x: () => {
-          const a = sqrt(sub(square(cast(x, "float32")), 1));
-          return div(dy, a);
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const addGradConfig = {
-    kernelName: Add,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        let res = dy;
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, a.shape);
-      };
-      const derB = () => {
-        let res = dy;
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, b.shape);
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const addNGradConfig = {
-    kernelName: AddN,
-    saveAllInputs: true,
-    gradFunc: (dy, saved) => {
-      const ders = {};
-      saved.forEach((_, i) => {
-        ders[i] = () => dy.clone();
-      });
-      return ders;
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const argMaxGradConfig = {
-    kernelName: ArgMax,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => zerosLike(x)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const argMinGradConfig = {
-    kernelName: ArgMin,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => zerosLike(x)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const asinGradConfig = {
-    kernelName: Asin,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32")))))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const asinhGradConfig = {
-    kernelName: Asinh,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {
-        x: () => {
-          const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
-          return div(dy, a);
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const atan2GradConfig = {
-    kernelName: Atan2,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        const d = add2(square(a), square(b));
-        let res = mul(dy, div(b, d));
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, a.shape);
-      };
-      const derB = () => {
-        const d = add2(square(a), square(b));
-        let res = neg(mul(dy, div(a, d)));
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, b.shape);
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const atanGradConfig = {
-    kernelName: Atan,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, add2(square(cast(x, "float32")), 1))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const atanhGradConfig = {
-    kernelName: Atanh,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, sub(scalar(1), square(cast(x, "float32"))))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_backprop.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-    const $dy = convertToTensor(dy, "dy", "avgPool3dBackprop");
-    const $input = convertToTensor(input, "input", "avgPool3dBackprop");
-    let dy5D = $dy;
-    let input5D = $input;
-    let reshapedTo5D = false;
-    if ($input.rank === 4) {
-      reshapedTo5D = true;
-      dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-      input5D = reshape($input, [
-        1,
-        $input.shape[0],
-        $input.shape[1],
-        $input.shape[2],
-        $input.shape[3]
-      ]);
-    }
-    assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-    assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2) => {
-      const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-      return backend2.avgPool3dBackprop(dy5D, input5D, convInfo);
-    };
-    const inputs = {dy: dy5D, input: input5D};
-    const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3DBackprop, attrs);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const avgPool3dBackprop = op({avgPool3dBackprop_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const avgPool3DGradConfig = {
-    kernelName: AvgPool3D,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-      const $dilations = dilations == null ? [1, 1, 1] : dilations;
-      return {
-        x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad8, dimRoundingMode)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_backprop.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function avgPoolBackprop_(dy, input, filterSize, strides, pad8) {
-    const $dy = convertToTensor(dy, "dy", "avgPoolBackprop");
-    const $input = convertToTensor(input, "input", "avgPoolBackprop");
-    assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-    let input4D = $input;
-    let dy4D = $dy;
-    let reshapedTo4D = false;
-    if ($input.rank === 3) {
-      reshapedTo4D = true;
-      input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
-      dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
-    }
-    assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`);
-    assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);
-    const forward = (backend2) => {
-      const convInfo = computePool2DInfo(input4D.shape, filterSize, strides, 1, pad8);
-      return backend2.avgPoolBackprop(dy4D, input4D, convInfo);
-    };
-    const inputs = {dy: dy4D, input: input4D};
-    const attrs = {filterSize, strides, pad: pad8};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);
-    if (reshapedTo4D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-    }
-    return res;
-  }
-  const avgPoolBackprop = op({avgPoolBackprop_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const avgPoolGradConfig = {
-    kernelName: AvgPool,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {filterSize, strides, pad: pad8} = attrs;
-      return {
-        x: () => avgPoolBackprop(dy, x, filterSize, strides, pad8)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const batchMatMulGradConfig = {
-    kernelName: BatchMatMul,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved, attrs) => {
-      const [a, b] = saved;
-      const {transposeA, transposeB} = attrs;
-      if (!transposeA && !transposeB) {
-        return {
-          a: () => matMul(dy, b, false, true),
-          b: () => matMul(a, dy, true, false)
-        };
-      } else if (!transposeA && transposeB) {
-        return {
-          a: () => matMul(dy, b, false, false),
-          b: () => matMul(dy, a, true, false)
-        };
-      } else if (transposeA && !transposeB) {
-        return {
-          a: () => matMul(b, dy, false, true),
-          b: () => matMul(a, dy, false, false)
-        };
-      } else {
-        return {
-          a: () => matMul(b, dy, true, true),
-          b: () => matMul(dy, a, true, true)
-        };
-      }
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const batchToSpaceNDGradConfig = {
-    kernelName: BatchToSpaceND,
-    gradFunc: (dy, saved, attrs) => {
-      const {blockShape, crops} = attrs;
-      return {x: () => spaceToBatchND(dy, blockShape, crops)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const broadcastToGradConfig = {
-    kernelName: BroadcastTo,
-    gradFunc: (dy, saved, attrs) => {
-      const broadCastToAttrs = attrs;
-      const inputShape = broadCastToAttrs.inputShape;
-      const outputShape = broadCastToAttrs.shape;
-      const reps = Array.from(outputShape);
-      for (let i = inputShape.length - 1; i >= 0; i--) {
-        if (inputShape[i] === outputShape[i]) {
-          reps[i] = 1;
-        } else if (inputShape[i] !== 1) {
-          throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
-        }
-      }
-      const axes = [];
-      for (let i = 0; i < reps.length; i++) {
-        if (reps[i] > 1) {
-          axes.push(i);
-        }
-      }
-      return {x: () => sum2(dy, axes, true)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const castGradConfig = {
-    kernelName: Cast,
-    gradFunc: (dy) => {
-      return {x: () => dy.clone()};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const ceilGradConfig = {
-    kernelName: Ceil,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const clipByValueGradConfig = {
-    kernelName: ClipByValue,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {clipValueMin, clipValueMax} = attrs;
-      return {
-        x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const concatGradConfig = {
-    kernelName: Concat,
-    saveAllInputs: true,
-    gradFunc: (dy, saved, attrs) => {
-      const shapes = saved.map((t) => t.shape);
-      const {axis} = attrs;
-      const $axis = parseAxisParam(axis, saved[0].shape)[0];
-      const sizeSplits = shapes.map((s) => s[$axis]);
-      const derTensors = split(dy, sizeSplits, $axis);
-      return derTensors.map((t) => () => t);
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const conv2DGradConfig = {
-    kernelName: Conv2D,
-    inputsToSave: ["x", "filter"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x4D, $filter] = saved;
-      const {dilations, strides, pad: pad8, dataFormat} = attrs;
-      assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-      return {
-        x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad8, dataFormat),
-        filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad8, dataFormat)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const conv2DBackpropInputGradConfig = {
-    kernelName: Conv2DBackpropInput,
-    inputsToSave: ["dy", "filter"],
-    gradFunc: (ddx, saved, attrs) => {
-      const [dy, filter] = saved;
-      const {strides, pad: pad8, dataFormat, dimRoundingMode} = attrs;
-      return {
-        dy: () => conv2d(ddx, filter, strides, pad8, dataFormat, 1, dimRoundingMode),
-        filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad8, dataFormat, dimRoundingMode)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function conv3DBackpropFilter_(x, dy, filterShape, strides, pad8) {
-    let x5D = x;
-    if (x.rank === 4) {
-      x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
-    }
-    let dy5D = dy;
-    if (dy5D.rank === 4) {
-      dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-    }
-    assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
-    assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
-    assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
-    assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
-    assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
-    const forward = (backend2) => {
-      const dilations = 1;
-      const convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad8);
-      return backend2.conv3dDerFilter(x5D, dy5D, convInfo);
-    };
-    const inputs = {x: x5D, y: dy5D};
-    const attrs = {strides, pad: pad8};
-    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);
-  }
-  const conv3DBackpropFilter = op({conv3DBackpropFilter_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const conv3DGradConfig = {
-    kernelName: Conv3D,
-    inputsToSave: ["x", "filter"],
-    gradFunc: (dy, saved, attrs) => {
-      const {dilations, strides, pad: pad8} = attrs;
-      assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-      const [x5D, $filter] = saved;
-      return {
-        x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad8),
-        filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad8)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const cosGradConfig = {
-    kernelName: Cos,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(neg(sin(cast(x, "float32"))), dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const coshGradConfig = {
-    kernelName: Cosh,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(sinh(cast(x, "float32")), dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const cumsumGradConfig = {
-    kernelName: Cumsum,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {axis, exclusive, reverse: reverse9} = attrs;
-      return {
-        x: () => {
-          const permutation = getAxesPermutation([axis], x.rank);
-          let out = cumsum(dy, axis, exclusive, !reverse9);
-          if (permutation != null) {
-            out = transpose(out, permutation);
-          }
-          return out;
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const depthwiseConv2dNativeGradConfig = {
-    kernelName: DepthwiseConv2dNative,
-    inputsToSave: ["x", "filter"],
-    gradFunc: (dy, saved, attrs) => {
-      const {dilations, strides, pad: pad8, dimRoundingMode} = attrs;
-      const $dilations = dilations == null ? [1, 1] : dilations;
-      assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
-      const [x, filter] = saved;
-      assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
-      assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
-      assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
-      assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
-      if (dimRoundingMode != null) {
-        assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-      }
-      const convInfo = computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad8, dimRoundingMode, true);
-      return {
-        x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),
-        filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const dilation2dGradConfig = {
-    kernelName: Dilation2D,
-    inputsToSave: ["x", "filter"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x, filter] = saved;
-      const inputInputs = {x, filter, dy};
-      const filterInputs = {x, filter, dy};
-      return {
-        x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
-        filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Div_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const divGradConfig = {
-    kernelName: Div,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        const res = div(dy, cast(b, "float32"));
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(res, reduceAxes), a.shape);
-        }
-        return res;
-      };
-      const derB = () => {
-        let res = mul(dy, cast(a, "float32"));
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = reshape(sum2(res, reduceAxes), b.shape);
-        }
-        const tmp = square(b);
-        return neg(div(res, cast(tmp, "float32")));
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const eluGradConfig = {
-    kernelName: Elu,
-    outputsToSave: [true],
-    gradFunc: (dy, saved) => {
-      const [y] = saved;
-      const backPropKernelFunc = (backend2) => {
-        return backend2.eluDer(dy, y);
-      };
-      const inputs = {dy, y};
-      return {
-        x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, EluGrad)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const erfGradConfig = {
-    kernelName: Erf,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
-      return {x: () => mul(dy, a)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const expGradConfig = {
-    kernelName: Exp,
-    outputsToSave: [true],
-    gradFunc: (dy, saved) => {
-      const [y] = saved;
-      return {x: () => mul(dy, y)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const expm1GradConfig = {
-    kernelName: Expm1,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(dy, exp(x))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const floorGradConfig = {
-    kernelName: Floor,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const floorDivGradConfig = {
-    kernelName: FloorDiv,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        const res = div(dy, cast(b, "float32"));
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(res, reduceAxes), a.shape);
-        }
-        return res;
-      };
-      const derB = () => {
-        let res = mul(dy, cast(a, "float32"));
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = reshape(sum2(res, reduceAxes), b.shape);
-        }
-        const tmp = square(b);
-        return neg(div(res, cast(tmp, "float32")));
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const fusedBatchNormGradConfig = {
-    kernelName: FusedBatchNorm,
-    inputsToSave: ["x", "mean", "variance", "scale"],
-    gradFunc: (dy, saved, attrs) => {
-      const {varianceEpsilon} = attrs;
-      const [x, mean5, variance, scale2] = saved;
-      const scaleValue = scale2 == null ? scalar(1) : scale2;
-      const reductionAxes = getReductionAxes(mean5.shape, x.shape);
-      const tileShape = [];
-      if (mean5.rank === 1) {
-        for (let i = 0; i < x.shape.length - 1; ++i) {
-          tileShape.push(x.shape[i]);
-        }
-        tileShape.push(1);
-      }
-      const xMinusMean = sub(x, mean5);
-      const dyTimesScaleValue = mul(dy, scaleValue);
-      const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
-      const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
-      const derX = () => {
-        if (mean5.rank === 1) {
-          return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
-        } else {
-          return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
-        }
-      };
-      const derMean = () => {
-        let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
-        if (mean5.rank === 1) {
-          meanDer = sum2(meanDer, reductionAxes);
-        }
-        return reshape(meanDer, mean5.shape);
-      };
-      const derVariance = () => {
-        let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
-        if (mean5.rank === 1) {
-          varianceDer = sum2(varianceDer, reductionAxes);
-        }
-        return reshape(varianceDer, mean5.shape);
-      };
-      const derScale = () => {
-        const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
-        let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
-        if (mean5.rank === 1) {
-          scaleDer = sum2(scaleDer, reductionAxes);
-        }
-        return reshape(scaleDer, mean5.shape);
-      };
-      const derOffset = () => {
-        let offsetDer = dy;
-        if (mean5.rank === 1) {
-          offsetDer = sum2(offsetDer, reductionAxes);
-        }
-        return reshape(offsetDer, mean5.shape);
-      };
-      return {
-        x: derX,
-        mean: derMean,
-        variance: derVariance,
-        scale: derScale,
-        offset: derOffset
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const gatherGradConfig = {
-    kernelName: GatherV2,
-    inputsToSave: ["x", "indices"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x, indices] = saved;
-      const {axis} = attrs;
-      const parsedAxis = parseAxisParam(axis, x.shape)[0];
-      const derX = () => {
-        const paramsShape = x.shape;
-        const indicesSize = indices.size;
-        const outerShape = paramsShape.slice(0, parsedAxis);
-        const outerDims = outerShape.length;
-        const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
-        const innerDims = innerShape.length;
-        const outerAxesIndices = arrayRange(0, outerDims);
-        const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
-        const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
-        const values = reshape(dy, valuesShape);
-        const reshapedIndices = reshape(indices, [indicesSize]);
-        const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
-        const valuesTranspose = transpose(values, transposeDims);
-        let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
-        const invertTransposeDims = getUndoAxesPermutation(transposeDims);
-        paramsGrad = transpose(paramsGrad, invertTransposeDims);
-        return paramsGrad;
-      };
-      return {x: derX, indices: () => indices};
-    }
-  };
-  function arrayRange(start, stop) {
-    const result = [];
-    for (let i = start; i < stop; ++i) {
-      result.push(i);
-    }
-    return result;
-  }
-  function arrayConcat(arrays) {
-    const result = [];
-    for (let i = 0; i < arrays.length; ++i) {
-      for (let j = 0; j < arrays[i].length; ++j) {
-        result.push(arrays[i][j]);
-      }
-    }
-    return result;
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const greaterEqualGradConfig = {
-    kernelName: GreaterEqual,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      return {a: () => zerosLike(a), b: () => zerosLike(b)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const identityGradConfig = {
-    kernelName: Identity,
-    gradFunc: (dy) => {
-      return {x: () => cast(dy, "float32")};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const isFiniteGradConfig = {
-    kernelName: IsFinite,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const isInfGradConfig = {
-    kernelName: IsInf,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const isNanGradConfig = {
-    kernelName: IsNan,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const log1pGradConfig = {
-    kernelName: Log1p,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, add2(x, 1))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const logGradConfig = {
-    kernelName: Log,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, cast(x, "float32"))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const logSoftmaxGradConfig = {
-    kernelName: LogSoftmax,
-    inputsToSave: [],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const [value] = saved;
-      const {axis} = attrs;
-      return {
-        logits: () => {
-          const keepDims = true;
-          const softmax3 = exp(value);
-          return sub(dy, mul(sum2(dy, axis, keepDims), softmax3));
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-    const forward = (backend2) => backend2.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);
-    const inputs = {x, y, dy};
-    const attrs = {depthRadius, bias, alpha, beta};
-    return ENGINE.runKernelFunc(forward, inputs, null, LRNBackprop, attrs);
-  }
-  const localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const lrnGradConfig = {
-    kernelName: LRN,
-    inputsToSave: ["x"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const [x, y] = saved;
-      const {depthRadius, bias, alpha, beta} = attrs;
-      return {
-        x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
-    if (y.rank < xOrig.rank) {
-      y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
-    }
-    if (dy.rank < xOrig.rank) {
-      dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
-    }
-    return {
-      x: () => {
-        const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
-        return permutedAxes == null ? dx : transpose(dx, permutedAxes);
-      }
-    };
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const maxGradConfig = {
-    kernelName: Max,
-    inputsToSave: ["x"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const maxAttrs = attrs;
-      const {reductionIndices} = maxAttrs;
-      const [x, y] = saved;
-      const origAxes = parseAxisParam(reductionIndices, x.shape);
-      const permutedAxes = getAxesPermutation(origAxes, x.rank);
-      const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-      return {
-        x: () => {
-          let out = maxGrad["x"]();
-          if (permutedAxes != null) {
-            out = transpose(out);
-          }
-          return out;
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const maximumGradConfig = {
-    kernelName: Maximum,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
-      const derB = () => mul(dy, cast(less(a, b), "float32"));
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_backprop.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-    const $dy = convertToTensor(dy, "dy", "maxPool3dBackprop");
-    const $input = convertToTensor(input, "input", "maxPool3dBackprop");
-    const $output = convertToTensor(output, "output", "maxPool3dBackprop");
-    let dy5D = $dy;
-    let input5D = $input;
-    let output5D = $output;
-    let reshapedTo5D = false;
-    if ($input.rank === 4) {
-      reshapedTo5D = true;
-      dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-      input5D = reshape($input, [
-        1,
-        $input.shape[0],
-        $input.shape[1],
-        $input.shape[2],
-        $input.shape[3]
-      ]);
-      output5D = reshape($output, [
-        1,
-        $output.shape[0],
-        $output.shape[1],
-        $output.shape[2],
-        $output.shape[3]
-      ]);
-    }
-    assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-    assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-    assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2) => {
-      const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-      return backend2.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);
-    };
-    const inputs = {dy: dy5D, input: input5D, output: output5D};
-    const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-    const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3DBackprop, attrs);
-    if (reshapedTo5D) {
-      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-    }
-    return res;
-  }
-  const maxPool3dBackprop = op({maxPool3dBackprop_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const maxPool3DGradConfig = {
-    kernelName: MaxPool3D,
-    inputsToSave: ["x"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const [x, y] = saved;
-      const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-      const $dilations = dilations == null ? [1, 1, 1] : dilations;
-      return {
-        x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad8, dimRoundingMode)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_backprop.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  function maxPoolBackprop_(dy, input, output, filterSize, strides, pad8, dimRoundingMode) {
-    const $dy = convertToTensor(dy, "dy", "maxPoolBackprop");
-    const $input = convertToTensor(input, "input", "maxPoolBackprop");
-    const $output = convertToTensor(output, "output", "maxPoolBackprop");
-    assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-    assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`);
-    assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const forward = (backend2) => {
-      const convInfo = computePool2DInfo($input.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-      return backend2.maxPoolBackprop($dy, $input, $output, convInfo);
-    };
-    const inputs = {dy: $dy, input: $input, output: $output};
-    const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);
-  }
-  const maxPoolBackprop = op({maxPoolBackprop_});
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const maxPoolGradConfig = {
-    kernelName: MaxPool,
-    inputsToSave: ["x"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const [x, y] = saved;
-      const {filterSize, strides, pad: pad8} = attrs;
-      return {
-        x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad8)
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const minGradConfig = {
-    kernelName: Min,
-    inputsToSave: ["x"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const minAttrs = attrs;
-      const {axis} = minAttrs;
-      const [x, y] = saved;
-      const origAxes = parseAxisParam(axis, x.shape);
-      const permutedAxes = getAxesPermutation(origAxes, x.rank);
-      const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-      return {
-        x: () => {
-          let out = minGrad["x"]();
-          if (permutedAxes != null) {
-            out = transpose(out);
-          }
-          return out;
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const minimumGradConfig = {
-    kernelName: Minimum,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
-      const derB = () => mul(dy, cast(greater(a, b), "float32"));
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const modGradConfig = {
-    kernelName: Mod,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(dy, reduceAxes), a.shape);
-        }
-        return dy;
-      };
-      const derB = () => {
-        const res = mul(dy, neg(floor(div(a, b))));
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(res, reduceAxes), b.shape);
-        }
-        return res;
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const multiplyGradConfig = {
-    kernelName: Multiply,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        const res = mul(dy, cast(b, "float32"));
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(res, reduceAxes), a.shape);
-        }
-        return res;
-      };
-      const derB = () => {
-        const res = mul(dy, cast(a, "float32"));
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          return reshape(sum2(res, reduceAxes), b.shape);
-        }
-        return res;
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Negate_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const negateGradConfig = {
-    kernelName: Negate,
-    gradFunc: (dy) => {
-      return {x: () => neg(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const oneHotGradConfig = {
-    kernelName: OneHot,
-    inputsToSave: ["indices"],
-    gradFunc: (dy, saved) => {
-      const indices = saved[0];
-      return {indices: () => zeros(indices.shape, "float32")};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const onesLikeGradConfig = {
-    kernelName: OnesLike,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const padV2GradConfig = {
-    kernelName: PadV2,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const x = saved[0];
-      const {paddings} = attrs;
-      const begin = paddings.map((p) => p[0]);
-      return {x: () => slice(dy, begin, x.shape)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const powGradConfig = {
-    kernelName: Pow,
-    inputsToSave: ["a", "b"],
-    outputsToSave: [true],
-    gradFunc: (dy, saved) => {
-      const [a, b, y] = saved;
-      const base = a;
-      const exp11 = b;
-      const outShape = assertAndGetBroadcastShape(base.shape, exp11.shape);
-      const derBase = () => {
-        const expFloat = cast(exp11, "float32");
-        let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
-        const reduceAxes = getReductionAxes(base.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, base.shape);
-      };
-      const derExp = () => {
-        const condition = greater(base, 0);
-        const logBase = where(condition, log(base), zerosLike(base));
-        let res = mul(dy, mul(y, logBase));
-        const reduceAxes = getReductionAxes(exp11.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, exp11.shape);
-      };
-      return {a: derBase, b: derExp};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const preluGradConfig = {
-    kernelName: Prelu,
-    inputsToSave: ["x", "alpha"],
-    gradFunc: (dy, saved) => {
-      const [x, alpha] = saved;
-      const mask = greater(x, 0);
-      return {
-        x: () => where(mask, dy, mul(dy, alpha)),
-        alpha: () => {
-          let res = where(mask, zerosLike(dy), mul(dy, x));
-          const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
-          if (reduceAxes.length > 0) {
-            res = sum2(res, reduceAxes);
-          }
-          return reshape(res, alpha.shape);
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const reciprocalGradConfig = {
-    kernelName: Reciprocal,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, neg(square(x)))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const relu6GradConfig = {
-    kernelName: Relu6,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      const mask = mul(lessEqual(x, 6), step(x));
-      return {x: () => mul(dy, cast(mask, "float32"))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const reluGradConfig = {
-    kernelName: Relu,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(dy, cast(step(x), "float32"))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const reshapeGradConfig = {
-    kernelName: Reshape,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => reshape(dy, x.shape)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const resizeBilinearGradConfig = {
-    kernelName: ResizeBilinear,
-    inputsToSave: ["images"],
-    gradFunc: (dy, saved, attrs) => {
-      const [images] = saved;
-      const backPropKernelFunc = (backend2) => {
-        const {alignCorners} = attrs;
-        return backend2.resizeBilinearBackprop(dy, images, alignCorners);
-      };
-      const inputs = {images};
-      const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeBilinearGrad, attrs);
-      return {images: imagesDer};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const resizeNearestNeighborGradConfig = {
-    kernelName: ResizeNearestNeighbor,
-    inputsToSave: ["images"],
-    gradFunc: (dy, saved, attrs) => {
-      const [images] = saved;
-      const backPropKernelFunc = (backend2) => {
-        const {alignCorners} = attrs;
-        return backend2.resizeNearestNeighborBackprop(dy, images, alignCorners);
-      };
-      const inputs = {images};
-      const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeNearestNeighborGrad, attrs);
-      return {images: imagesDer};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const reverseGradConfig = {
-    kernelName: Reverse,
-    gradFunc: (dy, saved, attrs) => {
-      const {dims} = attrs;
-      const axes = parseAxisParam(dims, dy.shape);
-      return {x: () => reverse(dy, axes)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const roundGradConfig = {
-    kernelName: Round,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const rsqrtGradConfig = {
-    kernelName: Rsqrt,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/SelectV2_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const selectV2PoolGradConfig = {
-    kernelName: SelectV2,
-    inputsToSave: ["condition"],
-    gradFunc: (dy, saved) => {
-      const [condition] = saved;
-      return {
-        condition: () => cast(zerosLike(condition), "float32"),
-        t: () => mul(dy, cast(condition, dy.dtype)),
-        e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const seluGradConfig = {
-    kernelName: Selu,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {
-        x: () => {
-          const mask = greater(x, scalar(0));
-          const scaleAlpha = scalar(SELU_SCALEALPHA);
-          const scale2 = scalar(SELU_SCALE);
-          const greaterThanZeroDer = mul(dy, scale2);
-          const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, "float32")));
-          return where(mask, greaterThanZeroDer, lessEqualZeroDer);
-        }
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sigmoidGradConfig = {
-    kernelName: Sigmoid,
-    outputsToSave: [true],
-    gradFunc: (dy, saved) => {
-      const [y] = saved;
-      return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const signGradConfig = {
-    kernelName: Sign,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sinGradConfig = {
-    kernelName: Sin,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(cos(cast(x, "float32")), dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sinhGradConfig = {
-    kernelName: Sinh,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(cosh(cast(x, "float32")), dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sliceGradConfig = {
-    kernelName: Slice,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {begin, size} = attrs;
-      const inputShape = x.shape;
-      const [begin_, size_] = parseSliceParams(x, begin, size);
-      const paddings = [];
-      for (let i = 0; i < dy.rank; i++) {
-        paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
-      }
-      return {x: () => pad(dy, paddings)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const softmaxGradConfig = {
-    kernelName: Softmax,
-    outputsToSave: [true],
-    gradFunc: (dy, saved, attrs) => {
-      const [y] = saved;
-      const {dim} = attrs;
-      const keepDims = true;
-      const dyTimesY = mul(dy, y);
-      return {
-        logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
-      };
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const softplusGradConfig = {
-    kernelName: Softplus,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(dy, sigmoid(x))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const spaceToBatchNDGradConfig = {
-    kernelName: SpaceToBatchND,
-    gradFunc: (dy, saved, attrs) => {
-      const {blockShape, paddings} = attrs;
-      return {x: () => batchToSpaceND(dy, blockShape, paddings)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const splitVGradConfig = {
-    kernelName: SplitV,
-    gradFunc: (dy, saved, attrs) => {
-      const {axis} = attrs;
-      return {x: () => concat(dy, axis)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sqrtGradConfig = {
-    kernelName: Sqrt,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, mul(sqrt(cast(x, "float32")), 2))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
-  /**
-   * @license
-   * Copyright 2019 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const squareGradConfig = {
-    kernelName: Square,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => mul(dy, mul(cast(x, "float32"), 2))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const squaredDifferenceGradConfig = {
-    kernelName: SquaredDifference,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const two = scalar(2);
-      const derA = () => mul(dy, mul(two, sub(a, b)));
-      const derB = () => mul(dy, mul(two, sub(b, a)));
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const stepGradConfig = {
-    kernelName: Step,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const subGradConfig = {
-    kernelName: Sub,
-    inputsToSave: ["a", "b"],
-    gradFunc: (dy, saved) => {
-      const [a, b] = saved;
-      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-      const derA = () => {
-        let res = dy;
-        const reduceAxes = getReductionAxes(a.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, a.shape);
-      };
-      const derB = () => {
-        let res = dy;
-        const reduceAxes = getReductionAxes(b.shape, outShape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(neg(res), b.shape);
-      };
-      return {a: derA, b: derB};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const sumGradConfig = {
-    kernelName: Sum,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const expandedDyShape = x.shape.slice();
-      const {axis} = attrs;
-      const axes = parseAxisParam(axis, x.shape);
-      axes.forEach((axis2) => {
-        expandedDyShape[axis2] = 1;
-      });
-      const expandedDy = reshape(dy, expandedDyShape);
-      const derX = mul(expandedDy, ones2(x.shape, "float32"));
-      return {x: () => derX};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const tanGradConfig = {
-    kernelName: Tan,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved) => {
-      const [x] = saved;
-      return {x: () => div(dy, square(cos(x)))};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const tanhGradConfig = {
-    kernelName: Tanh,
-    outputsToSave: [true],
-    gradFunc: (dy, saved) => {
-      const [y] = saved;
-      return {x: () => mul(sub(scalar(1), square(y)), dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const tileGradConfig = {
-    kernelName: Tile,
-    inputsToSave: ["x"],
-    gradFunc: (dy, saved, attrs) => {
-      const [x] = saved;
-      const {reps} = attrs;
-      const derX = () => {
-        let xGrad = zerosLike(x);
-        if (x.rank === 1) {
-          for (let i = 0; i < reps[0]; ++i) {
-            xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
-          }
-        } else if (x.rank === 2) {
-          for (let i = 0; i < reps[0]; ++i) {
-            for (let j = 0; j < reps[1]; ++j) {
-              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
-                x.shape[0],
-                x.shape[1]
-              ]));
-            }
-          }
-        } else if (x.rank === 3) {
-          for (let i = 0; i < reps[0]; ++i) {
-            for (let j = 0; j < reps[1]; ++j) {
-              for (let k = 0; k < reps[2]; ++k) {
-                xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
-              }
-            }
-          }
-        } else if (x.rank === 4) {
-          for (let i = 0; i < reps[0]; ++i) {
-            for (let j = 0; j < reps[1]; ++j) {
-              for (let k = 0; k < reps[2]; ++k) {
-                for (let l = 0; l < reps[3]; ++l) {
-                  xGrad = add2(xGrad, slice(dy, [
-                    i * x.shape[0],
-                    j * x.shape[1],
-                    k * x.shape[2],
-                    l * x.shape[3]
-                  ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
-                }
-              }
-            }
-          }
-        } else {
-          throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
-        }
-        return xGrad;
-      };
-      return {x: derX};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const transposeGradConfig = {
-    kernelName: Transpose,
-    gradFunc: (dy, saved, attrs) => {
-      const transposeAttrs = attrs;
-      const {perm} = transposeAttrs;
-      const undoPerm = getUndoAxesPermutation(perm);
-      return {x: () => transpose(dy, undoPerm)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google Inc. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const unpackGradConfig = {
-    kernelName: Unpack,
-    gradFunc: (dy, saved, attrs) => {
-      const unpackAttrs = attrs;
-      const {axis} = unpackAttrs;
-      return {value: () => stack(dy, axis)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const unsortedSegmentSumGradConfig = {
-    kernelName: UnsortedSegmentSum,
-    inputsToSave: ["segmentIds"],
-    gradFunc: (dy, saved) => {
-      const [segmentIds] = saved;
-      const derX = () => {
-        return gatherDropNegatives(dy, segmentIds);
-      };
-      return {x: derX};
-    }
-  };
-  function gatherDropNegatives(x, indices) {
-    const zeroClippedIndices = maximum(indices, zerosLike(indices));
-    const gathered = gather(x, zeroClippedIndices);
-    let isPositive = greaterEqual(indices, scalar(0, "int32"));
-    const numIters = gathered.rank - isPositive.rank;
-    for (let i = 0; i < numIters; ++i) {
-      isPositive = expandDims(isPositive, i + 1);
-    }
-    isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
-    const zeroSlice = zerosLike(gathered);
-    return where(isPositive, gathered, zeroSlice);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const zerosLikeGradConfig = {
-    kernelName: ZerosLike,
-    gradFunc: (dy) => {
-      return {x: () => zerosLike(dy)};
-    }
-  };
-
-  // node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
-  /**
-   * @license
-   * Copyright 2020 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-  const gradConfigs = [
-    absGradConfig,
-    acosGradConfig,
-    acoshGradConfig,
-    addGradConfig,
-    addNGradConfig,
-    argMaxGradConfig,
-    argMinGradConfig,
-    asinGradConfig,
-    asinhGradConfig,
-    atan2GradConfig,
-    atanGradConfig,
-    atanhGradConfig,
-    avgPool3DGradConfig,
-    avgPoolGradConfig,
-    batchMatMulGradConfig,
-    batchToSpaceNDGradConfig,
-    broadcastToGradConfig,
-    castGradConfig,
-    ceilGradConfig,
-    clipByValueGradConfig,
-    concatGradConfig,
-    conv2DBackpropInputGradConfig,
-    conv2DGradConfig,
-    conv3DGradConfig,
-    cosGradConfig,
-    coshGradConfig,
-    cumsumGradConfig,
-    depthwiseConv2dNativeGradConfig,
-    dilation2dGradConfig,
-    divGradConfig,
-    eluGradConfig,
-    erfGradConfig,
-    expGradConfig,
-    expm1GradConfig,
-    floorDivGradConfig,
-    floorGradConfig,
-    fusedBatchNormGradConfig,
-    gatherGradConfig,
-    greaterEqualGradConfig,
-    identityGradConfig,
-    isFiniteGradConfig,
-    isInfGradConfig,
-    isNanGradConfig,
-    log1pGradConfig,
-    logGradConfig,
-    logSoftmaxGradConfig,
-    lrnGradConfig,
-    maxGradConfig,
-    maxGradConfig,
-    maximumGradConfig,
-    maxPool3DGradConfig,
-    maxPoolGradConfig,
-    minGradConfig,
-    minimumGradConfig,
-    modGradConfig,
-    multiplyGradConfig,
-    negateGradConfig,
-    oneHotGradConfig,
-    onesLikeGradConfig,
-    padV2GradConfig,
-    padV2GradConfig,
-    powGradConfig,
-    preluGradConfig,
-    reciprocalGradConfig,
-    relu6GradConfig,
-    reluGradConfig,
-    reshapeGradConfig,
-    resizeBilinearGradConfig,
-    resizeNearestNeighborGradConfig,
-    reverseGradConfig,
-    roundGradConfig,
-    rsqrtGradConfig,
-    selectV2PoolGradConfig,
-    seluGradConfig,
-    sigmoidGradConfig,
-    signGradConfig,
-    sinGradConfig,
-    sinhGradConfig,
-    sliceGradConfig,
-    softmaxGradConfig,
-    softplusGradConfig,
-    spaceToBatchNDGradConfig,
-    spaceToBatchNDGradConfig,
-    splitVGradConfig,
-    splitVGradConfig,
-    sqrtGradConfig,
-    squaredDifferenceGradConfig,
-    squareGradConfig,
-    stepGradConfig,
-    subGradConfig,
-    sumGradConfig,
-    tanGradConfig,
-    tanhGradConfig,
-    tileGradConfig,
-    transposeGradConfig,
-    unpackGradConfig,
-    unsortedSegmentSumGradConfig,
-    zerosLikeGradConfig
-  ];
-  for (const gradientConfig of gradConfigs) {
-    registerGradient(gradientConfig);
-  }
-
-  // node_modules/@tensorflow/tfjs-core/dist/index.js
-  const dist_exports = {};
-  __export(dist_exports, {
-    Abs: () => Abs,
-    Acos: () => Acos,
-    Acosh: () => Acosh,
-    AdadeltaOptimizer: () => AdadeltaOptimizer,
-    AdagradOptimizer: () => AdagradOptimizer,
-    AdamOptimizer: () => AdamOptimizer,
-    AdamaxOptimizer: () => AdamaxOptimizer,
-    Add: () => Add,
-    AddN: () => AddN,
-    All: () => All,
-    Any: () => Any,
-    ArgMax: () => ArgMax,
-    ArgMin: () => ArgMin,
-    Asin: () => Asin,
-    Asinh: () => Asinh,
-    Atan: () => Atan,
-    Atan2: () => Atan2,
-    Atanh: () => Atanh,
-    AvgPool: () => AvgPool,
-    AvgPool3D: () => AvgPool3D,
-    AvgPool3DBackprop: () => AvgPool3DBackprop,
-    AvgPoolBackprop: () => AvgPoolBackprop,
-    BatchMatMul: () => BatchMatMul,
-    BatchToSpaceND: () => BatchToSpaceND,
-    BroadcastTo: () => BroadcastTo,
-    Cast: () => Cast,
-    Ceil: () => Ceil,
-    ClipByValue: () => ClipByValue,
-    Complex: () => Complex,
-    Concat: () => Concat,
-    Conv2D: () => Conv2D,
-    Conv2DBackpropFilter: () => Conv2DBackpropFilter,
-    Conv2DBackpropInput: () => Conv2DBackpropInput,
-    Conv3D: () => Conv3D,
-    Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
-    Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
-    Cos: () => Cos,
-    Cosh: () => Cosh,
-    CropAndResize: () => CropAndResize,
-    Cumsum: () => Cumsum,
-    DataStorage: () => DataStorage,
-    DepthToSpace: () => DepthToSpace,
-    DepthwiseConv2dNative: () => DepthwiseConv2dNative,
-    DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
-    DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
-    Diag: () => Diag,
-    Dilation2D: () => Dilation2D,
-    Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
-    Dilation2DBackpropInput: () => Dilation2DBackpropInput,
-    Div: () => Div,
-    ENV: () => ENV,
-    Elu: () => Elu,
-    EluGrad: () => EluGrad,
-    Environment: () => Environment,
-    Equal: () => Equal,
-    Erf: () => Erf,
-    Exp: () => Exp,
-    Expm1: () => Expm1,
-    FFT: () => FFT,
-    Fill: () => Fill,
-    FlipLeftRight: () => FlipLeftRight,
-    Floor: () => Floor,
-    FloorDiv: () => FloorDiv,
-    FromPixels: () => FromPixels,
-    FusedBatchNorm: () => FusedBatchNorm,
-    FusedConv2D: () => FusedConv2D,
-    FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
-    GatherNd: () => GatherNd,
-    GatherV2: () => GatherV2,
-    Greater: () => Greater,
-    GreaterEqual: () => GreaterEqual,
-    IFFT: () => IFFT,
-    Identity: () => Identity,
-    Imag: () => Imag,
-    IsFinite: () => IsFinite,
-    IsInf: () => IsInf,
-    IsNan: () => IsNan,
-    KernelBackend: () => KernelBackend,
-    LRN: () => LRN,
-    LRNBackprop: () => LRNBackprop,
-    Less: () => Less,
-    LessEqual: () => LessEqual,
-    LinSpace: () => LinSpace,
-    Log: () => Log,
-    Log1p: () => Log1p,
-    LogSoftmax: () => LogSoftmax,
-    LogicalAnd: () => LogicalAnd,
-    LogicalNot: () => LogicalNot,
-    LogicalOr: () => LogicalOr,
-    Max: () => Max,
-    MaxPool: () => MaxPool,
-    MaxPool3D: () => MaxPool3D,
-    MaxPool3DBackprop: () => MaxPool3DBackprop,
-    MaxPoolBackprop: () => MaxPoolBackprop,
-    MaxPoolWithArgmax: () => MaxPoolWithArgmax,
-    Maximum: () => Maximum,
-    Mean: () => Mean,
-    Min: () => Min,
-    Minimum: () => Minimum,
-    Mod: () => Mod,
-    MomentumOptimizer: () => MomentumOptimizer,
-    Multiply: () => Multiply,
-    Negate: () => Negate,
-    NonMaxSuppressionV3: () => NonMaxSuppressionV3,
-    NonMaxSuppressionV4: () => NonMaxSuppressionV4,
-    NonMaxSuppressionV5: () => NonMaxSuppressionV5,
-    NotEqual: () => NotEqual,
-    OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
-    OneHot: () => OneHot,
-    OnesLike: () => OnesLike,
-    Optimizer: () => Optimizer,
-    PadV2: () => PadV2,
-    Pool: () => Pool,
-    Pow: () => Pow,
-    Prelu: () => Prelu,
-    Prod: () => Prod,
-    RMSPropOptimizer: () => RMSPropOptimizer,
-    Range: () => Range,
-    Rank: () => Rank,
-    Real: () => Real,
-    Reciprocal: () => Reciprocal,
-    Reduction: () => Reduction,
-    Relu: () => Relu,
-    Relu6: () => Relu6,
-    Reshape: () => Reshape,
-    ResizeBilinear: () => ResizeBilinear,
-    ResizeBilinearGrad: () => ResizeBilinearGrad,
-    ResizeNearestNeighbor: () => ResizeNearestNeighbor,
-    ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
-    Reverse: () => Reverse,
-    RotateWithOffset: () => RotateWithOffset,
-    Round: () => Round,
-    Rsqrt: () => Rsqrt,
-    SGDOptimizer: () => SGDOptimizer,
-    ScatterNd: () => ScatterNd,
-    SelectV2: () => SelectV2,
-    Selu: () => Selu,
-    Sigmoid: () => Sigmoid,
-    Sign: () => Sign,
-    Sin: () => Sin,
-    Sinh: () => Sinh,
-    Slice: () => Slice,
-    Softmax: () => Softmax,
-    Softplus: () => Softplus,
-    SpaceToBatchND: () => SpaceToBatchND,
-    SparseToDense: () => SparseToDense,
-    SplitV: () => SplitV,
-    Sqrt: () => Sqrt,
-    Square: () => Square,
-    SquaredDifference: () => SquaredDifference,
-    Step: () => Step,
-    StridedSlice: () => StridedSlice,
-    Sub: () => Sub,
-    Sum: () => Sum,
-    Tan: () => Tan,
-    Tanh: () => Tanh,
-    Tensor: () => Tensor,
-    TensorBuffer: () => TensorBuffer,
-    Tile: () => Tile,
-    TopK: () => TopK,
-    Transpose: () => Transpose,
-    Unpack: () => Unpack,
-    UnsortedSegmentSum: () => UnsortedSegmentSum,
-    Variable: () => Variable,
-    ZerosLike: () => ZerosLike,
-    _FusedMatMul: () => _FusedMatMul,
-    abs: () => abs,
-    acos: () => acos,
-    acosh: () => acosh,
-    add: () => add2,
-    addN: () => addN,
-    addStrict: () => addStrict,
-    all: () => all,
-    any: () => any,
-    argMax: () => argMax,
-    argMin: () => argMin,
-    asin: () => asin,
-    asinh: () => asinh,
-    atan: () => atan,
-    atan2: () => atan2,
-    atanh: () => atanh,
-    avgPool: () => avgPool,
-    avgPool3d: () => avgPool3d,
-    backend: () => backend,
-    backend_util: () => backend_util_exports,
-    basicLSTMCell: () => basicLSTMCell,
-    batchNorm: () => batchNorm,
-    batchNorm2d: () => batchNorm2d,
-    batchNorm3d: () => batchNorm3d,
-    batchNorm4d: () => batchNorm4d,
-    batchToSpaceND: () => batchToSpaceND,
-    booleanMaskAsync: () => booleanMaskAsync,
-    broadcastTo: () => broadcastTo,
-    browser: () => browser_exports,
-    buffer: () => buffer,
-    cast: () => cast,
-    ceil: () => ceil,
-    clipByValue: () => clipByValue,
-    clone: () => clone,
-    complex: () => complex,
-    concat: () => concat,
-    concat1d: () => concat1d,
-    concat2d: () => concat2d,
-    concat3d: () => concat3d,
-    concat4d: () => concat4d,
-    conv1d: () => conv1d,
-    conv2d: () => conv2d,
-    conv2dTranspose: () => conv2dTranspose,
-    conv3d: () => conv3d,
-    conv3dTranspose: () => conv3dTranspose,
-    cos: () => cos,
-    cosh: () => cosh,
-    cosineWindow: () => cosineWindow,
-    cumsum: () => cumsum,
-    customGrad: () => customGrad,
-    deprecationWarn: () => deprecationWarn,
-    depthToSpace: () => depthToSpace,
-    depthwiseConv2d: () => depthwiseConv2d,
-    device_util: () => device_util_exports,
-    diag: () => diag,
-    dilation2d: () => dilation2d,
-    disableDeprecationWarnings: () => disableDeprecationWarnings,
-    dispose: () => dispose,
-    disposeVariables: () => disposeVariables,
-    div: () => div,
-    divNoNan: () => divNoNan,
-    divStrict: () => divStrict,
-    dot: () => dot,
-    dropout: () => dropout,
-    elu: () => elu,
-    enableDebugMode: () => enableDebugMode,
-    enableProdMode: () => enableProdMode,
-    enclosingPowerOfTwo: () => enclosingPowerOfTwo,
-    engine: () => engine15,
-    env: () => env,
-    equal: () => equal,
-    equalStrict: () => equalStrict,
-    erf: () => erf,
-    exp: () => exp,
-    expandDims: () => expandDims,
-    expm1: () => expm1,
-    eye: () => eye,
-    fft: () => fft,
-    fill: () => fill,
-    findBackend: () => findBackend,
-    findBackendFactory: () => findBackendFactory,
-    floor: () => floor,
-    floorDiv: () => floorDiv,
-    fused: () => fused_ops_exports,
-    gather: () => gather,
-    gatherND: () => gatherND,
-    gather_util: () => gather_nd_util_exports,
-    getBackend: () => getBackend,
-    getGradient: () => getGradient,
-    getKernel: () => getKernel,
-    getKernelsForBackend: () => getKernelsForBackend,
-    grad: () => grad,
-    grads: () => grads,
-    greater: () => greater,
-    greaterEqual: () => greaterEqual,
-    greaterEqualStrict: () => greaterEqualStrict,
-    greaterStrict: () => greaterStrict,
-    ifft: () => ifft,
-    imag: () => imag,
-    image: () => image,
-    inTopKAsync: () => inTopKAsync,
-    io: () => io_exports,
-    irfft: () => irfft,
-    isFinite: () => isFinite2,
-    isInf: () => isInf,
-    isNaN: () => isNaN2,
-    keep: () => keep,
-    kernel_impls: () => kernel_impls_exports,
-    leakyRelu: () => leakyRelu,
-    less: () => less,
-    lessEqual: () => lessEqual,
-    lessEqualStrict: () => lessEqualStrict,
-    lessStrict: () => lessStrict,
-    linalg: () => linalg,
-    linspace: () => linspace,
-    localResponseNormalization: () => localResponseNormalization,
-    log: () => log,
-    log1p: () => log1p,
-    logSigmoid: () => logSigmoid,
-    logSoftmax: () => logSoftmax,
-    logSumExp: () => logSumExp,
-    logicalAnd: () => logicalAnd,
-    logicalNot: () => logicalNot,
-    logicalOr: () => logicalOr,
-    logicalXor: () => logicalXor,
-    losses: () => losses,
-    matMul: () => matMul,
-    math: () => math_exports,
-    max: () => max,
-    maxPool: () => maxPool,
-    maxPool3d: () => maxPool3d,
-    maxPoolWithArgmax: () => maxPoolWithArgmax,
-    maximum: () => maximum,
-    maximumStrict: () => maximumStrict,
-    mean: () => mean,
-    memory: () => memory,
-    min: () => min,
-    minimum: () => minimum,
-    minimumStrict: () => minimumStrict,
-    mod: () => mod,
-    modStrict: () => modStrict,
-    moments: () => moments,
-    movingAverage: () => movingAverage,
-    mul: () => mul,
-    mulStrict: () => mulStrict,
-    multiRNNCell: () => multiRNNCell,
-    multinomial: () => multinomial,
-    neg: () => neg,
-    nextFrame: () => nextFrame,
-    norm: () => norm,
-    notEqual: () => notEqual,
-    notEqualStrict: () => notEqualStrict,
-    oneHot: () => oneHot,
-    ones: () => ones2,
-    onesLike: () => onesLike,
-    op: () => op,
-    outerProduct: () => outerProduct,
-    pad: () => pad,
-    pad1d: () => pad1d,
-    pad2d: () => pad2d,
-    pad3d: () => pad3d,
-    pad4d: () => pad4d,
-    pool: () => pool,
-    pow: () => pow,
-    powStrict: () => powStrict,
-    prelu: () => prelu,
-    print: () => print,
-    prod: () => prod,
-    profile: () => profile,
-    rand: () => rand,
-    randomGamma: () => randomGamma,
-    randomNormal: () => randomNormal,
-    randomUniform: () => randomUniform,
-    range: () => range,
-    ready: () => ready,
-    real: () => real,
-    reciprocal: () => reciprocal,
-    registerBackend: () => registerBackend,
-    registerGradient: () => registerGradient,
-    registerKernel: () => registerKernel,
-    relu: () => relu,
-    relu6: () => relu6,
-    removeBackend: () => removeBackend,
-    reshape: () => reshape,
-    reverse: () => reverse,
-    reverse1d: () => reverse1d,
-    reverse2d: () => reverse2d,
-    reverse3d: () => reverse3d,
-    reverse4d: () => reverse4d,
-    rfft: () => rfft,
-    round: () => round,
-    rsqrt: () => rsqrt,
-    scalar: () => scalar,
-    scatterND: () => scatterND,
-    scatter_util: () => scatter_nd_util_exports,
-    selu: () => selu,
-    separableConv2d: () => separableConv2d,
-    serialization: () => serialization_exports,
-    setBackend: () => setBackend,
-    setPlatform: () => setPlatform,
-    setdiff1dAsync: () => setdiff1dAsync,
-    sigmoid: () => sigmoid,
-    sign: () => sign,
-    signal: () => signal,
-    sin: () => sin,
-    sinh: () => sinh,
-    slice: () => slice,
-    slice1d: () => slice1d,
-    slice2d: () => slice2d,
-    slice3d: () => slice3d,
-    slice4d: () => slice4d,
-    slice_util: () => slice_util_exports,
-    softmax: () => softmax,
-    softplus: () => softplus,
-    spaceToBatchND: () => spaceToBatchND,
-    sparseToDense: () => sparseToDense,
-    spectral: () => spectral,
-    split: () => split,
-    sqrt: () => sqrt,
-    square: () => square,
-    squaredDifference: () => squaredDifference,
-    squaredDifferenceStrict: () => squaredDifferenceStrict,
-    squeeze: () => squeeze,
-    stack: () => stack,
-    step: () => step,
-    stridedSlice: () => stridedSlice,
-    sub: () => sub,
-    subStrict: () => subStrict,
-    sum: () => sum2,
-    sumOutType: () => sumOutType,
-    tan: () => tan,
-    tanh: () => tanh2,
-    tensor: () => tensor4,
-    tensor1d: () => tensor1d,
-    tensor2d: () => tensor2d,
-    tensor3d: () => tensor3d,
-    tensor4d: () => tensor4d,
-    tensor5d: () => tensor5d,
-    tensor6d: () => tensor6d,
-    tensor_util: () => tensor_util_exports,
-    test_util: () => test_util_exports,
-    tidy: () => tidy,
-    tile: () => tile,
-    time: () => time,
-    topk: () => topk,
-    train: () => train,
-    transpose: () => transpose,
-    truncatedNormal: () => truncatedNormal,
-    unregisterGradient: () => unregisterGradient,
-    unregisterKernel: () => unregisterKernel,
-    unsortedSegmentSum: () => unsortedSegmentSum,
-    unstack: () => unstack,
-    upcastType: () => upcastType,
-    util: () => util_exports,
-    valueAndGrad: () => valueAndGrad,
-    valueAndGrads: () => valueAndGrads,
-    variable: () => variable,
-    variableGrads: () => variableGrads,
-    version_core: () => version,
-    where: () => where,
-    whereAsync: () => whereAsync,
-    zeros: () => zeros,
-    zerosLike: () => zerosLike
-  });
-  /**
-   * @license
-   * Copyright 2017 Google LLC. All Rights Reserved.
-   * Licensed under the Apache License, Version 2.0 (the "License");
-   * you may not use this file except in compliance with the License.
-   * You may obtain a copy of the License at
-   *
-   * http://www.apache.org/licenses/LICENSE-2.0
-   *
-   * Unless required by applicable law or agreed to in writing, software
-   * distributed under the License is distributed on an "AS IS" BASIS,
-   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   * See the License for the specific language governing permissions and
-   * limitations under the License.
-   * =============================================================================
-   */
-
   // build/src/draw/drawContour.js
   function drawContour(ctx, points, isClosed = false) {
     ctx.beginPath();
@@ -20118,23 +227,24 @@ Expected: ${expectedFlat}.`);
     isTensor4D: () => isTensor4D,
     isValidNumber: () => isValidNumber,
     isValidProbablitiy: () => isValidProbablitiy,
-    range: () => range4,
-    round: () => round3
+    range: () => range,
+    round: () => round
   });
-  function isTensor(tensor17, dim) {
-    return tensor17 instanceof Tensor && tensor17.shape.length === dim;
+  const tf = __toModule(require("@tensorflow/tfjs"));
+  function isTensor(tensor2, dim) {
+    return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim;
   }
-  function isTensor1D(tensor17) {
-    return isTensor(tensor17, 1);
+  function isTensor1D(tensor2) {
+    return isTensor(tensor2, 1);
   }
-  function isTensor2D(tensor17) {
-    return isTensor(tensor17, 2);
+  function isTensor2D(tensor2) {
+    return isTensor(tensor2, 2);
   }
-  function isTensor3D(tensor17) {
-    return isTensor(tensor17, 3);
+  function isTensor3D(tensor2) {
+    return isTensor(tensor2, 3);
   }
-  function isTensor4D(tensor17) {
-    return isTensor(tensor17, 4);
+  function isTensor4D(tensor2) {
+    return isTensor(tensor2, 4);
   }
   function isFloat(num) {
     return num % 1 !== 0;
@@ -20142,7 +252,7 @@ Expected: ${expectedFlat}.`);
   function isEven(num) {
     return num % 2 === 0;
   }
-  function round3(num, prec = 2) {
+  function round(num, prec = 2) {
     const f = Math.pow(10, prec);
     return Math.floor(num * f) / f;
   }
@@ -20154,10 +264,10 @@ Expected: ${expectedFlat}.`);
     return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
   }
   function getCenterPoint(pts) {
-    return pts.reduce((sum26, pt) => sum26.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
+    return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
   }
-  function range4(num, start, step7) {
-    return Array(num).fill(0).map((_, i) => start + i * step7);
+  function range(num, start, step) {
+    return Array(num).fill(0).map((_, i) => start + i * step);
   }
   function isValidNumber(num) {
     return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
@@ -20430,15 +540,15 @@ Expected: ${expectedFlat}.`);
   function minBbox(pts) {
     const xs = pts.map((pt) => pt.x);
     const ys = pts.map((pt) => pt.y);
-    const minX = xs.reduce((min4, x) => x < min4 ? x : min4, Infinity);
-    const minY = ys.reduce((min4, y) => y < min4 ? y : min4, Infinity);
-    const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0);
-    const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0);
+    const minX = xs.reduce((min, x) => x < min ? x : min, Infinity);
+    const minY = ys.reduce((min, y) => y < min ? y : min, Infinity);
+    const maxX = xs.reduce((max, x) => max < x ? x : max, 0);
+    const maxY = ys.reduce((max, y) => max < y ? y : max, 0);
     return new BoundingBox(minX, minY, maxX, maxY);
   }
 
   // build/src/ops/nonMaxSuppression.js
-  function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
+  function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) {
     let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex);
     const pick = [];
     while (indicesSortedByScore.length > 0) {
@@ -20458,20 +568,22 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ops/normalize.js
+  const tf2 = __toModule(require("@tensorflow/tfjs"));
   function normalize(x, meanRgb) {
-    return tidy(() => {
+    return tf2.tidy(() => {
       const [r, g, b] = meanRgb;
-      const avg_r = fill([...x.shape.slice(0, 3), 1], r);
-      const avg_g = fill([...x.shape.slice(0, 3), 1], g);
-      const avg_b = fill([...x.shape.slice(0, 3), 1], b);
-      const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
-      return sub(x, avg_rgb);
+      const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r);
+      const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g);
+      const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b);
+      const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
+      return tf2.sub(x, avg_rgb);
     });
   }
 
   // build/src/ops/padToSquare.js
+  const tf3 = __toModule(require("@tensorflow/tfjs"));
   function padToSquare(imgTensor, isCenterImage = false) {
-    return tidy(() => {
+    return tf3.tidy(() => {
       const [height, width] = imgTensor.shape.slice(1);
       if (height === width) {
         return imgTensor;
@@ -20482,7 +594,7 @@ Expected: ${expectedFlat}.`);
       const createPaddingTensor = (paddingAmount2) => {
         const paddingTensorShape = imgTensor.shape.slice();
         paddingTensorShape[paddingAxis] = paddingAmount2;
-        return fill(paddingTensorShape, 0);
+        return tf3.fill(paddingTensorShape, 0);
       };
       const paddingTensorAppend = createPaddingTensor(paddingAmount);
       const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -20491,8 +603,8 @@ Expected: ${expectedFlat}.`);
         paddingTensorPrepend,
         imgTensor,
         paddingTensorAppend
-      ].filter((t) => !!t).map((t) => cast(t, "float32"));
-      return concat(tensorsToStack, paddingAxis);
+      ].filter((t) => !!t).map((t) => tf3.cast(t, "float32"));
+      return tf3.concat(tensorsToStack, paddingAxis);
     });
   }
 
@@ -20509,7 +621,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ops/index.js
-  function sigmoid6(x) {
+  function sigmoid(x) {
     return 1 / (1 + Math.exp(-x));
   }
   function inverseSigmoid(x) {
@@ -20646,7 +758,7 @@ Expected: ${expectedFlat}.`);
       return this._distance;
     }
     toString(withDistance = true) {
-      return `${this.label}${withDistance ? ` (${round3(this.distance)})` : ""}`;
+      return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
     }
   }
 
@@ -20733,7 +845,7 @@ Expected: ${expectedFlat}.`);
 
   // build/src/env/createBrowserEnv.js
   function createBrowserEnv() {
-    const fetch3 = window["fetch"] || function() {
+    const fetch = window["fetch"] || function() {
       throw new Error("fetch - missing fetch implementation for browser environment");
     };
     const readFile = function() {
@@ -20747,7 +859,7 @@ Expected: ${expectedFlat}.`);
       Video: HTMLVideoElement,
       createCanvasElement: () => document.createElement("canvas"),
       createImageElement: () => document.createElement("img"),
-      fetch: fetch3,
+      fetch,
       readFile
     };
   }
@@ -20764,8 +876,8 @@ Expected: ${expectedFlat}.`);
     }
     const readFile = fs ? function(filePath) {
       return new Promise((res, rej) => {
-        fs.readFile(filePath, function(err, buffer11) {
-          return err ? rej(err) : res(buffer11);
+        fs.readFile(filePath, function(err, buffer) {
+          return err ? rej(err) : res(buffer);
         });
       });
     } : function() {
@@ -20792,11 +904,11 @@ Expected: ${expectedFlat}.`);
       }
       throw new Error("createImageElement - missing Image implementation for nodejs environment");
     };
-    const fetch3 = global["fetch"] || function() {
+    const fetch = global["fetch"] || function() {
       throw new Error("fetch - missing fetch implementation for nodejs environment");
     };
     const fileSystem = createFileSystem();
-    return __assign({
+    return {
       Canvas: Canvas || class {
       },
       CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
@@ -20809,12 +921,13 @@ Expected: ${expectedFlat}.`);
       },
       createCanvasElement,
       createImageElement,
-      fetch: fetch3
-    }, fileSystem);
+      fetch,
+      ...fileSystem
+    };
   }
 
   // build/src/env/isBrowser.js
-  function isBrowser2() {
+  function isBrowser() {
     return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
   }
 
@@ -20822,42 +935,42 @@ Expected: ${expectedFlat}.`);
 
   // build/src/env/index.js
   const isNodejs = __toModule(require_isNodejs());
-  let environment11;
+  let environment;
   function getEnv() {
-    if (!environment11) {
+    if (!environment) {
       throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
     }
-    return environment11;
+    return environment;
   }
-  function setEnv(env17) {
-    environment11 = env17;
+  function setEnv(env16) {
+    environment = env16;
   }
   function initialize() {
-    if (isBrowser2()) {
+    if (isBrowser()) {
       return setEnv(createBrowserEnv());
     }
     if (isNodejs.isNodejs()) {
       return setEnv(createNodejsEnv());
     }
   }
-  function monkeyPatch(env17) {
-    if (!environment11) {
+  function monkeyPatch(env16) {
+    if (!environment) {
       initialize();
     }
-    if (!environment11) {
+    if (!environment) {
       throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
     }
-    const {Canvas = environment11.Canvas, Image = environment11.Image} = env17;
-    environment11.Canvas = Canvas;
-    environment11.Image = Image;
-    environment11.createCanvasElement = env17.createCanvasElement || (() => new Canvas());
-    environment11.createImageElement = env17.createImageElement || (() => new Image());
-    environment11.ImageData = env17.ImageData || environment11.ImageData;
-    environment11.Video = env17.Video || environment11.Video;
-    environment11.fetch = env17.fetch || environment11.fetch;
-    environment11.readFile = env17.readFile || environment11.readFile;
+    const {Canvas = environment.Canvas, Image = environment.Image} = env16;
+    environment.Canvas = Canvas;
+    environment.Image = Image;
+    environment.createCanvasElement = env16.createCanvasElement || (() => new Canvas());
+    environment.createImageElement = env16.createImageElement || (() => new Image());
+    environment.ImageData = env16.ImageData || environment.ImageData;
+    environment.Video = env16.Video || environment.Video;
+    environment.fetch = env16.fetch || environment.fetch;
+    environment.readFile = env16.readFile || environment.readFile;
   }
-  const env2 = {
+  const env = {
     getEnv,
     setEnv,
     initialize,
@@ -20865,14 +978,14 @@ Expected: ${expectedFlat}.`);
     createFileSystem,
     createNodejsEnv,
     monkeyPatch,
-    isBrowser: isBrowser2,
+    isBrowser,
     isNodejs: isNodejs.isNodejs
   };
   initialize();
 
   // build/src/dom/resolveInput.js
   function resolveInput(arg) {
-    if (!env2.isNodejs() && typeof arg === "string") {
+    if (!env.isNodejs() && typeof arg === "string") {
       return document.getElementById(arg);
     }
     return arg;
@@ -20880,7 +993,7 @@ Expected: ${expectedFlat}.`);
 
   // build/src/dom/getContext2dOrThrow.js
   function getContext2dOrThrow(canvasArg) {
-    const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env2.getEnv();
+    const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
     if (canvasArg instanceof CanvasRenderingContext2D2) {
       return canvasArg;
     }
@@ -21002,21 +1115,21 @@ Expected: ${expectedFlat}.`);
     detectionsArray.forEach((det) => {
       const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0;
       const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det);
-      const label = score ? `${round3(score)}` : void 0;
+      const label = score ? `${round(score)}` : void 0;
       new DrawBox(box, {label}).draw(canvasArg);
     });
   }
 
   // build/src/dom/isMediaLoaded.js
   function isMediaLoaded(media) {
-    const {Image, Video} = env2.getEnv();
+    const {Image, Video} = env.getEnv();
     return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
   }
 
   // build/src/dom/awaitMediaLoaded.js
   function awaitMediaLoaded(media) {
     return new Promise((resolve, reject) => {
-      if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media)) {
+      if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
         return resolve(null);
       }
       function onLoad(e) {
@@ -21049,7 +1162,7 @@ Expected: ${expectedFlat}.`);
         if (typeof reader.result !== "string") {
           return reject("bufferToImage - expected reader.result to be a string, in onload");
         }
-        const img = env2.getEnv().createImageElement();
+        const img = env.getEnv().createImageElement();
         img.onload = () => resolve(img);
         img.onerror = reject;
         img.src = reader.result;
@@ -21061,7 +1174,7 @@ Expected: ${expectedFlat}.`);
 
   // build/src/dom/getMediaDimensions.js
   function getMediaDimensions(input) {
-    const {Image, Video} = env2.getEnv();
+    const {Image, Video} = env.getEnv();
     if (input instanceof Image) {
       return new Dimensions(input.naturalWidth, input.naturalHeight);
     }
@@ -21073,14 +1186,14 @@ Expected: ${expectedFlat}.`);
 
   // build/src/dom/createCanvas.js
   function createCanvas({width, height}) {
-    const {createCanvasElement} = env2.getEnv();
+    const {createCanvasElement} = env.getEnv();
     const canvas = createCanvasElement();
     canvas.width = width;
     canvas.height = height;
     return canvas;
   }
   function createCanvasFromMedia(media, dims) {
-    const {ImageData: ImageData2} = env2.getEnv();
+    const {ImageData: ImageData2} = env.getEnv();
     if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
       throw new Error("createCanvasFromMedia - media has not finished loading yet");
     }
@@ -21095,24 +1208,25 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/dom/imageTensorToCanvas.js
+  const tf4 = __toModule(require("@tensorflow/tfjs"));
   async function imageTensorToCanvas(imgTensor, canvas) {
-    const targetCanvas = canvas || env2.getEnv().createCanvasElement();
+    const targetCanvas = canvas || env.getEnv().createCanvasElement();
     const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-    const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-    await browser_exports.toPixels(imgTensor3D, targetCanvas);
+    const imgTensor3D = tf4.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
+    await tf4.browser.toPixels(imgTensor3D, targetCanvas);
     imgTensor3D.dispose();
     return targetCanvas;
   }
 
   // build/src/dom/isMediaElement.js
   function isMediaElement(input) {
-    const {Image, Canvas, Video} = env2.getEnv();
+    const {Image, Canvas, Video} = env.getEnv();
     return input instanceof Image || input instanceof Canvas || input instanceof Video;
   }
 
   // build/src/dom/imageToSquare.js
   function imageToSquare(input, inputSize, centerImage = false) {
-    const {Image, Canvas} = env2.getEnv();
+    const {Image, Canvas} = env.getEnv();
     if (!(input instanceof Image || input instanceof Canvas)) {
       throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
     }
@@ -21130,6 +1244,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/dom/NetInput.js
+  const tf5 = __toModule(require("@tensorflow/tfjs"));
   class NetInput {
     constructor(inputs, treatAsBatchInput = false) {
       this._imageTensors = [];
@@ -21156,7 +1271,7 @@ Expected: ${expectedFlat}.`);
           this._inputDimensions[idx] = input.shape.slice(1);
           return;
         }
-        const canvas = input instanceof env2.getEnv().Canvas ? input : createCanvasFromMedia(input);
+        const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
         this._canvases[idx] = canvas;
         this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
       });
@@ -21180,7 +1295,7 @@ Expected: ${expectedFlat}.`);
       return this._inputSize;
     }
     get reshapedInputDimensions() {
-      return range4(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
+      return range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
     }
     getInput(batchIdx) {
       return this.canvases[batchIdx] || this.imageTensors[batchIdx];
@@ -21204,23 +1319,23 @@ Expected: ${expectedFlat}.`);
     }
     toBatchTensor(inputSize, isCenterInputs = true) {
       this._inputSize = inputSize;
-      return tidy(() => {
-        const inputTensors = range4(this.batchSize, 0, 1).map((batchIdx) => {
+      return tf5.tidy(() => {
+        const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
           const input = this.getInput(batchIdx);
-          if (input instanceof Tensor) {
+          if (input instanceof tf5.Tensor) {
             let imgTensor = isTensor4D(input) ? input : input.expandDims();
             imgTensor = padToSquare(imgTensor, isCenterInputs);
             if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
-              imgTensor = image.resizeBilinear(imgTensor, [inputSize, inputSize]);
+              imgTensor = tf5.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
             }
             return imgTensor.as3D(inputSize, inputSize, 3);
           }
-          if (input instanceof env2.getEnv().Canvas) {
-            return browser_exports.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+          if (input instanceof env.getEnv().Canvas) {
+            return tf5.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
           }
           throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
         });
-        const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
+        const batchTensor = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
         return batchTensor;
       });
     }
@@ -21257,7 +1372,7 @@ Expected: ${expectedFlat}.`);
 
   // build/src/dom/extractFaces.js
   async function extractFaces(input, detections) {
-    const {Canvas} = env2.getEnv();
+    const {Canvas} = env.getEnv();
     let canvas = input;
     if (!(input instanceof Canvas)) {
       const netInput = await toNetInput(input);
@@ -21277,6 +1392,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/dom/extractFaceTensors.js
+  const tf6 = __toModule(require("@tensorflow/tfjs"));
   async function extractFaceTensors(imageTensor, detections) {
     if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
       throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");
@@ -21284,18 +1400,18 @@ Expected: ${expectedFlat}.`);
     if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
       throw new Error("extractFaceTensors - batchSize > 1 not supported");
     }
-    return tidy(() => {
+    return tf6.tidy(() => {
       const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);
       const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight));
-      const faceTensors = boxes.map(({x, y, width, height}) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
+      const faceTensors = boxes.map(({x, y, width, height}) => tf6.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
       return faceTensors;
     });
   }
 
   // build/src/dom/fetchOrThrow.js
   async function fetchOrThrow(url, init) {
-    const fetch3 = env2.getEnv().fetch;
-    const res = await fetch3(url, init);
+    const fetch = env.getEnv().fetch;
+    const res = await fetch(url, init);
     if (!(res.status < 400)) {
       throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
     }
@@ -21350,10 +1466,11 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/dom/loadWeightMap.js
+  const tf7 = __toModule(require("@tensorflow/tfjs"));
   async function loadWeightMap(uri, defaultModelName) {
     const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
     let manifest = await fetchJson(manifestUri);
-    return io_exports.loadWeights(manifest, modelBaseUri);
+    return tf7.io.loadWeights(manifest, modelBaseUri);
   }
 
   // build/src/dom/matchDimensions.js
@@ -21369,6 +1486,7 @@ Expected: ${expectedFlat}.`);
   // build/src/dom/index.js
 
   // build/src/NeuralNetwork.js
+  const tf8 = __toModule(require("@tensorflow/tfjs"));
   class NeuralNetwork {
     constructor(_name) {
       this._name = _name;
@@ -21388,10 +1506,10 @@ Expected: ${expectedFlat}.`);
       const {obj, objProp} = this.traversePropertyPath(paramPath);
       return obj[objProp];
     }
-    reassignParamFromPath(paramPath, tensor17) {
+    reassignParamFromPath(paramPath, tensor2) {
       const {obj, objProp} = this.traversePropertyPath(paramPath);
       obj[objProp].dispose();
-      obj[objProp] = tensor17;
+      obj[objProp] = tensor2;
     }
     getParamList() {
       return this._paramMappings.map(({paramPath}) => ({
@@ -21400,21 +1518,21 @@ Expected: ${expectedFlat}.`);
       }));
     }
     getTrainableParams() {
-      return this.getParamList().filter((param) => param.tensor instanceof Variable);
+      return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable);
     }
     getFrozenParams() {
-      return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
+      return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable));
     }
     variable() {
-      this.getFrozenParams().forEach(({path, tensor: tensor17}) => {
-        this.reassignParamFromPath(path, tensor17.variable());
+      this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
+        this.reassignParamFromPath(path, tensor2.variable());
       });
     }
     freeze() {
-      this.getTrainableParams().forEach(({path, tensor: variable3}) => {
-        const tensor17 = tensor4(variable3.dataSync());
-        variable3.dispose();
-        this.reassignParamFromPath(path, tensor17);
+      this.getTrainableParams().forEach(({path, tensor: variable}) => {
+        const tensor2 = tf8.tensor(variable.dataSync());
+        variable.dispose();
+        this.reassignParamFromPath(path, tensor2);
       });
     }
     dispose(throwOnRedispose = true) {
@@ -21427,7 +1545,7 @@ Expected: ${expectedFlat}.`);
       this._params = void 0;
     }
     serializeParams() {
-      return new Float32Array(this.getParamList().map(({tensor: tensor17}) => Array.from(tensor17.dataSync())).reduce((flat, arr) => flat.concat(arr)));
+      return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
     }
     async load(weightsOrUrl) {
       if (weightsOrUrl instanceof Float32Array) {
@@ -21447,12 +1565,12 @@ Expected: ${expectedFlat}.`);
       if (filePath && typeof filePath !== "string") {
         throw new Error(`${this._name}.loadFromDisk - expected model file path`);
       }
-      const {readFile} = env2.getEnv();
+      const {readFile} = env.getEnv();
       const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName());
       const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((filePath2) => readFile(filePath2).then((buf) => buf.buffer)));
-      const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk);
+      const loadWeights = tf8.io.weightsLoaderFactory(fetchWeightsFromDisk);
       const manifest = JSON.parse((await readFile(manifestUri)).toString());
-      const weightMap = await loadWeights2(manifest, modelBaseUri);
+      const weightMap = await loadWeights(manifest, modelBaseUri);
       this.loadFromWeightMap(weightMap);
     }
     loadFromWeightMap(weightMap) {
@@ -21476,7 +1594,7 @@ Expected: ${expectedFlat}.`);
         return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
       }, {nextObj: this.params});
       const {obj, objProp} = result;
-      if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+      if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) {
         throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
       }
       return {obj, objProp};
@@ -21484,41 +1602,44 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/common/depthwiseSeparableConv.js
+  const tf9 = __toModule(require("@tensorflow/tfjs"));
   function depthwiseSeparableConv(x, params, stride) {
-    return tidy(() => {
-      let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
-      out = add2(out, params.bias);
+    return tf9.tidy(() => {
+      let out = tf9.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
+      out = tf9.add(out, params.bias);
       return out;
     });
   }
 
   // build/src/faceFeatureExtractor/denseBlock.js
+  const tf10 = __toModule(require("@tensorflow/tfjs"));
   function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
-    return tidy(() => {
-      const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
+    return tf10.tidy(() => {
+      const out1 = tf10.relu(isFirstLayer ? tf10.add(tf10.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
       const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
-      const in3 = relu(add2(out1, out2));
+      const in3 = tf10.relu(tf10.add(out1, out2));
       const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-      return relu(add2(out1, add2(out2, out3)));
+      return tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
     });
   }
   function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-    return tidy(() => {
-      const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
+    return tf10.tidy(() => {
+      const out1 = tf10.relu(isFirstLayer ? tf10.add(tf10.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
       const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
-      const in3 = relu(add2(out1, out2));
+      const in3 = tf10.relu(tf10.add(out1, out2));
       const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-      const in4 = relu(add2(out1, add2(out2, out3)));
+      const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
       const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-      return relu(add2(out1, add2(out2, add2(out3, out4))));
+      return tf10.relu(tf10.add(out1, tf10.add(out2, tf10.add(out3, out4))));
     });
   }
 
   // build/src/common/convLayer.js
+  const tf11 = __toModule(require("@tensorflow/tfjs"));
   function convLayer(x, params, padding = "same", withRelu = false) {
-    return tidy(() => {
-      const out = add2(conv2d(x, params.filters, [1, 1], padding), params.bias);
-      return withRelu ? relu(out) : out;
+    return tf11.tidy(() => {
+      const out = tf11.add(tf11.conv2d(x, params.filters, [1, 1], padding), params.bias);
+      return withRelu ? tf11.relu(out) : out;
     });
   }
 
@@ -21532,20 +1653,22 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/common/extractConvParamsFactory.js
+  const tf12 = __toModule(require("@tensorflow/tfjs"));
   function extractConvParamsFactory(extractWeights, paramMappings) {
     return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
-      const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-      const bias = tensor1d(extractWeights(channelsOut));
+      const filters = tf12.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+      const bias = tf12.tensor1d(extractWeights(channelsOut));
       paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
       return {filters, bias};
     };
   }
 
   // build/src/common/extractFCParamsFactory.js
+  const tf13 = __toModule(require("@tensorflow/tfjs"));
   function extractFCParamsFactory(extractWeights, paramMappings) {
     return function(channelsIn, channelsOut, mappedPrefix) {
-      const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-      const fc_bias = tensor1d(extractWeights(channelsOut));
+      const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
+      const fc_bias = tf13.tensor1d(extractWeights(channelsOut));
       paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
       return {
         weights: fc_weights,
@@ -21564,11 +1687,12 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/common/extractSeparableConvParamsFactory.js
+  const tf14 = __toModule(require("@tensorflow/tfjs"));
   function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
     return function(channelsIn, channelsOut, mappedPrefix) {
-      const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-      const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-      const bias = tensor1d(extractWeights(channelsOut));
+      const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+      const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+      const bias = tf14.tensor1d(extractWeights(channelsOut));
       paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
       return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
     };
@@ -21585,12 +1709,12 @@ Expected: ${expectedFlat}.`);
   // build/src/common/extractWeightEntryFactory.js
   function extractWeightEntryFactory(weightMap, paramMappings) {
     return function(originalPath, paramRank, mappedPath) {
-      const tensor17 = weightMap[originalPath];
-      if (!isTensor(tensor17, paramRank)) {
-        throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor17}`);
+      const tensor2 = weightMap[originalPath];
+      if (!isTensor(tensor2, paramRank)) {
+        throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
       }
       paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-      return tensor17;
+      return tensor2;
     };
   }
 
@@ -21700,6 +1824,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+  const tf15 = __toModule(require("@tensorflow/tfjs"));
   class FaceFeatureExtractor extends NeuralNetwork {
     constructor() {
       super("FaceFeatureExtractor");
@@ -21709,15 +1834,15 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("FaceFeatureExtractor - load model before inference");
       }
-      return tidy(() => {
+      return tf15.tidy(() => {
         const batchTensor = input.toBatchTensor(112, true);
         const meanRgb = [122.782, 117.001, 104.298];
-        const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+        const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255));
         let out = denseBlock4(normalized, params.dense0, true);
         out = denseBlock4(out, params.dense1);
         out = denseBlock4(out, params.dense2);
         out = denseBlock4(out, params.dense3);
-        out = avgPool(out, [7, 7], [2, 2], "valid");
+        out = tf15.avgPool(out, [7, 7], [2, 2], "valid");
         return out;
       });
     }
@@ -21736,8 +1861,9 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/common/fullyConnectedLayer.js
+  const tf16 = __toModule(require("@tensorflow/tfjs"));
   function fullyConnectedLayer(x, params) {
-    return tidy(() => add2(matMul(x, params.weights), params.bias));
+    return tf16.tidy(() => tf16.add(tf16.matMul(x, params.weights), params.bias));
   }
 
   // build/src/faceProcessor/extractParams.js
@@ -21783,6 +1909,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceProcessor/FaceProcessor.js
+  const tf17 = __toModule(require("@tensorflow/tfjs"));
   class FaceProcessor extends NeuralNetwork {
     constructor(_name, faceFeatureExtractor) {
       super(_name);
@@ -21796,7 +1923,7 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error(`${this._name} - load model before inference`);
       }
-      return tidy(() => {
+      return tf17.tidy(() => {
         const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
         return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
       });
@@ -21846,12 +1973,13 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceExpressionNet/FaceExpressionNet.js
+  const tf18 = __toModule(require("@tensorflow/tfjs"));
   class FaceExpressionNet extends FaceProcessor {
     constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
       super("FaceExpressionNet", faceFeatureExtractor);
     }
     forwardInput(input) {
-      return tidy(() => softmax(this.runNet(input)));
+      return tf18.tidy(() => tf18.softmax(this.runNet(input)));
     }
     async forward(input) {
       return this.forwardInput(await toNetInput(input));
@@ -21859,7 +1987,7 @@ Expected: ${expectedFlat}.`);
     async predictExpressions(input) {
       const netInput = await toNetInput(input);
       const out = await this.forwardInput(netInput);
-      const probabilitesByBatch = await Promise.all(unstack(out).map(async (t) => {
+      const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => {
         const data = await t.data();
         t.dispose();
         return data;
@@ -21901,7 +2029,7 @@ Expected: ${expectedFlat}.`);
       const sorted = expr.asSortedArray();
       const resultsToDisplay = sorted.filter((expr2) => expr2.probability > minConfidence);
       const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0);
-      const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round3(expr2.probability)})`), anchor);
+      const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round(expr2.probability)})`), anchor);
       drawTextField.draw(canvasArg);
     });
   }
@@ -22030,7 +2158,7 @@ Expected: ${expectedFlat}.`);
       reduction_block_1: entry_flow_reduction_block_1
     };
     const middle_flow = {};
-    range4(numMainBlocks, 0, 1).forEach((idx) => {
+    range(numMainBlocks, 0, 1).forEach((idx) => {
       middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
     });
     const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
@@ -22084,7 +2212,7 @@ Expected: ${expectedFlat}.`);
       reduction_block_1: entry_flow_reduction_block_1
     };
     const middle_flow = {};
-    range4(numMainBlocks, 0, 1).forEach((idx) => {
+    range(numMainBlocks, 0, 1).forEach((idx) => {
       middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
     });
     const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
@@ -22098,22 +2226,23 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/xception/TinyXception.js
+  const tf19 = __toModule(require("@tensorflow/tfjs"));
   function conv(x, params, stride) {
-    return add2(conv2d(x, params.filters, stride, "same"), params.bias);
+    return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias);
   }
   function reductionBlock(x, params, isActivateInput = true) {
-    let out = isActivateInput ? relu(x) : x;
+    let out = isActivateInput ? tf19.relu(x) : x;
     out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-    out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-    out = maxPool(out, [3, 3], [2, 2], "same");
-    out = add2(out, conv(x, params.expansion_conv, [2, 2]));
+    out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]);
+    out = tf19.maxPool(out, [3, 3], [2, 2], "same");
+    out = tf19.add(out, conv(x, params.expansion_conv, [2, 2]));
     return out;
   }
   function mainBlock(x, params) {
-    let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]);
-    out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-    out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]);
-    out = add2(out, x);
+    let out = depthwiseSeparableConv(tf19.relu(x), params.separable_conv0, [1, 1]);
+    out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]);
+    out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv2, [1, 1]);
+    out = tf19.add(out, x);
     return out;
   }
   class TinyXception extends NeuralNetwork {
@@ -22126,18 +2255,18 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("TinyXception - load model before inference");
       }
-      return tidy(() => {
+      return tf19.tidy(() => {
         const batchTensor = input.toBatchTensor(112, true);
         const meanRgb = [122.782, 117.001, 104.298];
-        const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
-        let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
+        const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256));
+        let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
         out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
         out = reductionBlock(out, params.entry_flow.reduction_block_1);
-        range4(this._numMainBlocks, 0, 1).forEach((idx) => {
+        range(this._numMainBlocks, 0, 1).forEach((idx) => {
           out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
         });
         out = reductionBlock(out, params.exit_flow.reduction_block);
-        out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
+        out = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
         return out;
       });
     }
@@ -22198,6 +2327,7 @@ Expected: ${expectedFlat}.`);
   })(Gender || (Gender = {}));
 
   // build/src/ageGenderNet/AgeGenderNet.js
+  const tf20 = __toModule(require("@tensorflow/tfjs"));
   class AgeGenderNet extends NeuralNetwork {
     constructor(faceFeatureExtractor = new TinyXception(2)) {
       super("AgeGenderNet");
@@ -22211,18 +2341,18 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error(`${this._name} - load model before inference`);
       }
-      return tidy(() => {
+      return tf20.tidy(() => {
         const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-        const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
+        const pooled = tf20.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
         const age = fullyConnectedLayer(pooled, params.fc.age).as1D();
         const gender = fullyConnectedLayer(pooled, params.fc.gender);
         return {age, gender};
       });
     }
     forwardInput(input) {
-      return tidy(() => {
+      return tf20.tidy(() => {
         const {age, gender} = this.runNet(input);
-        return {age, gender: softmax(gender)};
+        return {age, gender: tf20.softmax(gender)};
       });
     }
     async forward(input) {
@@ -22231,8 +2361,8 @@ Expected: ${expectedFlat}.`);
     async predictAgeAndGender(input) {
       const netInput = await toNetInput(input);
       const out = await this.forwardInput(netInput);
-      const ages = unstack(out.age);
-      const genders = unstack(out.gender);
+      const ages = tf20.unstack(out.age);
+      const genders = tf20.unstack(out.gender);
       const ageAndGenderTensors = ages.map((ageTensor, i) => ({
         ageTensor,
         genderTensor: genders[i]
@@ -22283,6 +2413,7 @@ Expected: ${expectedFlat}.`);
   // build/src/ageGenderNet/index.js
 
   // build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+  const tf21 = __toModule(require("@tensorflow/tfjs"));
   class FaceLandmark68NetBase extends FaceProcessor {
     postProcess(output, inputSize, originalDimensions) {
       const inputDimensions = originalDimensions.map(({width, height}) => {
@@ -22293,10 +2424,10 @@ Expected: ${expectedFlat}.`);
         };
       });
       const batchSize = inputDimensions.length;
-      return tidy(() => {
-        const createInterleavedTensor = (fillX, fillY) => stack([
-          fill([68], fillX),
-          fill([68], fillY)
+      return tf21.tidy(() => {
+        const createInterleavedTensor = (fillX, fillY) => tf21.stack([
+          tf21.fill([68], fillX),
+          tf21.fill([68], fillY)
         ], 1).as2D(1, 136).as1D();
         const getPadding = (batchIdx, cond) => {
           const {width, height} = inputDimensions[batchIdx];
@@ -22304,12 +2435,12 @@ Expected: ${expectedFlat}.`);
         };
         const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
         const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-        const landmarkTensors = output.mul(fill([batchSize, 136], inputSize)).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+        const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize)).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
         return landmarkTensors;
       });
     }
     forwardInput(input) {
-      return tidy(() => {
+      return tf21.tidy(() => {
         const out = this.runNet(input);
         return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
       });
@@ -22319,7 +2450,7 @@ Expected: ${expectedFlat}.`);
     }
     async detectLandmarks(input) {
       const netInput = await toNetInput(input);
-      const landmarkTensors = tidy(() => unstack(this.forwardInput(netInput)));
+      const landmarkTensors = tf21.tidy(() => tf21.unstack(this.forwardInput(netInput)));
       const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => {
         const landmarksArray = Array.from(await landmarkTensor.data());
         const xCoords = landmarksArray.filter((_, i) => isEven(i));
@@ -22381,6 +2512,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+  const tf22 = __toModule(require("@tensorflow/tfjs"));
   class TinyFaceFeatureExtractor extends NeuralNetwork {
     constructor() {
       super("TinyFaceFeatureExtractor");
@@ -22390,14 +2522,14 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("TinyFaceFeatureExtractor - load model before inference");
       }
-      return tidy(() => {
+      return tf22.tidy(() => {
         const batchTensor = input.toBatchTensor(112, true);
         const meanRgb = [122.782, 117.001, 104.298];
-        const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+        const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255));
         let out = denseBlock3(normalized, params.dense0, true);
         out = denseBlock3(out, params.dense1);
         out = denseBlock3(out, params.dense2);
-        out = avgPool(out, [14, 14], [2, 2], "valid");
+        out = tf22.avgPool(out, [14, 14], [2, 2], "valid");
         return out;
       });
     }
@@ -22433,17 +2565,19 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceRecognitionNet/scaleLayer.js
+  const tf23 = __toModule(require("@tensorflow/tfjs"));
   function scale(x, params) {
-    return add2(mul(x, params.weights), params.biases);
+    return tf23.add(tf23.mul(x, params.weights), params.biases);
   }
 
   // build/src/faceRecognitionNet/convLayer.js
+  const tf24 = __toModule(require("@tensorflow/tfjs"));
   function convLayer2(x, params, strides, withRelu, padding = "same") {
     const {filters, bias} = params.conv;
-    let out = conv2d(x, filters, strides, padding);
-    out = add2(out, bias);
+    let out = tf24.conv2d(x, filters, strides, padding);
+    out = tf24.add(out, bias);
     out = scale(out, params.scale);
-    return withRelu ? relu(out) : out;
+    return withRelu ? tf24.relu(out) : out;
   }
   function conv2(x, params) {
     return convLayer2(x, params, [1, 1], true);
@@ -22456,6 +2590,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceRecognitionNet/extractParams.js
+  const tf25 = __toModule(require("@tensorflow/tfjs"));
   function extractorsFactory5(extractWeights, paramMappings) {
     function extractFilterValues(numFilterValues, numFilters, filterSize) {
       const weights = extractWeights(numFilterValues);
@@ -22463,17 +2598,17 @@ Expected: ${expectedFlat}.`);
       if (isFloat(depth)) {
         throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
       }
-      return tidy(() => transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
+      return tf25.tidy(() => tf25.transpose(tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
     }
     function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
       const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-      const bias = tensor1d(extractWeights(numFilters));
+      const bias = tf25.tensor1d(extractWeights(numFilters));
       paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
       return {filters, bias};
     }
     function extractScaleLayerParams(numWeights, mappedPrefix) {
-      const weights = tensor1d(extractWeights(numWeights));
-      const biases = tensor1d(extractWeights(numWeights));
+      const weights = tf25.tensor1d(extractWeights(numWeights));
+      const biases = tf25.tensor1d(extractWeights(numWeights));
       paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
       return {
         weights,
@@ -22514,7 +2649,7 @@ Expected: ${expectedFlat}.`);
     const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1");
     const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2");
     const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out");
-    const fc = tidy(() => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
+    const fc = tf25.tidy(() => tf25.transpose(tf25.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
     paramMappings.push({paramPath: `fc`});
     if (getRemainingWeights().length !== 0) {
       throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
@@ -22611,37 +2746,39 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/faceRecognitionNet/residualLayer.js
+  const tf26 = __toModule(require("@tensorflow/tfjs"));
   function residual(x, params) {
     let out = conv2(x, params.conv1);
     out = convNoRelu(out, params.conv2);
-    out = add2(out, x);
-    out = relu(out);
+    out = tf26.add(out, x);
+    out = tf26.relu(out);
     return out;
   }
   function residualDown(x, params) {
     let out = convDown(x, params.conv1);
     out = convNoRelu(out, params.conv2);
-    let pooled = avgPool(x, 2, 2, "valid");
-    const zeros9 = zeros(pooled.shape);
+    let pooled = tf26.avgPool(x, 2, 2, "valid");
+    const zeros2 = tf26.zeros(pooled.shape);
     const isPad = pooled.shape[3] !== out.shape[3];
     const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
     if (isAdjustShape) {
       const padShapeX = [...out.shape];
       padShapeX[1] = 1;
-      const zerosW = zeros(padShapeX);
-      out = concat([out, zerosW], 1);
+      const zerosW = tf26.zeros(padShapeX);
+      out = tf26.concat([out, zerosW], 1);
       const padShapeY = [...out.shape];
       padShapeY[2] = 1;
-      const zerosH = zeros(padShapeY);
-      out = concat([out, zerosH], 2);
+      const zerosH = tf26.zeros(padShapeY);
+      out = tf26.concat([out, zerosH], 2);
     }
-    pooled = isPad ? concat([pooled, zeros9], 3) : pooled;
-    out = add2(pooled, out);
-    out = relu(out);
+    pooled = isPad ? tf26.concat([pooled, zeros2], 3) : pooled;
+    out = tf26.add(pooled, out);
+    out = tf26.relu(out);
     return out;
   }
 
   // build/src/faceRecognitionNet/FaceRecognitionNet.js
+  const tf27 = __toModule(require("@tensorflow/tfjs"));
   class FaceRecognitionNet extends NeuralNetwork {
     constructor() {
       super("FaceRecognitionNet");
@@ -22651,12 +2788,12 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("FaceRecognitionNet - load model before inference");
       }
-      return tidy(() => {
-        const batchTensor = cast(input.toBatchTensor(150, true), "float32");
+      return tf27.tidy(() => {
+        const batchTensor = tf27.cast(input.toBatchTensor(150, true), "float32");
         const meanRgb = [122.782, 117.001, 104.298];
-        const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
+        const normalized = normalize(batchTensor, meanRgb).div(tf27.scalar(256));
         let out = convDown(normalized, params.conv32_down);
-        out = maxPool(out, 3, 2, "valid");
+        out = tf27.maxPool(out, 3, 2, "valid");
         out = residual(out, params.conv32_1);
         out = residual(out, params.conv32_2);
         out = residual(out, params.conv32_3);
@@ -22672,7 +2809,7 @@ Expected: ${expectedFlat}.`);
         out = residual(out, params.conv256_2);
         out = residualDown(out, params.conv256_down_out);
         const globalAvg = out.mean([1, 2]);
-        const fullyConnected = matMul(globalAvg, params.fc);
+        const fullyConnected = tf27.matMul(globalAvg, params.fc);
         return fullyConnected;
       });
     }
@@ -22681,7 +2818,7 @@ Expected: ${expectedFlat}.`);
     }
     async computeFaceDescriptor(input) {
       const netInput = await toNetInput(input);
-      const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput)));
+      const faceDescriptorTensors = tf27.tidy(() => tf27.unstack(this.forwardInput(netInput)));
       const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data()));
       faceDescriptorTensors.forEach((t) => t.dispose());
       return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];
@@ -22731,13 +2868,14 @@ Expected: ${expectedFlat}.`);
   // build/src/factories/index.js
 
   // build/src/ssdMobilenetv1/extractParams.js
+  const tf28 = __toModule(require("@tensorflow/tfjs"));
   function extractorsFactory7(extractWeights, paramMappings) {
     function extractDepthwiseConvParams(numChannels, mappedPrefix) {
-      const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
-      const batch_norm_scale = tensor1d(extractWeights(numChannels));
-      const batch_norm_offset = tensor1d(extractWeights(numChannels));
-      const batch_norm_mean = tensor1d(extractWeights(numChannels));
-      const batch_norm_variance = tensor1d(extractWeights(numChannels));
+      const filters = tf28.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+      const batch_norm_scale = tf28.tensor1d(extractWeights(numChannels));
+      const batch_norm_offset = tf28.tensor1d(extractWeights(numChannels));
+      const batch_norm_mean = tf28.tensor1d(extractWeights(numChannels));
+      const batch_norm_variance = tf28.tensor1d(extractWeights(numChannels));
       paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`});
       return {
         filters,
@@ -22748,8 +2886,8 @@ Expected: ${expectedFlat}.`);
       };
     }
     function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-      const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-      const bias = tensor1d(extractWeights(channelsOut));
+      const filters = tf28.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+      const bias = tf28.tensor1d(extractWeights(channelsOut));
       paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
       return {filters, bias};
     }
@@ -22870,7 +3008,7 @@ Expected: ${expectedFlat}.`);
     const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory7(extractWeights, paramMappings);
     const mobilenetv1 = extractMobilenetV1Params();
     const prediction_layer = extractPredictionLayerParams();
-    const extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
+    const extra_dim = tf28.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
     const output_layer = {
       extra_dim
     };
@@ -22988,28 +3126,30 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/pointwiseConvLayer.js
+  const tf29 = __toModule(require("@tensorflow/tfjs"));
   function pointwiseConvLayer(x, params, strides) {
-    return tidy(() => {
-      let out = conv2d(x, params.filters, strides, "same");
-      out = add2(out, params.batch_norm_offset);
-      return clipByValue(out, 0, 6);
+    return tf29.tidy(() => {
+      let out = tf29.conv2d(x, params.filters, strides, "same");
+      out = tf29.add(out, params.batch_norm_offset);
+      return tf29.clipByValue(out, 0, 6);
     });
   }
 
   // build/src/ssdMobilenetv1/mobileNetV1.js
+  const tf30 = __toModule(require("@tensorflow/tfjs"));
   const epsilon = 0.0010000000474974513;
   function depthwiseConvLayer(x, params, strides) {
-    return tidy(() => {
-      let out = depthwiseConv2d(x, params.filters, strides, "same");
-      out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
-      return clipByValue(out, 0, 6);
+    return tf30.tidy(() => {
+      let out = tf30.depthwiseConv2d(x, params.filters, strides, "same");
+      out = tf30.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
+      return tf30.clipByValue(out, 0, 6);
     });
   }
   function getStridesForLayerIdx(layerIdx) {
     return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];
   }
   function mobileNetV1(x, params) {
-    return tidy(() => {
+    return tf30.tidy(() => {
       let conv11;
       let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
       const convPairParams = [
@@ -23047,7 +3187,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/nonMaxSuppression.js
-  function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+  function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
     const numBoxes = boxes.shape[0];
     const outputSize = Math.min(maxOutputSize, numBoxes);
     const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score);
@@ -23098,15 +3238,16 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/outputLayer.js
+  const tf31 = __toModule(require("@tensorflow/tfjs"));
   function getCenterCoordinatesAndSizesLayer(x) {
-    const vec = unstack(transpose(x, [1, 0]));
+    const vec = tf31.unstack(tf31.transpose(x, [1, 0]));
     const sizes = [
-      sub(vec[2], vec[0]),
-      sub(vec[3], vec[1])
+      tf31.sub(vec[2], vec[0]),
+      tf31.sub(vec[3], vec[1])
     ];
     const centers = [
-      add2(vec[0], div(sizes[0], scalar(2))),
-      add2(vec[1], div(sizes[1], scalar(2)))
+      tf31.add(vec[0], tf31.div(sizes[0], tf31.scalar(2))),
+      tf31.add(vec[1], tf31.div(sizes[1], tf31.scalar(2)))
     ];
     return {
       sizes,
@@ -23115,28 +3256,28 @@ Expected: ${expectedFlat}.`);
   }
   function decodeBoxesLayer(x0, x1) {
     const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-    const vec = unstack(transpose(x1, [1, 0]));
-    const div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
-    const add0_out = add2(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
-    const div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
-    const add1_out = add2(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
-    return transpose(stack([
-      sub(add0_out, div0_out),
-      sub(add1_out, div1_out),
-      add2(add0_out, div0_out),
-      add2(add1_out, div1_out)
+    const vec = tf31.unstack(tf31.transpose(x1, [1, 0]));
+    const div0_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2], tf31.scalar(5))), sizes[0]), tf31.scalar(2));
+    const add0_out = tf31.add(tf31.mul(tf31.div(vec[0], tf31.scalar(10)), sizes[0]), centers[0]);
+    const div1_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3], tf31.scalar(5))), sizes[1]), tf31.scalar(2));
+    const add1_out = tf31.add(tf31.mul(tf31.div(vec[1], tf31.scalar(10)), sizes[1]), centers[1]);
+    return tf31.transpose(tf31.stack([
+      tf31.sub(add0_out, div0_out),
+      tf31.sub(add1_out, div1_out),
+      tf31.add(add0_out, div0_out),
+      tf31.add(add1_out, div1_out)
     ]), [1, 0]);
   }
   function outputLayer(boxPredictions, classPredictions, params) {
-    return tidy(() => {
+    return tf31.tidy(() => {
       const batchSize = boxPredictions.shape[0];
-      let boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
-      boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
-      const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
-      let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
-      scores = reshape(scores, [batchSize, scores.shape[1]]);
-      const boxesByBatch = unstack(boxes);
-      const scoresByBatch = unstack(scores);
+      let boxes = decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf31.reshape(boxPredictions, [-1, 4]));
+      boxes = tf31.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
+      const scoresAndClasses = tf31.sigmoid(tf31.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+      let scores = tf31.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
+      scores = tf31.reshape(scores, [batchSize, scores.shape[1]]);
+      const boxesByBatch = tf31.unstack(boxes);
+      const scoresByBatch = tf31.unstack(scores);
       return {
         boxes: boxesByBatch,
         scores: scoresByBatch
@@ -23145,11 +3286,12 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/boxPredictionLayer.js
+  const tf32 = __toModule(require("@tensorflow/tfjs"));
   function boxPredictionLayer(x, params) {
-    return tidy(() => {
+    return tf32.tidy(() => {
       const batchSize = x.shape[0];
-      const boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-      const classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+      const boxPredictionEncoding = tf32.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+      const classPrediction = tf32.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
       return {
         boxPredictionEncoding,
         classPrediction
@@ -23158,8 +3300,9 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/predictionLayer.js
+  const tf33 = __toModule(require("@tensorflow/tfjs"));
   function predictionLayer(x, conv11, params) {
-    return tidy(() => {
+    return tf33.tidy(() => {
       const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
       const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
       const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
@@ -23174,7 +3317,7 @@ Expected: ${expectedFlat}.`);
       const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
       const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
       const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
-      const boxPredictions = concat([
+      const boxPredictions = tf33.concat([
         boxPrediction0.boxPredictionEncoding,
         boxPrediction1.boxPredictionEncoding,
         boxPrediction2.boxPredictionEncoding,
@@ -23182,7 +3325,7 @@ Expected: ${expectedFlat}.`);
         boxPrediction4.boxPredictionEncoding,
         boxPrediction5.boxPredictionEncoding
       ], 1);
-      const classPredictions = concat([
+      const classPredictions = tf33.concat([
         boxPrediction0.classPrediction,
         boxPrediction1.classPrediction,
         boxPrediction2.classPrediction,
@@ -23219,6 +3362,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/ssdMobilenetv1/SsdMobilenetv1.js
+  const tf34 = __toModule(require("@tensorflow/tfjs"));
   class SsdMobilenetv1 extends NeuralNetwork {
     constructor() {
       super("SsdMobilenetv1");
@@ -23228,9 +3372,9 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("SsdMobilenetv1 - load model before inference");
       }
-      return tidy(() => {
-        const batchTensor = cast(input.toBatchTensor(512, false), "float32");
-        const x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
+      return tf34.tidy(() => {
+        const batchTensor = tf34.cast(input.toBatchTensor(512, false), "float32");
+        const x = tf34.sub(tf34.mul(batchTensor, tf34.scalar(0.007843137718737125)), tf34.scalar(1));
         const features = mobileNetV1(x, params.mobilenetv1);
         const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer);
         return outputLayer(boxPredictions, classPredictions, params.output_layer);
@@ -23251,7 +3395,7 @@ Expected: ${expectedFlat}.`);
       }
       const scoresData = Array.from(await scores.data());
       const iouThreshold = 0.5;
-      const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+      const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
       const reshapedDims = netInput.getReshapedInputDimensions(0);
       const inputSize = netInput.inputSize;
       const padX = inputSize / reshapedDims.width;
@@ -23319,7 +3463,7 @@ Expected: ${expectedFlat}.`);
   const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
 
   // build/src/tinyYolov2/config.js
-  const isNumber2 = (arg) => typeof arg === "number";
+  const isNumber = (arg) => typeof arg === "number";
   function validateConfig(config2) {
     if (!config2) {
       throw new Error(`invalid config: ${config2}`);
@@ -23327,58 +3471,62 @@ Expected: ${expectedFlat}.`);
     if (typeof config2.withSeparableConvs !== "boolean") {
       throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
     }
-    if (!isNumber2(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
+    if (!isNumber(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
       throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config2.iouThreshold}`);
     }
     if (!Array.isArray(config2.classes) || !config2.classes.length || !config2.classes.every((c) => typeof c === "string")) {
       throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config2.classes)}`);
     }
-    if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) {
+    if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) {
       throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config2.anchors)}`);
     }
-    if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber2))) {
+    if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber))) {
       throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
     }
   }
 
   // build/src/tinyYolov2/leaky.js
+  const tf35 = __toModule(require("@tensorflow/tfjs"));
   function leaky(x) {
-    return tidy(() => {
-      const min4 = mul(x, scalar(0.10000000149011612));
-      return add2(relu(sub(x, min4)), min4);
+    return tf35.tidy(() => {
+      const min = tf35.mul(x, tf35.scalar(0.10000000149011612));
+      return tf35.add(tf35.relu(tf35.sub(x, min)), min);
     });
   }
 
   // build/src/tinyYolov2/convWithBatchNorm.js
+  const tf36 = __toModule(require("@tensorflow/tfjs"));
   function convWithBatchNorm(x, params) {
-    return tidy(() => {
-      let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-      out = conv2d(out, params.conv.filters, [1, 1], "valid");
-      out = sub(out, params.bn.sub);
-      out = mul(out, params.bn.truediv);
-      out = add2(out, params.conv.bias);
+    return tf36.tidy(() => {
+      let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+      out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid");
+      out = tf36.sub(out, params.bn.sub);
+      out = tf36.mul(out, params.bn.truediv);
+      out = tf36.add(out, params.conv.bias);
       return leaky(out);
     });
   }
 
   // build/src/tinyYolov2/depthwiseSeparableConv.js
+  const tf37 = __toModule(require("@tensorflow/tfjs"));
   function depthwiseSeparableConv3(x, params) {
-    return tidy(() => {
-      let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-      out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-      out = add2(out, params.bias);
+    return tf37.tidy(() => {
+      let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+      out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
+      out = tf37.add(out, params.bias);
       return leaky(out);
     });
   }
 
   // build/src/tinyYolov2/extractParams.js
+  const tf38 = __toModule(require("@tensorflow/tfjs"));
   function extractorsFactory9(extractWeights, paramMappings) {
     const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
     function extractBatchNormParams(size, mappedPrefix) {
-      const sub32 = tensor1d(extractWeights(size));
-      const truediv = tensor1d(extractWeights(size));
+      const sub6 = tf38.tensor1d(extractWeights(size));
+      const truediv = tf38.tensor1d(extractWeights(size));
       paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-      return {sub: sub32, truediv};
+      return {sub: sub6, truediv};
     }
     function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
       const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
@@ -23432,9 +3580,9 @@ Expected: ${expectedFlat}.`);
   function extractorsFactory10(weightMap, paramMappings) {
     const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
     function extractBatchNormParams(prefix) {
-      const sub32 = extractWeightEntry(`${prefix}/sub`, 1);
+      const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
       const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-      return {sub: sub32, truediv};
+      return {sub: sub6, truediv};
     }
     function extractConvParams(prefix) {
       const filters = extractWeightEntry(`${prefix}/filters`, 4);
@@ -23516,6 +3664,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/tinyYolov2/TinyYolov2Base.js
+  const tf39 = __toModule(require("@tensorflow/tfjs"));
   class TinyYolov2Base extends NeuralNetwork {
     constructor(config2) {
       super("TinyYolov2");
@@ -23533,34 +3682,34 @@ Expected: ${expectedFlat}.`);
     }
     runTinyYolov2(x, params) {
       let out = convWithBatchNorm(x, params.conv0);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = convWithBatchNorm(out, params.conv1);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = convWithBatchNorm(out, params.conv2);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = convWithBatchNorm(out, params.conv3);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = convWithBatchNorm(out, params.conv4);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = convWithBatchNorm(out, params.conv5);
-      out = maxPool(out, [2, 2], [1, 1], "same");
+      out = tf39.maxPool(out, [2, 2], [1, 1], "same");
       out = convWithBatchNorm(out, params.conv6);
       out = convWithBatchNorm(out, params.conv7);
       return convLayer(out, params.conv8, "valid", false);
     }
     runMobilenet(x, params) {
       let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv3(x, params.conv0);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = depthwiseSeparableConv3(out, params.conv1);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = depthwiseSeparableConv3(out, params.conv2);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = depthwiseSeparableConv3(out, params.conv3);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = depthwiseSeparableConv3(out, params.conv4);
-      out = maxPool(out, [2, 2], [2, 2], "same");
+      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
       out = depthwiseSeparableConv3(out, params.conv5);
-      out = maxPool(out, [2, 2], [1, 1], "same");
+      out = tf39.maxPool(out, [2, 2], [1, 1], "same");
       out = params.conv6 ? depthwiseSeparableConv3(out, params.conv6) : out;
       out = params.conv7 ? depthwiseSeparableConv3(out, params.conv7) : out;
       return convLayer(out, params.conv8, "valid", false);
@@ -23570,10 +3719,10 @@ Expected: ${expectedFlat}.`);
       if (!params) {
         throw new Error("TinyYolov2 - load model before inference");
       }
-      return tidy(() => {
-        let batchTensor = cast(input.toBatchTensor(inputSize, false), "float32");
+      return tf39.tidy(() => {
+        let batchTensor = tf39.cast(input.toBatchTensor(inputSize, false), "float32");
         batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
-        batchTensor = batchTensor.div(scalar(256));
+        batchTensor = batchTensor.div(tf39.scalar(256));
         return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
       });
     }
@@ -23584,7 +3733,7 @@ Expected: ${expectedFlat}.`);
       const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
       const netInput = await toNetInput(input);
       const out = await this.forwardInput(netInput, inputSize);
-      const out0 = tidy(() => unstack(out)[0].expandDims());
+      const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims());
       const inputDimensions = {
         width: netInput.getInputWidth(0),
         height: netInput.getInputHeight(0)
@@ -23596,7 +3745,7 @@ Expected: ${expectedFlat}.`);
       const scores = results.map((res) => res.score);
       const classScores = results.map((res) => res.classScore);
       const classNames = results.map((res) => this.config.classes[res.label]);
-      const indices = nonMaxSuppression2(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
+      const indices = nonMaxSuppression(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
       const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions));
       return detections;
     }
@@ -23621,11 +3770,11 @@ Expected: ${expectedFlat}.`);
       const correctionFactorY = inputSize / height;
       const numCells = outputTensor.shape[1];
       const numBoxes = this.config.anchors.length;
-      const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => {
+      const [boxesTensor, scoresTensor, classScoresTensor] = tf39.tidy(() => {
         const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);
         const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
         const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
-        const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0);
+        const classScores = this.withClassScores ? tf39.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tf39.scalar(0);
         return [boxes, scores, classScores];
       });
       const results = [];
@@ -23634,22 +3783,23 @@ Expected: ${expectedFlat}.`);
       for (let row = 0; row < numCells; row++) {
         for (let col = 0; col < numCells; col++) {
           for (let anchor = 0; anchor < numBoxes; anchor++) {
-            const score = sigmoid6(scoresData[row][col][anchor][0]);
+            const score = sigmoid(scoresData[row][col][anchor][0]);
             if (!scoreThreshold || score > scoreThreshold) {
-              const ctX = (col + sigmoid6(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
-              const ctY = (row + sigmoid6(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
+              const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
+              const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
               const width2 = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX;
               const height2 = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY;
               const x = ctX - width2 / 2;
               const y = ctY - height2 / 2;
               const pos = {row, col, anchor};
               const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0};
-              results.push(__assign({
+              results.push({
                 box: new BoundingBox(x, y, x + width2, y + height2),
                 score,
                 classScore: score * classScore,
-                label
-              }, pos));
+                label,
+                ...pos
+              });
             }
           }
         }
@@ -23665,7 +3815,7 @@ Expected: ${expectedFlat}.`);
       return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
         classScore,
         label
-      })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr);
+      })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
     }
   }
   TinyYolov2Base.DEFAULT_FILTER_SIZES = [
@@ -23742,11 +3892,12 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/globalApi/extractFacesAndComputeResults.js
+  const tf40 = __toModule(require("@tensorflow/tfjs"));
   async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) {
     const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection);
-    const faces = extractedFaces || (input instanceof Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
+    const faces = extractedFaces || (input instanceof tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
     const results = await computeResults(faces);
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof tf40.Tensor && f.dispose());
     return results;
   }
   async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
@@ -23964,6 +4115,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/src/globalApi/DetectFaceLandmarksTasks.js
+  const tf41 = __toModule(require("@tensorflow/tfjs"));
   class DetectFaceLandmarksTaskBase extends ComposableTask {
     constructor(parentTask, input, useTinyLandmarkNet) {
       super();
@@ -23979,9 +4131,9 @@ Expected: ${expectedFlat}.`);
     async run() {
       const parentResults = await this.parentTask;
       const detections = parentResults.map((res) => res.detection);
-      const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
+      const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
       const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face)));
-      faces.forEach((f) => f instanceof Tensor && f.dispose());
+      faces.forEach((f) => f instanceof tf41.Tensor && f.dispose());
       return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
     }
     withFaceExpressions() {
@@ -24001,9 +4153,9 @@ Expected: ${expectedFlat}.`);
         return;
       }
       const {detection} = parentResult;
-      const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
+      const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
       const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
-      faces.forEach((f) => f instanceof Tensor && f.dispose());
+      faces.forEach((f) => f instanceof tf41.Tensor && f.dispose());
       return extendWithFaceLandmarks(parentResult, landmarks);
     }
     withFaceExpressions() {
@@ -24192,7 +4344,7 @@ Expected: ${expectedFlat}.`);
   }
 
   // build/package.json
-  var version2 = "0.5.2";
+  var version = "0.5.3";
   return require_src();
 })();
 //# sourceMappingURL=face-api.iife.js.map
diff --git a/dist/face-api.iife.js.map b/dist/face-api.iife.js.map
index afdf71b..4faa9da 100644
--- a/dist/face-api.iife.js.map
+++ b/dist/face-api.iife.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["node_modules/seedrandom/lib/alea.js", "node_modules/seedrandom/lib/xor128.js", "node_modules/seedrandom/lib/xorwow.js", "node_modules/seedrandom/lib/xorshift7.js", "node_modules/seedrandom/lib/xor4096.js", "node_modules/seedrandom/lib/tychei.js", "disabled:crypto", "node_modules/seedrandom/seedrandom.js", "node_modules/seedrandom/index.js", "build/src/env/../../../src/env/isNodejs.ts", "build/src/../../src/index.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/environment.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/global_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_names.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/profiler.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tape.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_format.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/types.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/engine.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/device_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/flags.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util_env.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/operation.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/complex.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor_ops_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/types.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io_utils.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/router_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/model_management.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/buffer.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cast.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clone.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/print.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/base_side_effects.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/browser_files.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/progress.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/weights_loader.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/http.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/passthrough.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reshape.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mat_mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/one_hot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/confusion_matrix.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/math.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/browser.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/serialization.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/test_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/version.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/globals.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floorDiv.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/abs.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add_n.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/axis_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/all.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/any.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan2.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/basic_lstm_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batch_to_space_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_to.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ceil.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clip_by_value.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cumsum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depth_to_space.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depthwise_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/diag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dilation2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div_no_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/elu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/erf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expand_dims.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expm1.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tile.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/eye.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/fill.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reduce_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/segment_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/imag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_finite.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_inf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/maximum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scalar.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/leaky_relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/linspace.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/local_response_normalization.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log1p.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/gradients.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/neg.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softplus.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sub.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sum_exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_and.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_not.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_or.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_xor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_with_argmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mean.ts", 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-  "sourcesContent": ["// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],
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diff --git a/dist/face-api.min.js b/dist/face-api.min.js
index 5c80966..ab67e06 100644
--- a/dist/face-api.min.js
+++ b/dist/face-api.min.js
@@ -1,99 +1,2 @@
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Buffer shape=${this.shape}`;throw new Error(o)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Bt().makeTensor(this.values,this.shape,this.dtype)}}let Bt=null,wr=null,uw=null;function au(e){Bt=e}function cu(e){wr=e}function pu(e){uw=e}class te{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=ee(e),this.strides=Ut(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return wr.buffer(this.shape,this.dtype,e)}bufferSync(){return wr.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return kn(this.shape,e)}arraySync(){return kn(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Bt().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>pa(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Bt().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>pa(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Bt().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Bt().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return wr.print(this,e)}clone(){return this.throwIfDisposed(),wr.clone(this)}toString(e=!1){const t=this.dataSync();return iu(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),wr.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Bt().makeVariable(this,e,t,n)}}Object.defineProperty(te,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class $t extends te{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Se(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Bt().disposeTensor(this),this.dataId=e.dataId,Bt().incRef(this,null)}dispose(){Bt().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty($t,Symbol.hasInstance,{value:e=>e instanceof te&&e.assign!=null&&e.assign instanceof Function});var Wp;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Wp||(Wp={}));var Bp;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Bp||(Bp={}));var $p;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})($p||($p={}));var jp;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(jp||(jp={}));var Gp;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Gp||(Gp={}));const dw={float32:jp,int32:Bp,bool:$p,complex64:Gp};function xr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return dw[e][t]}function mw(e){return xr(e,"int32")}const lu={};_e(lu,{assertTypesMatch:()=>Pp,getTensorsInContainer:()=>rs,isTensorInList:()=>fw,makeTypesMatch:()=>Y});function Y(e,t){if(e.dtype===t.dtype)return[e,t];const n=xr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Pp(e,t){f(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function fw(e,t){return t.some(n=>n.id===e.id)}function rs(e){const t=[],n=new Set;return hu(e,t,n),t}function hu(e,t,n){if(e==null)return;if(e instanceof te){t.push(e);return}if(!gw(e))return;const r=e;for(const o in r){const i=r[o];n.has(i)||(n.add(i),hu(i,t,n))}}function gw(e){return Array.isArray(e)||typeof e=="object"}class uu{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class os{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new uu}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],r=await this.initializeBackend(n).success;if(r){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),r=n?await t:t;if(!r)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new tu(this.backendInstance),!0}setupRegisteredKernels(){const e=aa(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=aa(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(Promise.resolve(n)===n){const r=++this.pendingBackendInitId,o=n.then(i=>r<this.pendingBackendInitId?!1:(this.registry[e]=i,this.pendingBackendInit=null,!0)).catch(i=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(i.stack||i.message)),!1));return this.pendingBackendInit=o,{success:o,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:o}=this.initializeBackend(n);if(o||r)return{name:n,asyncInit:o}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,o=this.readSync(t);r.disposeData(t),n.backend=e,e.move(t,o,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{const r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return os.nextTensorId++}nextVariableId(){return os.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},r=i=>({x:()=>{const a="float32",s={x:i},c={dtype:a};return b.runKernelFunc(p=>p.cast(i,a),s,null,En,c)}}),o=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,o,{}),t}runKernel(e,t,n,r,o){const i=null,a=null;return this.runKernelFunc(i,t,a,e,n,r,o)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let o=0;n.forEach(s=>{o+=s.dtype==="complex64"?3:1});const i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-o-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,r,o,i,a){let s,c=[];const p=this.isTapeOn();r==null&&(r=this.state.activeScope!=null?this.state.activeScope.name:"");const l=this.state.numBytes,h=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let d;const g=Ji(r,this.backendName);let x;if(g!=null)d=()=>{const L=this.backend.numDataIds();x=g.kernelFunc({inputs:t,attrs:o,backend:this.backend});const S=Array.isArray(x)?x:[x];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,L,S);const I=S.map(({dataId:R,shape:A,dtype:E})=>this.makeTensorFromDataId(R,A,E));if(p){let R=this.getTensorsForGradient(r,t,I);if(R==null){a==null&&(a=[]);const A=I.filter((E,F)=>a[F]);R=(i||[]).slice().concat(A)}c=this.saveTensorsForBackwardMode(R)}return I};else{const L=S=>{if(!p)return;c=S.map(I=>this.keep(this.clone(I)))};d=()=>{const S=this.backend.numDataIds();x=this.tidy(()=>e(this.backend,L));const I=Array.isArray(x)?x:[x];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,S,I),I}}let w;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?s=d():(w=this.profiler.profileKernel(r,t,()=>d()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(w),s=w.outputs)}),p&&this.addTapeNode(r,t,s,n,c,o),this.state.profiling&&this.state.activeProfile.kernels.push({name:r,bytesAdded:this.state.numBytes-l,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-h,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(L=>t[L]!=null?t[L].shape:null),outputShapes:s.map(L=>L.shape),kernelTimeMs:w.timeMs,extraInfo:w.extraInfo}),Array.isArray(x)?s:s[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const r=sa(e);if(r!=null){const o=r.inputsToSave||[],i=r.outputsToSave||[];let a;r.saveAllInputs?(f(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(c=>t[c])):a=o.map(c=>t[c]);const s=n.filter((c,p)=>i[p]);return a.concat(s)}return null}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let o=e;n==="string"&&Tt(e[0])&&(o=e.map(s=>Mp(s)));const i=r.write(o,t,n),a=new te(t,n,i,this.nextTensorId());if(this.incRef(a,r),n==="string"){const s=this.state.tensorInfo.get(i),c=Fp(o);this.state.numBytes+=c-s.bytes,s.bytes=c}return a}makeTensorFromDataId(e,t,n,r){n=n||"float32";const o=new te(t,n,e,this.nextTensorId());return this.incRef(o,r),o}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));const o=new $t(e,t,n,this.nextTensorId());if(this.state.registeredVariables[o.name]!=null)throw new Error(`Variable with name ${o.name} was already registered`);return this.state.registeredVariables[o.name]=o,this.incRef(o,this.backend),o}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*Dp(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof $t||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,o,i){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:o},s=sa(e);s!=null&&(r=s.gradFunc),r!=null&&(a.gradient=c=>(c=c.map((p,l)=>{if(p==null){const h=n[l],d=Wt(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return p}),r(c.length>1?c:c[0],o,i))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=rs(e),n=new Set(t.map(o=>o.id));for(let o=0;o<this.state.activeScope.track.length;o++){const i=this.state.activeScope.track[o];!i.kept&&!n.has(i.id)&&i.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(o=>{!o.kept&&o.scopeId===r.id&&this.track(o)})}gradients(e,t,n,r=!1){if(f(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const o=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));f(o instanceof te,()=>"The result y returned by f() must be a tensor.");const i=nu(this.state.activeTape,t,o);if(!r&&i.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const a={};a[o.id]=n==null?bw(o.shape):n,ru(a,i,c=>this.tidy(c),ww);const s=t.map(c=>a[c.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(c=>{for(const p of c.saved)p.dispose()}),this.state.activeTape=null),{value:o,grads:s}})}customGrad(e){return f(Mt(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{f(t.every(o=>o instanceof te),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n;const r={};return t.forEach((o,i)=>{r[i]=o}),this.runKernelFunc((o,i)=>(n=e(...t,i),f(n.value instanceof te,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),f(Mt(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),r,(o,i)=>{const a=n.gradFunc(o,i),s=Array.isArray(a)?a:[a];f(s.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),f(s.every(p=>p instanceof te),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const c={};return s.forEach((p,l)=>{c[l]=()=>p}),c})}}readSync(e){const t=this.state.tensorInfo.get(e);return t.backend.readSync(e)}read(e){const t=this.state.tensorInfo.get(e);return t.backend.read(e)}async time(e){const t=ca(),n=await this.backend.time(e);return n.wallMs=ca()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new uu;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}os.nextTensorId=0;os.nextVariableId=0;function bw(e){const t=Zi(ee(e),"float32");return b.makeTensor(t,e,"float32")}function qp(){const e=Cc();if(e._tfengine==null){const t=new Nc(e);e._tfengine=new os(t)}return Vh(e._tfengine.ENV),au(()=>e._tfengine),e._tfengine}const b=qp();function ww(e,t){const n={a:e,b:t};return b.runKernelFunc((r,o)=>{const i=r.add(e,t);return o([e,t]),i},n,null,Cn)}const du={};_e(du,{isBrowser:()=>zp,isMobile:()=>yw});function xw(){return typeof navigator!="undefined"&&navigator!=null}function yw(){if(xw()){const e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function zp(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}const Kt=pe();Kt.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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Got an object with ${t.length} keys.`);let n=t[0];const r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+Hp;const o=(...i)=>{b.startScope(n);try{const a=r(...i);return a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),b.endScope(a),a}catch(a){throw b.endScope(null),a}};return Object.defineProperty(o,"name",{value:n,configurable:!0}),o}function Lw(e,t){const n=u(e,"real","complex"),r=u(t,"imag","complex");G(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);const o=a=>a.complex(n,r),i={real:n,imag:r};return b.runKernelFunc(o,i,null,Mc)}const Je=m({complex_:Lw});function Pe(e,t,n,r){if(r==null&&(r=fr(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!Ne(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Qi(t);const o=ee(t),i=ee(n);f(o===i,()=>`Based on the provided shape, [${t}], the tensor should have ${o} values but has ${i}`);for(let a=0;a<n.length;++a){const s=n[a],c=a===n.length-1?s!==ee(t.slice(a)):!0;f(n[a]===t[a]||!c,()=>`Error creating a new Tensor. 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Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const d=is[h.dtype],g=e.slice(o,o+p*d),x=h.dtype==="uint8"?new Uint8Array(g):new Uint16Array(g);if(s==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){l=new Float32Array(x.length);for(let w=0;w<x.length;w++){const L=x[w];l[w]=L*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=Iw()),l=r(x);else throw new Error(`Unsupported quantization type ${h.dtype} for weight type float32.`);else if(s==="int32"){if(h.dtype!=="uint8"&&h.dtype!=="uint16")throw new Error(`Unsupported quantization type ${h.dtype} for weight type int32.`);l=new Int32Array(x.length);for(let w=0;w<x.length;w++){const L=x[w];l[w]=Math.round(L*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${s}`);o+=p*d}else if(s==="string"){const h=ee(i.shape);l=[];for(let d=0;d<h;d++){const g=new Uint32Array(e.slice(o,o+ha))[0];o+=ha;const x=new Uint8Array(e.slice(o,o+g));l.push(x),o+=g}}else{const h=is[s],d=e.slice(o,o+p*h);if(s==="float32")l=new Float32Array(d);else if(s==="int32")l=new Int32Array(d);else if(s==="bool")l=new Uint8Array(d);else if(s==="complex64"){l=new Float32Array(d);const g=new Float32Array(l.length/2),x=new Float32Array(l.length/2);for(let S=0;S<g.length;S++)g[S]=l[S*2],x[S]=l[S*2+1];const w=Xe(g,c,"float32"),L=Xe(x,c,"float32");n[a]=Je(w,L)}else throw new Error(`Unsupported dtype in weight '${a}': ${s}`);o+=p*h}s!=="complex64"&&(n[a]=Xe(l,c,s))}return n}function vw(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(i=>{if(t+=i.byteLength,n.push(i.byteLength===i.buffer.byteLength?i:new i.constructor(i)),!(i instanceof Float32Array||i instanceof Int32Array||i instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${i.constructor.name}`)});const r=new Uint8Array(t);let o=0;return n.forEach(i=>{r.set(new Uint8Array(i.buffer),o),o+=i.byteLength}),r.buffer}const Tw=typeof 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Ie.getHandlers(e,"load",t)}static getHandlers(e,t,n){const r=[],o=t==="load"?Ie.getInstance().loadRouters:Ie.getInstance().saveRouters;return o.forEach(i=>{const a=i(e,n);a!==null&&r.push(a)}),r}}const _w=e=>Ie.registerSaveRouter(e),Cw=e=>Ie.registerLoadRouter(e),Ew=e=>Ie.getSaveHandlers(e),Ow=(e,t)=>Ie.getLoadHandlers(e,t);const yr="://";class At{constructor(){this.managers={}}static getInstance(){return At.instance==null&&(At.instance=new At),At.instance}static registerManager(e,t){f(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(yr)&&(e=e.slice(0,e.indexOf(yr))),f(e.length>0,()=>"scheme must not be an empty string.");const n=At.getInstance();f(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function ua(e){if(e.indexOf(yr)===-1)throw new Error(`The url string provided does not contain a scheme. 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Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(l){let h=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?h+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. 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Labels and predictions should have the same number of elements.`),f(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);const i=cs(_(r,"int32"),n),a=cs(_(o,"int32"),n),s=K(i);return _(V(s,a),"int32")}const nx=m({confusionMatrix_:tx});const Tu={};_e(Tu,{confusionMatrix:()=>nx});function Sr(e,t,n){if(at(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");const r=Be(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Pe(e,t,r,n)}const Ir={};_e(Ir,{fromPixels:()=>ix,toPixels:()=>ox});let Tr;function rx(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,o=!1,i=!1,a=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)o=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)i=!0;else if(e.getContext!=null)a=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(o){const g=2;if(o&&e.readyState<g)throw new Error("The video element has not loaded data yet. 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av(e){const t=u(e,"x","reverse");return f(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),et(t,0)}const Jd=m({reverse1d_:av});function cv(e,t){const n=u(e,"x","reverse");return f(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),et(n,t)}const Xd=m({reverse2d_:cv});function pv(e,t){const n=u(e,"x","reverse");return f(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),et(n,t)}const Zd=m({reverse3d_:pv});function lv(e,t){const n=u(e,"x","reverse");return f(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),et(n,t)}const Qd=m({reverse4d_:lv});function hv(e){const t=u(e,"x","round"),n={x:t};return b.runKernelFunc(r=>r.round(t),n,null,Ii)}const em=m({round_:hv});function uv(e){const t=u(e,"x","rsqrt"),n={x:t};return b.runKernelFunc((r,o)=>{const i=r.rsqrt(t);return o([t]),i},n,null,Ti)}const Da=m({rsqrt_:uv});function dv(e){const t=u(e,"x","selu"),n=(o,i)=>{const 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o=r,i=!1;r.rank===3&&(i=!0,o=y(r,[1,r.shape[0],r.shape[1],r.shape[2]]));const[a,s]=t,c=(d,g)=>(g([o]),d.resizeBilinear(o,a,s,n)),p={images:o},l={alignCorners:n,size:t},h=b.runKernelFunc(c,p,null,Li,l);return i?y(h,[h.shape[1],h.shape[2],h.shape[3]]):h}const Em=m({resizeBilinear_:iI});function sI(e,t,n=!1){const r=u(e,"images","resizeNearestNeighbor");f(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),f(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),f(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let o=r,i=!1;r.rank===3&&(i=!0,o=y(r,[1,r.shape[0],r.shape[1],r.shape[2]]));const[a,s]=t,c={images:o},p={alignCorners:n,size:t},l=(d,g)=>(g([o]),d.resizeNearestNeighbor(o,a,s,n)),h=b.runKernelFunc(l,c,null,yi,p);return i?y(h,[h.shape[1],h.shape[2],h.shape[3]]):h}const Om=m({resizeNearestNeighbor_:sI});function aI(e,t,n){f(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),f(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const r=u(e,"a","bandPart");f(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);const o=r.shape,[i,a]=r.shape.slice(-2);if(!(t<=i))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${i}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=i),n<0&&(n=a);const s=y(fs(0,i,1,"int32"),[-1,1]),c=fs(0,a,1,"int32"),p=k(s,c),l=_t(bt(p,C(+t,"int32")),gt(p,C(-n,"int32"))),h=Ce([i,a],r.dtype);return y(Fe(Ae(y(r,[-1,i,a])).map(d=>De(l,d,h))),o)}const km=m({bandPart_:aI});function cI(e){let t;if(Array.isArray(e)){t=!1,f(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const o=e[0].shape[0];for(let i=1;i<e.length;++i)f(e[i].shape[0]===o,()=>`Gram-Schmidt: Non-unique lengths found in 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n=e.shape[0],r=e.shape[1];let o=va(n),i=dt(e);const a=wt([[1]],[1,1]);let s=dt(a);const c=n>=r?r:n;for(let p=0;p<c;++p){const l=i,h=s,d=o;[s,i,o]=b.tidy(()=>{const g=P(i,[p,p],[n-p,1]),x=gs(g),w=P(i,[p,p],[1,1]),L=De(rt(w,0),wt([[-1]]),wt([[1]])),S=k(w,v(L,x)),I=D(g,S);I.shape[0]===1?s=dt(a):s=Z([a,P(I,[1,0],[I.shape[0]-1,I.shape[1]])],0);const R=re(D(V(L,S),x)),A=P(i,[p,0],[n-p,r]),E=v(R,s),F=K(s);if(p===0)i=k(A,V(E,V(F,A)));else{const $=k(A,V(E,V(F,A)));i=Z([P(i,[0,0],[p,r]),$],0)}const M=K(E),j=P(o,[0,p],[n,o.shape[1]-p]);if(p===0)o=k(j,V(V(j,s),M));else{const $=k(j,V(V(j,s),M));o=Z([P(o,[0,0],[n,p]),$],1)}return[s,i,o]}),Le([l,h,d])}return!t&&n>r&&(o=P(o,[0,0],[n,r]),i=P(i,[0,0],[r,r])),[o,i]})}const Mm=m({qr_:pI});var be;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(be||(be={}));function lI(e,t,n=be.SUM_BY_NONZERO_WEIGHTS){const r=u(e,"losses","computeWeightedLoss");let o=null;t!=null&&(o=u(t,"weights","computeWeightedLoss"));const i=o==null?r:v(r,o);if(n===be.NONE)return i;if(n===be.SUM)return W(i);if(n===be.MEAN){if(o==null)return Un(i);{const a=r.size/o.size,s=D(W(i),W(o));return a>1?D(s,C(a)):s}}if(n===be.SUM_BY_NONZERO_WEIGHTS){if(o==null)return D(W(i),C(r.size));{const a=v(o,Ct(r.shape)),s=_(W(Mr(a,C(0))),"float32");return D(W(i),s)}}throw Error(`Unknown reduction: ${n}`)}const $e=m({computeWeightedLoss_:lI});function hI(e,t,n,r=be.SUM_BY_NONZERO_WEIGHTS){const o=u(e,"labels","absoluteDifference"),i=u(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=u(n,"weights","absoluteDifference")),G(o.shape,i.shape,"Error in absoluteDifference: ");const s=Oe(k(o,i));return $e(s,a,r)}const Um=m({absoluteDifference_:hI});function uI(e,t,n,r,o=be.SUM_BY_NONZERO_WEIGHTS){const i=u(e,"labels","cosineDistance"),a=u(t,"predictions","cosineDistance");let s=null;r!=null&&(s=u(r,"weights","cosineDistance")),G(i.shape,a.shape,"Error in cosineDistance: ");const c=C(1),p=k(c,W(v(i,a),n,!0));return $e(p,s,o)}const Wm=m({cosineDistance_:uI});function dI(e,t,n,r=be.SUM_BY_NONZERO_WEIGHTS){let o=u(e,"labels","hingeLoss");const i=u(t,"predictions","hingeLoss");let a=null;n!=null&&(a=u(n,"weights","hingeLoss")),G(o.shape,i.shape,"Error in hingeLoss: ");const s=C(1);o=k(v(C(2),o),s);const c=ie(k(s,v(o,i)));return $e(c,a,r)}const Bm=m({hingeLoss_:dI});function mI(e,t,n,r=1,o=be.SUM_BY_NONZERO_WEIGHTS){const i=u(e,"labels","huberLoss"),a=u(t,"predictions","huberLoss");let s=null;n!=null&&(s=u(n,"weights","huberLoss")),G(i.shape,a.shape,"Error in huberLoss: ");const c=C(r),p=Oe(k(a,i)),l=Fr(p,c),h=k(p,l),d=N(v(C(.5),z(l)),v(c,h));return $e(d,s,o)}const $m=m({huberLoss_:mI});function fI(e,t,n,r=1e-7,o=be.SUM_BY_NONZERO_WEIGHTS){const i=u(e,"labels","logLoss"),a=u(t,"predictions","logLoss");let s=null;n!=null&&(s=u(n,"weights","logLoss")),G(i.shape,a.shape,"Error in logLoss: ");const c=C(1),p=C(r),l=re(v(i,Rt(N(a,p)))),h=v(k(c,i),Rt(N(k(c,a),p))),d=k(l,h);return $e(d,s,o)}const jm=m({logLoss_:fI});function gI(e,t,n,r=be.SUM_BY_NONZERO_WEIGHTS){const o=u(e,"labels","meanSquaredError"),i=u(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=u(n,"weights","meanSquaredError")),G(o.shape,i.shape,"Error in meanSquaredError: ");const s=jr(o,i);return $e(s,a,r)}const Gm=m({meanSquaredError_:gI});function bI(e,t){const n=u(e,"labels","sigmoidCrossEntropyWithLogits"),r=u(t,"logits","sigmoidCrossEntropyWithLogits");G(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");const o=ie(r),i=v(r,n),a=Ia(Te(re(Oe(r))));return N(k(o,i),a)}function wI(e,t,n,r=0,o=be.SUM_BY_NONZERO_WEIGHTS){let i=u(e,"multiClassLabels","sigmoidCrossEntropy");const a=u(t,"logits","sigmoidCrossEntropy");let s=null;if(n!=null&&(s=u(n,"weights","sigmoidCrossEntropy")),G(i.shape,a.shape,"Error in sigmoidCrossEntropy: "),r>0){const p=C(r),l=C(1),h=C(.5);i=N(v(i,k(l,p)),v(h,p))}const c=bI(i,a);return $e(c,s,o)}const Pm=m({sigmoidCrossEntropy_:wI});function xI(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const r=ze((o,i,a)=>{const s=!0,c=Na(i,[n],s),p=k(_(i,"float32"),c);a([o,p]);const l=re(v(p,o)),h=W(l,[n]),d=(g,x)=>{const[w,L]=x,S=we(g.shape,[n]);return[v(y(g,S),k(_(w,"float32"),Te(L))),v(y(g,S),k(Te(L),_(w,"float32")))]};return{value:h,gradFunc:d}});return r(e,t)}function yI(e,t,n,r=0,o=be.SUM_BY_NONZERO_WEIGHTS){let i=u(e,"onehotLabels","softmaxCrossEntropy");const a=u(t,"logits","softmaxCrossEntropy");let s=null;if(n!=null&&(s=u(n,"weights","softmaxCrossEntropy")),G(i.shape,a.shape,"Error in softmaxCrossEntropy: "),r>0){const p=C(r),l=C(1),h=C(i.shape[1]);i=N(v(i,k(l,p)),D(p,h))}const c=xI(i,a);return $e(c,s,o)}const qm=m({softmaxCrossEntropy_:yI});const LI={fft:Br,ifft:Wn,rfft:$r,irfft:Wa},vI={hammingWindow:wm,hannWindow:qa,frame:za,stft:xm},Sl={flipLeftRight:Lm,resizeNearestNeighbor:Om,resizeBilinear:Em,rotateWithOffset:vm,cropAndResize:ym,nonMaxSuppression:Sm,nonMaxSuppressionAsync:Am,nonMaxSuppressionWithScore:Nm,nonMaxSuppressionWithScoreAsync:Rm,nonMaxSuppressionPadded:_m,nonMaxSuppressionPaddedAsync:Cm},SI={bandPart:km,gramSchmidt:Dm,qr:Mm},II={absoluteDifference:Um,computeWeightedLoss:$e,cosineDistance:Wm,hingeLoss:Bm,huberLoss:$m,logLoss:jm,meanSquaredError:Gm,sigmoidCrossEntropy:Pm,softmaxCrossEntropy:qm};class ot extends sl{minimize(e,t=!1,n){const{value:r,grads:o}=this.computeGradients(e,n);if(n!=null){const i=n.map(a=>({name:a.name,tensor:o[a.name]}));this.applyGradients(i)}else this.applyGradients(o);return Le(o),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return wl(e,t)}dispose(){this.iterations_!=null&&Le(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:C(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(ot,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class Pn extends ot{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=b.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=b.registeredVariables[n],i=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accum_grad`,variable:O(()=>B(o).variable(i))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${n}/accum_var`,variable:O(()=>B(o).variable(i))});const a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;const s=this.accumulatedGrads[r].variable,c=this.accumulatedUpdates[r].variable;O(()=>{const p=N(v(s,this.rho),v(z(a),1-this.rho)),l=v(D(ge(N(c,this.epsilon)),ge(N(s,this.epsilon))),a),h=N(v(c,this.rho),v(z(l),1-this.rho));s.assign(p),c.assign(h);const d=N(v(l,-this.learningRate),o);o.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Le(this.accumulatedGrads.map(e=>e.variable)),Le(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}Pn.className="Adadelta";Ze(Pn);class qn extends ot{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=b.registeredVariables[n];if(this.accumulatedGrads[r]==null){const s=!1;this.accumulatedGrads[r]={originalName:`${n}/accumulator`,variable:O(()=>qe(o.shape,this.initialAccumulatorValue).variable(s))}}const i=Array.isArray(e)?e[r].tensor:e[n];if(i==null)return;const a=this.accumulatedGrads[r].variable;O(()=>{const s=N(a,z(i));a.assign(s);const c=N(v(D(i,ge(N(s,b.backend.epsilon()))),-this.learningRate),o);o.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Le(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}qn.className="Adagrad";Ze(qn);class zn extends ot{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=C(t).variable(),this.accBeta2=C(n).variable()}),r==null&&(this.epsilon=b.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{const n=k(1,this.accBeta1),r=k(1,this.accBeta2);t.forEach((o,i)=>{const a=b.registeredVariables[o],s=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:O(()=>B(a).variable(s))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${o}/v`,variable:O(()=>B(a).variable(s))});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const p=this.accumulatedFirstMoment[i].variable,l=this.accumulatedSecondMoment[i].variable,h=N(v(p,this.beta1),v(c,1-this.beta1)),d=N(v(l,this.beta2),v(z(c),1-this.beta2)),g=D(h,n),x=D(d,r);p.assign(h),l.assign(d);const w=N(v(D(g,N(ge(x),this.epsilon)),-this.learningRate),a);a.assign(w)}),this.accBeta1.assign(v(this.accBeta1,this.beta1)),this.accBeta2.assign(v(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Le(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Le(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),O(()=>{this.accBeta1.assign(Qe(this.beta1,this.iterations_+1)),this.accBeta2.assign(Qe(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}zn.className="Adam";Ze(zn);class Hn extends ot{constructor(e,t,n,r=null,o=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=o,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=C(0).variable(),this.accBeta1=C(t).variable()}),r==null&&(this.epsilon=b.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{const n=k(1,this.accBeta1),r=D(-this.learningRate,N(v(this.iteration,this.decay),1));t.forEach((o,i)=>{const a=b.registeredVariables[o],s=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:B(a).variable(s)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${o}/v`,variable:B(a).variable(s)});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const p=this.accumulatedFirstMoment[i].variable,l=this.accumulatedWeightedInfNorm[i].variable,h=N(v(p,this.beta1),v(c,1-this.beta1)),d=v(l,this.beta2),g=Oe(c),x=Pt(d,g);p.assign(h),l.assign(x);const w=N(v(D(r,n),D(h,N(x,this.epsilon))),a);a.assign(w)}),this.iteration.assign(N(this.iteration,1)),this.accBeta1.assign(v(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Le(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Le(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}Hn.className="Adamax";Ze(Hn);class en extends ot{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;const i=b.registeredVariables[n];O(()=>{const a=N(v(this.c,o),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=ll(C(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}en.className="SGD";Ze(en);class Yn extends en{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=C(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=b.registeredVariables[n];if(this.accumulations[r]==null){const s=!1;this.accumulations[r]={originalName:`${n}/momentum`,variable:O(()=>B(o).variable(s))}}const i=this.accumulations[r].variable,a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;O(()=>{let s;const c=N(v(this.m,i),a);this.useNesterov?s=N(v(this.c,N(a,v(c,this.m))),o):s=N(v(this.c,c),o),i.assign(c),o.assign(s)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Le(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Yn.className="Momentum";Ze(Yn);class Vn extends ot{constructor(e,t=.9,n=0,r=null,o=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=o,r==null&&(this.epsilon=b.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=b.registeredVariables[n],i=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:O(()=>B(o).variable(i))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:O(()=>B(o).variable(i))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:O(()=>B(o).variable(i))});const a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;const s=this.accumulatedMeanSquares[r].variable,c=this.accumulatedMoments[r].variable;O(()=>{const p=N(v(s,this.decay),v(z(a),1-this.decay));if(this.centered){const l=this.accumulatedMeanGrads[r].variable,h=N(v(l,this.decay),v(a,1-this.decay)),d=D(v(a,this.learningRate),ge(k(p,N(z(h),this.epsilon)))),g=N(v(c,this.momentum),d);s.assign(p),l.assign(h),c.assign(g);const x=k(o,g);o.assign(x)}else{const l=N(v(s,this.decay),v(z(a),1-this.decay)),h=N(v(c,this.momentum),D(v(a,this.learningRate),ge(N(l,this.epsilon))));s.assign(l),c.assign(h);const d=k(o,h);o.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Le(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Le(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Le(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Vn.className="RMSProp";Ze(Vn);class vn{static sgd(e){return new en(e)}static momentum(e,t,n=!1){return new Yn(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,o=!1){return new Vn(e,t,n,r,o)}static adam(e=.001,t=.9,n=.999,r=null){return new zn(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Pn(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,o=0){return new Hn(e,t,n,r,o)}static adagrad(e,t=.1){return new qn(e,t)}}Yn,en,Pn,qn,Vn,Hn,zn;const TI={sgd:vn.sgd,momentum:vn.momentum,adadelta:vn.adadelta,adagrad:vn.adagrad,rmsprop:vn.rmsprop,adamax:vn.adamax,adam:vn.adam};const AI=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function NI(){return new Promise(e=>AI(()=>e()))}function RI(e,t,n){const r=n*(typeof e=="number"?e:e[0]),o=t*(typeof e=="number"?e:e[1]);return[r,o]}function _I(e,t,n,r=!0){let o=[];if(r)o=o.concat(t.slice(0)),o.push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);const i=t.length;for(let a=0;a<i;++a)o=o.concat([e[a+1]/t[a],t[a]]);o=o.concat(e.slice(i+1))}return o}function CI(e,t,n=!0){const r=[];if(n){r.push(t);for(let o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{const o=[],i=[];for(let a=1;a<e;++a)a>=t*2+1||a%2===1?i.push(a):o.push(a);r.push(...o),r.push(0),r.push(...i)}return r}function EI(e,t,n,r=!0){const o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(let i=1;i<e.length;++i)i<=t.length?r?o.push(t[i-1]*e[i]):o.push(e[i]/t[i-1]):o.push(e[i]);return o}function OI(e,t){const n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function kI(e,t,n){const r=e.slice(0,1);for(let o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}const Il=1.7580993408473768,Tl=1.0507009873554805;const DI=.3275911,FI=.254829592,MI=-.284496736,UI=1.421413741,WI=-1.453152027,BI=1.061405429;function $I(...e){pe().getBool("IS_TEST")||console.warn(...e)}function jI(...e){pe().getBool("IS_TEST")||console.log(...e)}function GI(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function PI(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function qI(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function zI(e){const t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function HI(e,t){const n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function YI(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function VI(e,t){const n=new Float32Array(e/2),r=new Float32Array(e/2);for(let o=0;o<Math.ceil(e/2);o++){const i=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(i),r[o]=Math.sin(i)}return{real:n,imag:r}}function KI(e,t,n){const r=(n?2:-2)*Math.PI*(e/t),o=Math.cos(r),i=Math.sin(r);return{real:o,imag:i}}const zm={};_e(zm,{ERF_A1:()=>FI,ERF_A2:()=>MI,ERF_A3:()=>UI,ERF_A4:()=>WI,ERF_A5:()=>BI,ERF_P:()=>DI,PARALLELIZE_THRESHOLD:()=>Sa,SELU_SCALE:()=>Tl,SELU_SCALEALPHA:()=>Il,applyActivation:()=>jn,assertAndGetBroadcastShape:()=>q,assertAxesAreInnerMostDims:()=>Bx,assertParamsConsistent:()=>fl,assignToTypedArray:()=>YI,axesAreInnerMostDims:()=>hl,calculateShapes:()=>_u,castTensor:()=>JI,combineLocations:()=>qu,complexWithEvenIndex:()=>qI,complexWithOddIndex:()=>zI,computeConv2DInfo:()=>je,computeConv3DInfo:()=>bn,computeDefaultPad:()=>ml,computeDilation2DInfo:()=>Kx,computeOptimalWindowSize:()=>Yy,computeOutAndReduceShapes:()=>ul,computeOutShape:()=>gl,computePool2DInfo:()=>Nt,computePool3DInfo:()=>Xt,convertConv2DDataFormat:()=>gn,eitherStridesOrDilationsAreOne:()=>le,expandShapeToKeepDim:()=>we,exponent:()=>KI,exponents:()=>VI,getAxesPermutation:()=>fe,getBroadcastDims:()=>ky,getComplexWithIndex:()=>HI,getFusedBiasGradient:()=>$n,getFusedDyActivation:()=>Bn,getImageCenter:()=>RI,getInnerMostAxes:()=>ke,getPermuted:()=>CI,getReductionAxes:()=>ae,getReshaped:()=>_I,getReshapedPermuted:()=>EI,getSliceBeginCoords:()=>OI,getSliceSize:()=>kI,getUndoAxesPermutation:()=>Fn,linspaceImpl:()=>ZI,log:()=>jI,mergeRealAndImagArrays:()=>GI,prepareAndValidate:()=>Nu,prepareSplitSize:()=>yl,reshapeTensor:()=>XI,segment_util:()=>vd,shouldFuse:()=>Gn,slice_util:()=>nl,splitRealAndImagArrays:()=>PI,tupleValuesAreOne:()=>ct,upcastType:()=>xr,validateInput:()=>ma,validateUpdateShape:()=>tl,warn:()=>$I});function JI(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const r=Ce(e.shape),o=_(e,"float32"),i=n.complex(o,r);return r.dispose(),o.dispose(),i}if(!kp(e.dtype,t))return b.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const r=n.real(e),o=_(r,t);return r.dispose(),o}if(t==="int32")return n.int(e);if(t==="bool"){const r=C(0,e.dtype),o=n.notEqual(e,r);return r.dispose(),o}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function XI(e,t){return b.makeTensorFromDataId(e.dataId,t,e.dtype)}function ZI(e,t,n){const r=(t-e)/(n-1),o=Wt(n,"float32");o[0]=e;for(let i=1;i<o.length;i++)o[i]=o[i-1]+r;return ce(o,"float32")}function Hm(e,t,n){const r=new Array(e.rank).fill(0),o=e.shape.slice();return t.map(i=>{const a=[...o];a[n]=i;const s=P(e,r,a);return r[n]+=i,s})}function Ym(e,t){const n=new Array(e.rank);for(let o=0;o<n.length;o++)n[o]=e.shape[o]*t[o];const r=Ee(n,e.dtype);for(let o=0;o<r.values.length;++o){const i=r.indexToLoc(o),a=new Array(e.rank);for(let c=0;c<a.length;c++)a[c]=i[c]%e.shape[c];const s=e.locToIndex(a);r.values[o]=e.values[s]}return r.toTensor()}function Vm(e,t,n,r,o){const i=t[t.length-1],[a,s]=[e.length/i,i],c=mr(n,a*r),p=mr("int32",a*r);for(let h=0;h<a;h++){const d=h*s,g=e.subarray(d,d+s),x=[];for(let I=0;I<g.length;I++)x.push({value:g[I],index:I});x.sort((I,R)=>R.value-I.value);const w=h*r,L=c.subarray(w,w+r),S=p.subarray(w,w+r);for(let I=0;I<r;I++)L[I]=x[I].value,S[I]=x[I].index}const l=t.slice();return l[l.length-1]=r,[Xe(c,l,n),Xe(p,l,"int32")]}const Km={};_e(Km,{nonMaxSuppressionV3Impl:()=>Ha,nonMaxSuppressionV4Impl:()=>Ya,nonMaxSuppressionV5Impl:()=>Va,split:()=>Hm,tile:()=>Ym,topkImpl:()=>Vm,whereImpl:()=>$a});const QI=1e-7,eT=1e-4;class tT{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return 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T("realDivide")}floorDiv(e,t){return T("floorDiv")}sum(e,t){return T("sum")}prod(e,t){return T("prod")}unsortedSegmentSum(e,t,n){return T("unsortedSegmentSum")}argMin(e,t){return T("argMin")}argMax(e,t){return T("argMax")}equal(e,t){return T("equal")}notEqual(e,t){return T("notEqual")}less(e,t){return T("less")}lessEqual(e,t){return T("lessEqual")}greater(e,t){return T("greater")}greaterEqual(e,t){return T("greaterEqual")}logicalNot(e){return T("logicalNot")}logicalAnd(e,t){return T("logicalAnd")}logicalOr(e,t){return T("logicalOr")}where(e){return T("where")}select(e,t,n){return T("select")}topk(e,t,n){return T("topk")}min(e,t){return T("min")}minimum(e,t){return T("minimum")}mod(e,t){return T("mod")}max(e,t){return T("max")}maximum(e,t){return T("maximum")}all(e,t){return T("all")}any(e,t){return T("any")}squaredDifference(e,t){return T("squaredDifference")}ceil(e){return T("ceil")}floor(e){return T("floor")}round(e){return T("round")}sign(e){return T("sign")}isNaN(e){return 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tn(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:r,y:o},i)=>{const a=t[i];e.moveTo(a.x,a.y),e.lineTo(r,o)}),n){const r=t[t.length-1],o=t[0];if(!r||!o)return;e.moveTo(r.x,r.y),e.lineTo(o.x,o.y)}e.stroke()}class Ke{constructor(e,t){if(!Et(e)||!Et(t))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:e,height:t})}`);this._width=e,this._height=t}get width(){return this._width}get height(){return this._height}reverse(){return new Ke(1/this.width,1/this.height)}}const Cs={};_e(Cs,{computeReshapedDimensions:()=>Ol,getCenterPoint:()=>Xn,isDimensions:()=>Xa,isEven:()=>Ja,isFloat:()=>El,isTensor:()=>Kn,isTensor1D:()=>hT,isTensor2D:()=>Cl,isTensor3D:()=>nn,isTensor4D:()=>pt,isValidNumber:()=>Et,isValidProbablitiy:()=>qr,range:()=>Ht,round:()=>Jn});function Kn(e,t){return e instanceof te&&e.shape.length===t}function hT(e){return Kn(e,1)}function Cl(e){return Kn(e,2)}function nn(e){return Kn(e,3)}function pt(e){return 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n=t.length-1;n>0;n--){const r=Math.floor(Math.random()*(n+1)),o=t[n];t[n]=t[r],t[r]=o}return t}function or(e){return 1/(1+Math.exp(-e))}function bh(e){return Math.log(e/(1-e))}class pr extends de{constructor(e,t,n,r,o=!1){super({x:e,y:t,width:n,height:r},o)}}const uT=.5,dT=.43,mT=.45;class st{constructor(e,t,n=new J(0,0)){const{width:r,height:o}=t;this._imgDims=new Ke(r,o),this._shift=n,this._positions=e.map(i=>i.mul(new J(r,o)).add(n))}get shift(){return new J(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(e=>e.sub(this._shift).div(new J(this.imageWidth,this.imageHeight)))}forSize(e,t){return new this.constructor(this.relativePositions,{width:e,height:t})}shiftBy(e,t){return new this.constructor(this.relativePositions,this._imgDims,new J(e,t))}shiftByPoint(e){return this.shiftBy(e.x,e.y)}align(e,t={}){if(e){const o=e instanceof me?e.box.floor():new de(e);return this.shiftBy(o.x,o.y).align(null,t)}const{useDlibAlignment:n,minBoxPadding:r}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},t);return n?this.alignDlib():this.alignMinBbox(r)}alignDlib(){const e=this.getRefPointsForAlignment(),[t,n,r]=e,o=l=>r.sub(l).magnitude(),i=(o(t)+o(n))/2,a=Math.floor(i/mT),s=Xn(e),c=Math.floor(Math.max(0,s.x-uT*a)),p=Math.floor(Math.max(0,s.y-dT*a));return new pr(c,p,Math.min(a,this.imageWidth+c),Math.min(a,this.imageHeight+p))}alignMinBbox(e){const t=Ms(this.positions);return t.pad(t.width*e,t.height*e)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class zh extends st{getRefPointsForAlignment(){const e=this.positions;return[e[0],e[1],Xn([e[3],e[4]])]}}class lr extends st{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Xn)}}class lo{constructor(e,t){this._label=e,this._distance=t}get label(){return this._label}get distance(){return this._distance}toString(e=!0){return`${this.label}${e?` (${Jn(this.distance)})`:""}`}}class po extends de{constructor(e,t){super(e);this._label=t}static assertIsValidLabeledBox(e,t){if(de.assertIsValidBox(e,t),!Et(e.label))throw new Error(`${t} - expected property label (${e.label}) to be a number`)}get label(){return this._label}}class fn{constructor(e,t){if(!(typeof e=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(t)||t.some(n=>!(n instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=e,this._descriptors=t}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(e=>Array.from(e))}}static fromJSON(e){const t=e.descriptors.map(n=>new Float32Array(n));return new fn(e.label,t)}}class qh extends po{constructor(e,t,n,r){super(e,t);this._score=n,this._classScore=r}static assertIsValidPredictedBox(e,t){if(po.assertIsValidLabeledBox(e,t),!qr(e.score)||!qr(e.classScore))throw new Error(`${t} - expected properties score (${e.score}) and (${e.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}}function It(e){return e.detection instanceof me}function ln(e,t){const n={detection:t};return Object.assign({},e,n)}function kl(){const e=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},t=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function Za(e){let t="";if(!e)try{e=require("fs")}catch(r){t=r.toString()}const n=e?function(r){return new Promise((o,i)=>{e.readFile(r,function(a,s){return a?i(a):o(s)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)};return{readFile:n}}function Dl(){const e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=function(){if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=function(){if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},o=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},i=Za();return As({Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:r,fetch:o},i)}function Fl(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const Ml=zl(ph());let Me;function fT(){if(!Me)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Me}function Ul(e){Me=e}function Wl(){if(Fl())return Ul(kl());if(Ml.isNodejs())return Ul(Dl())}function gT(e){if(Me||Wl(),!Me)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:t=Me.Canvas,Image:n=Me.Image}=e;Me.Canvas=t,Me.Image=n,Me.createCanvasElement=e.createCanvasElement||(()=>new t),Me.createImageElement=e.createImageElement||(()=>new n),Me.ImageData=e.ImageData||Me.ImageData,Me.Video=e.Video||Me.Video,Me.fetch=e.fetch||Me.fetch,Me.readFile=e.readFile||Me.readFile}const se={getEnv:fT,setEnv:Ul,initialize:Wl,createBrowserEnv:kl,createFileSystem:Za,createNodejsEnv:Dl,monkeyPatch:gT,isBrowser:Fl,isNodejs:Ml.isNodejs};Wl();function un(e){return!se.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Ge(e){const{Canvas:t,CanvasRenderingContext2D:n}=se.getEnv();if(e instanceof n)return e;const r=un(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const o=r.getContext("2d");if(!o)throw new Error("resolveContext2d - canvas 2d context is null");return o}var Yt;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Yt||(Yt={}));class Qa{constructor(e={}){const{anchorPosition:t,backgroundColor:n,fontColor:r,fontSize:o,fontStyle:i,padding:a}=e;this.anchorPosition=t||Yt.TOP_LEFT,this.backgroundColor=n||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=o||14,this.fontStyle=i||"Georgia",this.padding=a||4}}class zr{constructor(e,t,n={}){this.text=typeof e=="string"?[e]:e instanceof zr?e.text:e,this.anchor=t,this.options=new Qa(n)}measureWidth(e){const{padding:t}=this.options;return this.text.map(n=>e.measureText(n).width).reduce((n,r)=>n<r?r:n,0)+2*t}measureHeight(){const{fontSize:e,padding:t}=this.options;return this.text.length*e+2*t}getUpperLeft(e,t){const{anchorPosition:n}=this.options,r=n===Yt.BOTTOM_RIGHT||n===Yt.TOP_RIGHT,o=n===Yt.BOTTOM_LEFT||n===Yt.BOTTOM_RIGHT,i=this.measureWidth(e),a=this.measureHeight(),s=r?this.anchor.x-i:this.anchor.x,c=o?this.anchor.y-a:this.anchor.y;if(t){const{width:p,height:l}=t,h=Math.max(Math.min(s,p-i),0),d=Math.max(Math.min(c,l-a),0);return{x:h,y:d}}return{x:s,y:c}}draw(e){const t=un(e),n=Ge(t),{backgroundColor:r,fontColor:o,fontSize:i,fontStyle:a,padding:s}=this.options;n.font=`${i}px ${a}`;const c=this.measureWidth(n),p=this.measureHeight();n.fillStyle=r;const l=this.getUpperLeft(n,t);n.fillRect(l.x,l.y,c,p),n.fillStyle=o,this.text.forEach((h,d)=>{const g=s+l.x,x=s+l.y+(d+1)*i;n.fillText(h,g,x)})}}class jg{constructor(e={}){const{boxColor:t,lineWidth:n,label:r,drawLabelOptions:o}=e;this.boxColor=t||"rgba(0, 0, 255, 1)",this.lineWidth=n||2,this.label=r;const i={anchorPosition:Yt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Qa(Object.assign({},i,o))}}class Bl{constructor(e,t={}){this.box=new de(e),this.options=new jg(t)}draw(e){const t=Ge(e),{boxColor:n,lineWidth:r}=this.options,{x:o,y:i,width:a,height:s}=this.box;t.strokeStyle=n,t.lineWidth=r,t.strokeRect(o,i,a,s);const{label:c}=this.options;c&&new zr([c],{x:o-r/2,y:i},this.options.drawLabelOptions).draw(e)}}function bT(e,t){const n=Array.isArray(t)?t:[t];n.forEach(r=>{const o=r instanceof me?r.score:It(r)?r.detection.score:void 0,i=r instanceof me?r.box:It(r)?r.detection.box:new de(r),a=o?`${Jn(o)}`:void 0;new Bl(i,{label:a}).draw(e)})}function ar(e){const{Image:t,Video:n}=se.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function ea(e){return new Promise((t,n)=>{if(e instanceof se.getEnv().Canvas||ar(e))return t(null);function r(i){if(!i.currentTarget)return;i.currentTarget.removeEventListener("load",r),i.currentTarget.removeEventListener("error",o),t(i)}function o(i){if(!i.currentTarget)return;i.currentTarget.removeEventListener("load",r),i.currentTarget.removeEventListener("error",o),n(i)}e.addEventListener("load",r),e.addEventListener("error",o)})}function Qs(e){return new Promise((t,n)=>{if(!(e instanceof Blob))return n("bufferToImage - expected buf to be of type: Blob");const r=new FileReader;r.onload=()=>{if(typeof r.result!="string")return n("bufferToImage - expected reader.result to be a string, in onload");const o=se.getEnv().createImageElement();o.onload=()=>t(o),o.onerror=n,o.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function dn(e){const{Image:t,Video:n}=se.getEnv();return e instanceof t?new Ke(e.naturalWidth,e.naturalHeight):e instanceof n?new Ke(e.videoWidth,e.videoHeight):new Ke(e.width,e.height)}function Rn({width:e,height:t}){const{createCanvasElement:n}=se.getEnv(),r=n();return r.width=e,r.height=t,r}function cr(e,t){const{ImageData:n}=se.getEnv();if(!(e instanceof n)&&!ar(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:r,height:o}=t||dn(e),i=Rn({width:r,height:o});return e instanceof n?Ge(i).putImageData(e,0,0):Ge(i).drawImage(e,0,0,r,o),i}async function Xs(e,t){const n=t||se.getEnv().createCanvasElement(),[r,o,i]=e.shape.slice(pt(e)?1:0),a=O(()=>e.as3D(r,o,i).toInt());return await Ir.toPixels(a,n),a.dispose(),n}function co(e){const{Image:t,Canvas:n,Video:r}=se.getEnv();return e instanceof t||e instanceof n||e instanceof r}function Js(e,t,n=!1){const{Image:r,Canvas:o}=se.getEnv();if(!(e instanceof r||e instanceof o))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const i=dn(e),a=t/Math.max(i.height,i.width),s=a*i.width,c=a*i.height,p=Rn({width:t,height:t}),l=e instanceof o?e:cr(e),h=Math.abs(s-c)/2,d=n&&s<c?h:0,g=n&&c<s?h:0;return Ge(p).drawImage(l,d,g,s,c),p}class Vt{constructor(e,t=!1){if(this._imageTensors=[],this._canvases=[],this._treatAsBatchInput=!1,this._inputDimensions=[],!Array.isArray(e))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${e}`);this._treatAsBatchInput=t,this._batchSize=e.length,e.forEach((n,r)=>{if(nn(n)){this._imageTensors[r]=n,this._inputDimensions[r]=n.shape;return}if(pt(n)){const i=n.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=n,this._inputDimensions[r]=n.shape.slice(1);return}const o=n instanceof se.getEnv().Canvas?n:cr(n);this._canvases[r]=o,this._inputDimensions[r]=[o.height,o.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return Ht(this.batchSize,0,1).map((e,t)=>this.getReshapedInputDimensions(t))}getInput(e){return this.canvases[e]||this.imageTensors[e]}getInputDimensions(e){return this._inputDimensions[e]}getInputHeight(e){return this._inputDimensions[e][0]}getInputWidth(e){return this._inputDimensions[e][1]}getReshapedInputDimensions(e){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");const t=this.getInputWidth(e),n=this.getInputHeight(e);return Ol({width:t,height:n},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,O(()=>{const n=Ht(this.batchSize,0,1).map(o=>{const i=this.getInput(o);if(i instanceof te){let a=pt(i)?i:i.expandDims();return a=Ds(a,t),(a.shape[1]!==e||a.shape[2]!==e)&&(a=Sl.resizeBilinear(a,[e,e])),a.as3D(e,e,3)}if(i instanceof se.getEnv().Canvas)return Ir.fromPixels(Js(i,e,t));throw new Error(`toBatchTensor - at batchIdx ${o}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)}),r=Fe(n.map(o=>_(o,"float32"))).as4D(this.batchSize,e,e,3);return r})}}async function ue(e){if(e instanceof Vt)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");const n=o=>Array.isArray(e)?` at input index ${o}:`:"",r=t.map(un);return r.forEach((o,i)=>{if(!co(o)&&!nn(o)&&!pt(o))throw typeof t[i]=="string"?new Error(`toNetInput -${n(i)} string passed, but could not resolve HTMLElement for element id ${t[i]}`):new Error(`toNetInput -${n(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(pt(o)){const a=o.shape[0];if(a!==1)throw new Error(`toNetInput -${n(i)} tf.Tensor4D with batchSize ${a} passed, but not supported in input array`)}}),await Promise.all(r.map(o=>co(o)&&ea(o))),new Vt(r,Array.isArray(e))}async function An(e,t){const{Canvas:n}=se.getEnv();let r=e;if(!(e instanceof n)){const a=await ue(e);if(a.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const s=a.getInput(0);r=s instanceof n?s:await Xs(s)}const o=Ge(r),i=t.map(a=>a instanceof me?a.forSize(r.width,r.height).box.floor():a).map(a=>a.clipAtImageBorders(r.width,r.height));return i.map(({x:a,y:s,width:c,height:p})=>{const l=Rn({width:c,height:p});return Ge(l).putImageData(o.getImageData(a,s,c,p),0,0),l})}async function Nn(e,t){if(!nn(e)&&!pt(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(pt(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return O(()=>{const[n,r,o]=e.shape.slice(pt(e)?1:0),i=t.map(s=>s instanceof me?s.forSize(r,n).box:s).map(s=>s.clipAtImageBorders(r,n)),a=i.map(({x:s,y:c,width:p,height:l})=>Ua(e.as3D(n,r,o),[c,s,0],[l,p,o]));return a})}async function mn(e,t){const n=se.getEnv().fetch,r=await n(e,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Ph(e){const t=await mn(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return Qs(n)}async function Zs(e){return(await mn(e)).json()}async function Gh(e){return new Float32Array(await(await mn(e)).arrayBuffer())}function ec(e,t){const n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};const r=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(r,"");const o=e.split("/").filter(s=>s),i=e.endsWith(".json")?o[o.length-1]:n;let a=r+(e.endsWith(".json")?o.slice(0,o.length-1):o).join("/");return a=e.startsWith("/")?`/${a}`:a,{modelBaseUri:a,manifestUri:a==="/"?`/${i}`:`${a}/${i}`}}async function Ks(e,t){const{manifestUri:n,modelBaseUri:r}=ec(e,t);let o=await Zs(n);return vr.loadWeights(o,r)}function jh(e,t,n=!1){const{width:r,height:o}=n?dn(t):t;return e.width=r,e.height=o,{width:r,height:o}}class We{constructor(e){this._name=e,this._params=void 0,this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(e){const{obj:t,objProp:n}=this.traversePropertyPath(e);return t[n]}reassignParamFromPath(e,t){const{obj:n,objProp:r}=this.traversePropertyPath(e);n[r].dispose(),n[r]=t}getParamList(){return this._paramMappings.map(({paramPath:e})=>({path:e,tensor:this.getParamFromPath(e)}))}getTrainableParams(){return this.getParamList().filter(e=>e.tensor instanceof $t)}getFrozenParams(){return this.getParamList().filter(e=>!(e.tensor instanceof $t))}variable(){this.getFrozenParams().forEach(({path:e,tensor:t})=>{this.reassignParamFromPath(e,t.variable())})}freeze(){this.getTrainableParams().forEach(({path:e,tensor:t})=>{const n=Xe(t.dataSync());t.dispose(),this.reassignParamFromPath(e,n)})}dispose(e=!0){this.getParamList().forEach(t=>{if(e&&t.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${t.path}`);t.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:e})=>Array.from(e.dataSync())).reduce((e,t)=>e.concat(t)))}async load(e){if(e instanceof Float32Array){this.extractWeights(e);return}await this.loadFromUri(e)}async loadFromUri(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const t=await Ks(e,this.getDefaultModelName());this.loadFromWeightMap(t)}async loadFromDisk(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:t}=se.getEnv(),{manifestUri:n,modelBaseUri:r}=ec(e,this.getDefaultModelName()),o=c=>Promise.all(c.map(p=>t(p).then(l=>l.buffer))),i=vr.weightsLoaderFactory(o),a=JSON.parse((await t(n)).toString()),s=await i(a,r);this.loadFromWeightMap(s)}loadFromWeightMap(e){const{paramMappings:t,params:n}=this.extractParamsFromWeigthMap(e);this._paramMappings=t,this._params=n}extractWeights(e){const{paramMappings:t,params:n}=this.extractParams(e);this._paramMappings=t,this._params=n}traversePropertyPath(e){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const t=e.split("/").reduce((o,i)=>{if(!o.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${e}`);return{obj:o.nextObj,objProp:i,nextObj:o.nextObj[i]}},{nextObj:this.params}),{obj:n,objProp:r}=t;if(!n||!r||!(n[r]instanceof te))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:n,objProp:r}}}function tt(e,t,n){return O(()=>{let r=Wr(e,t.depthwise_filter,t.pointwise_filter,n,"same");return r=N(r,t.bias),r})}function tc(e,t,n=!1){return O(()=>{const r=ie(n?N(ve(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):tt(e,t.conv0,[2,2])),o=tt(r,t.conv1,[1,1]),i=ie(N(r,o)),a=tt(i,t.conv2,[1,1]);return ie(N(r,N(o,a)))})}function ws(e,t,n=!1,r=!0){return O(()=>{const o=ie(n?N(ve(e,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):tt(e,t.conv0,r?[2,2]:[1,1])),i=tt(o,t.conv1,[1,1]),a=ie(N(o,i)),s=tt(a,t.conv2,[1,1]),c=ie(N(o,N(i,s))),p=tt(c,t.conv3,[1,1]);return ie(N(o,N(i,N(s,p))))})}function Zn(e,t,n="same",r=!1){return O(()=>{const o=N(ve(e,t.filters,[1,1],n),t.bias);return r?ie(o):o})}function Ye(e,t){Object.keys(e).forEach(n=>{t.some(r=>r.originalPath===n)||e[n].dispose()})}function Hr(e,t){return function(n,r,o,i){const a=xt(e(n*r*o*o),[o,o,n,r]),s=ce(e(r));return t.push({paramPath:`${i}/filters`},{paramPath:`${i}/bias`}),{filters:a,bias:s}}}function nc(e,t){return function(n,r,o){const i=wt(e(n*r),[n,r]),a=ce(e(r));return t.push({paramPath:`${o}/weights`},{paramPath:`${o}/bias`}),{weights:i,bias:a}}}class $l{constructor(e,t,n){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=n}}function Yr(e,t){return function(n,r,o){const i=xt(e(3*3*n),[3,3,n,1]),a=xt(e(n*r),[1,1,n,r]),s=ce(e(r));return t.push({paramPath:`${o}/depthwise_filter`},{paramPath:`${o}/pointwise_filter`},{paramPath:`${o}/bias`}),new $l(i,a,s)}}function Vr(e){return function(t){const n=e(`${t}/depthwise_filter`,4),r=e(`${t}/pointwise_filter`,4),o=e(`${t}/bias`,1);return new $l(n,r,o)}}function it(e,t){return function(n,r,o){const i=e[n];if(!Kn(i,r))throw new Error(`expected weightMap[${n}] to be a Tensor${r}D, instead have ${i}`);return t.push({originalPath:n,paramPath:o||n}),i}}function Ve(e){let t=e;function n(o){const i=t.slice(0,o);return t=t.slice(o),i}function r(){return t}return{extractWeights:n,getRemainingWeights:r}}function rc(e,t){const n=Hr(e,t),r=Yr(e,t);function o(a,s,c,p=!1){const l=p?n(a,s,3,`${c}/conv0`):r(a,s,`${c}/conv0`),h=r(s,s,`${c}/conv1`),d=r(s,s,`${c}/conv2`);return{conv0:l,conv1:h,conv2:d}}function i(a,s,c,p=!1){const{conv0:l,conv1:h,conv2:d}=o(a,s,c,p),g=r(s,s,`${c}/conv3`);return{conv0:l,conv1:h,conv2:d,conv3:g}}return{extractDenseBlock3Params:o,extractDenseBlock4Params:i}}function Gg(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ve(e),{extractDenseBlock4Params:o}=rc(n,t),i=o(3,32,"dense0",!0),a=o(32,64,"dense1"),s=o(64,128,"dense2"),c=o(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:a,dense2:s,dense3:c}}}function oc(e){return function(t){const n=e(`${t}/filters`,4),r=e(`${t}/bias`,1);return{filters:n,bias:r}}}function ic(e,t){const n=it(e,t),r=oc(n),o=Vr(n);function i(s,c=!1){const p=c?r(`${s}/conv0`):o(`${s}/conv0`),l=o(`${s}/conv1`),h=o(`${s}/conv2`);return{conv0:p,conv1:l,conv2:h}}function a(s,c=!1){const p=c?r(`${s}/conv0`):o(`${s}/conv0`),l=o(`${s}/conv1`),h=o(`${s}/conv2`),d=o(`${s}/conv3`);return{conv0:p,conv1:l,conv2:h,conv3:d}}return{extractDenseBlock3Params:i,extractDenseBlock4Params:a}}function Pg(e){const t=[],{extractDenseBlock4Params:n}=ic(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return Ye(e,t),{params:r,paramMappings:t}}class sc extends We{constructor(){super("FaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return O(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=ht(n,r).div(C(255));let i=ws(o,t.dense0,!0);return i=ws(i,t.dense1),i=ws(i,t.dense2),i=ws(i,t.dense3),i=mt(i,[7,7],[2,2],"valid"),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(e){return Pg(e)}extractParams(e){return Gg(e)}}function xs(e,t){return O(()=>N(V(e,t.weights),t.bias))}function qg(e,t,n){const r=[],{extractWeights:o,getRemainingWeights:i}=Ve(e),a=nc(o,r),s=a(t,n,"fc");if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:r,params:{fc:s}}}function zg(e){const t=[],n=it(e,t);function r(i){const a=n(`${i}/weights`,2),s=n(`${i}/bias`,1);return{weights:a,bias:s}}const o={fc:r("fc")};return Ye(e,t),{params:o,paramMappings:t}}function ac(e){const t={},n={};return Object.keys(e).forEach(r=>{const o=r.startsWith("fc")?n:t;o[r]=e[r]}),{featureExtractorMap:t,classifierMap:n}}class cc extends We{constructor(e,t){super(e);this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return O(()=>{const n=e instanceof Vt?this.faceFeatureExtractor.forwardInput(e):e;return xs(n.as2D(n.shape[0],-1),t.fc)})}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:n}=this.extractClassifierParams(e);this._params=t,this._paramMappings=n}extractClassifierParams(e){return qg(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:n}=ac(e);return this.faceFeatureExtractor.loadFromWeightMap(t),zg(n)}extractParams(e){const t=this.getClassifierChannelsIn(),n=this.getClassifierChannelsOut(),r=n*t+n,o=e.slice(0,e.length-r),i=e.slice(e.length-r);return this.faceFeatureExtractor.extractWeights(o),this.extractClassifierParams(i)}}const Hs=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class hn{constructor(e){if(e.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);Hs.forEach((t,n)=>{this[t]=e[n]})}asSortedArray(){return Hs.map(e=>({expression:e,probability:this[e]})).sort((e,t)=>t.probability-e.probability)}}class Ys extends cc{constructor(e=new sc){super("FaceExpressionNet",e)}forwardInput(e){return O(()=>Ln(this.runNet(e)))}async forward(e){return this.forwardInput(await ue(e))}async predictExpressions(e){const t=await ue(e),n=await this.forwardInput(t),r=await Promise.all(Ae(n).map(async i=>{const a=await i.data();return i.dispose(),a}));n.dispose();const o=r.map(i=>new hn(i));return t.isBatchInput?o:o[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function qs(e){return e.expressions instanceof hn}function io(e,t){const n={expressions:t};return Object.assign({},e,n)}function wT(e,t,n=.1,r){const o=Array.isArray(t)?t:[t];o.forEach(i=>{const a=i instanceof hn?i:qs(i)?i.expressions:void 0;if(!a)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const s=a.asSortedArray(),c=s.filter(h=>h.probability>n),p=It(i)?i.detection.box.bottomLeft:r||new J(0,0),l=new zr(c.map(h=>`${h.expression} (${Jn(h.probability)})`),p);l.draw(e)})}function pn(e){return It(e)&&e.landmarks instanceof st&&e.unshiftedLandmarks instanceof st&&e.alignedRect instanceof me}function Tn(e,t){const{box:n}=e.detection,r=t.shiftBy(n.x,n.y),o=r.align(),{imageDims:i}=e.detection,a=new me(e.detection.score,o.rescale(i.reverse()),i),s={landmarks:r,unshiftedLandmarks:t,alignedRect:a};return Object.assign({},e,s)}class Hg{constructor(e={}){const{drawLines:t=!0,drawPoints:n=!0,lineWidth:r,lineColor:o,pointSize:i,pointColor:a}=e;this.drawLines=t,this.drawPoints=n,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=o||"rgba(0, 255, 255, 1)",this.pointColor=a||"rgba(255, 0, 255, 1)"}}class Yg{constructor(e,t={}){this.faceLandmarks=e,this.options=new Hg(t)}draw(e){const t=Ge(e),{drawLines:n,drawPoints:r,lineWidth:o,lineColor:i,pointSize:a,pointColor:s}=this.options;if(n&&this.faceLandmarks instanceof lr&&(t.strokeStyle=i,t.lineWidth=o,tn(t,this.faceLandmarks.getJawOutline()),tn(t,this.faceLandmarks.getLeftEyeBrow()),tn(t,this.faceLandmarks.getRightEyeBrow()),tn(t,this.faceLandmarks.getNose()),tn(t,this.faceLandmarks.getLeftEye(),!0),tn(t,this.faceLandmarks.getRightEye(),!0),tn(t,this.faceLandmarks.getMouth(),!0)),r){t.strokeStyle=s,t.fillStyle=s;const c=p=>{t.beginPath(),t.arc(p.x,p.y,a,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(c)}}}function xT(e,t){const n=Array.isArray(t)?t:[t];n.forEach(r=>{const o=r instanceof st?r:pn(r)?r.landmarks:void 0;if(!o)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new Yg(o).draw(e)})}const _s={};_e(_s,{AnchorPosition:()=>Yt,DrawBox:()=>Bl,DrawBoxOptions:()=>jg,DrawFaceLandmarks:()=>Yg,DrawFaceLandmarksOptions:()=>Hg,DrawTextField:()=>zr,DrawTextFieldOptions:()=>Qa,drawContour:()=>tn,drawDetections:()=>bT,drawFaceExpressions:()=>wT,drawFaceLandmarks:()=>xT});function yT(e,t){const n=Hr(e,t),r=Yr(e,t);function o(a,s,c){const p=r(a,s,`${c}/separable_conv0`),l=r(s,s,`${c}/separable_conv1`),h=n(a,s,1,`${c}/expansion_conv`);return{separable_conv0:p,separable_conv1:l,expansion_conv:h}}function i(a,s){const c=r(a,a,`${s}/separable_conv0`),p=r(a,a,`${s}/separable_conv1`),l=r(a,a,`${s}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:l}}return{extractConvParams:n,extractSeparableConvParams:r,extractReductionBlockParams:o,extractMainBlockParams:i}}function Vg(e,t){const n=[],{extractWeights:r,getRemainingWeights:o}=Ve(e),{extractConvParams:i,extractSeparableConvParams:a,extractReductionBlockParams:s,extractMainBlockParams:c}=yT(r,n),p=i(3,32,3,"entry_flow/conv_in"),l=s(32,64,"entry_flow/reduction_block_0"),h=s(64,128,"entry_flow/reduction_block_1"),d={conv_in:p,reduction_block_0:l,reduction_block_1:h},g={};Ht(t,0,1).forEach(S=>{g[`main_block_${S}`]=c(128,`middle_flow/main_block_${S}`)});const x=s(128,256,"exit_flow/reduction_block"),w=a(256,512,"exit_flow/separable_conv"),L={reduction_block:x,separable_conv:w};if(o().length!==0)throw new Error(`weights remaing after extract: ${o().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:g,exit_flow:L}}}function LT(e,t){const n=it(e,t),r=oc(n),o=Vr(n);function i(s){const c=o(`${s}/separable_conv0`),p=o(`${s}/separable_conv1`),l=r(`${s}/expansion_conv`);return{separable_conv0:c,separable_conv1:p,expansion_conv:l}}function a(s){const c=o(`${s}/separable_conv0`),p=o(`${s}/separable_conv1`),l=o(`${s}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:l}}return{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:i,extractMainBlockParams:a}}function Kg(e,t){const n=[],{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:i,extractMainBlockParams:a}=LT(e,n),s=r("entry_flow/conv_in"),c=i("entry_flow/reduction_block_0"),p=i("entry_flow/reduction_block_1"),l={conv_in:s,reduction_block_0:c,reduction_block_1:p},h={};Ht(t,0,1).forEach(w=>{h[`main_block_${w}`]=a(`middle_flow/main_block_${w}`)});const d=i("exit_flow/reduction_block"),g=o("exit_flow/separable_conv"),x={reduction_block:d,separable_conv:g};return Ye(e,n),{params:{entry_flow:l,middle_flow:h,exit_flow:x},paramMappings:n}}function Jg(e,t,n){return N(ve(e,t.filters,n,"same"),t.bias)}function jl(e,t,n=!0){let r=n?ie(e):e;return r=tt(r,t.separable_conv0,[1,1]),r=tt(ie(r),t.separable_conv1,[1,1]),r=Re(r,[3,3],[2,2],"same"),r=N(r,Jg(e,t.expansion_conv,[2,2])),r}function vT(e,t){let n=tt(ie(e),t.separable_conv0,[1,1]);return n=tt(ie(n),t.separable_conv1,[1,1]),n=tt(ie(n),t.separable_conv2,[1,1]),n=N(n,e),n}class Xg extends We{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return O(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=ht(n,r).div(C(256));let i=ie(Jg(o,t.entry_flow.conv_in,[2,2]));return i=jl(i,t.entry_flow.reduction_block_0,!1),i=jl(i,t.entry_flow.reduction_block_1),Ht(this._numMainBlocks,0,1).forEach(a=>{i=vT(i,t.middle_flow[`main_block_${a}`])}),i=jl(i,t.exit_flow.reduction_block),i=ie(tt(i,t.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(e){return Kg(e,this._numMainBlocks)}extractParams(e){return Vg(e,this._numMainBlocks)}}function Zg(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ve(e),o=nc(n,t),i=o(512,1,"fc/age"),a=o(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:i,gender:a}}}}function Qg(e){const t=[],n=it(e,t);function r(i){const a=n(`${i}/weights`,2),s=n(`${i}/bias`,1);return{weights:a,bias:s}}const o={fc:{age:r("fc/age"),gender:r("fc/gender")}};return Ye(e,t),{params:o,paramMappings:t}}var Dt;(function(e){e.FEMALE="female",e.MALE="male"})(Dt||(Dt={}));class ta extends We{constructor(e=new Xg(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return O(()=>{const n=e instanceof Vt?this.faceFeatureExtractor.forwardInput(e):e,r=mt(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),o=xs(r,t.fc.age).as1D(),i=xs(r,t.fc.gender);return{age:o,gender:i}})}forwardInput(e){return O(()=>{const{age:t,gender:n}=this.runNet(e);return{age:t,gender:Ln(n)}})}async forward(e){return this.forwardInput(await ue(e))}async predictAgeAndGender(e){const t=await ue(e),n=await this.forwardInput(t),r=Ae(n.age),o=Ae(n.gender),i=r.map((s,c)=>({ageTensor:s,genderTensor:o[c]})),a=await Promise.all(i.map(async({ageTensor:s,genderTensor:c})=>{const p=(await s.data())[0],l=(await c.data())[0],h=l>.5,d=h?Dt.MALE:Dt.FEMALE,g=h?l:1-l;return s.dispose(),c.dispose(),{age:p,gender:d,genderProbability:g}}));return n.age.dispose(),n.gender.dispose(),t.isBatchInput?a:a[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:n}=this.extractClassifierParams(e);this._params=t,this._paramMappings=n}extractClassifierParams(e){return Zg(e)}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:n}=ac(e);return this.faceFeatureExtractor.loadFromWeightMap(t),Qg(n)}extractParams(e){const t=512*1+1+(512*2+2),n=e.slice(0,e.length-t),r=e.slice(e.length-t);return this.faceFeatureExtractor.extractWeights(n),this.extractClassifierParams(r)}}class pc extends cc{postProcess(e,t,n){const r=n.map(({width:i,height:a})=>{const s=t/Math.max(a,i);return{width:i*s,height:a*s}}),o=r.length;return O(()=>{const i=(l,h)=>Fe([qe([68],l),qe([68],h)],1).as2D(1,136).as1D(),a=(l,h)=>{const{width:d,height:g}=r[l];return h(d,g)?Math.abs(d-g)/2:0},s=l=>a(l,(h,d)=>h<d),c=l=>a(l,(h,d)=>d<h),p=e.mul(qe([o,136],t)).sub(Fe(Array.from(Array(o),(l,h)=>i(s(h),c(h))))).div(Fe(Array.from(Array(o),(l,h)=>i(r[h].width,r[h].height))));return p})}forwardInput(e){return O(()=>{const t=this.runNet(e);return this.postProcess(t,e.inputSize,e.inputDimensions.map(([n,r])=>({height:n,width:r})))})}async forward(e){return this.forwardInput(await ue(e))}async detectLandmarks(e){const t=await ue(e),n=O(()=>Ae(this.forwardInput(t))),r=await Promise.all(n.map(async(o,i)=>{const a=Array.from(await o.data()),s=a.filter((p,l)=>Ja(l)),c=a.filter((p,l)=>!Ja(l));return new lr(Array(68).fill(0).map((p,l)=>new J(s[l],c[l])),{height:t.getInputHeight(i),width:t.getInputWidth(i)})}));return n.forEach(o=>o.dispose()),t.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}}class sr extends pc{constructor(e=new sc){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function eb(e){const t=[],{extractDenseBlock3Params:n}=ic(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return Ye(e,t),{params:r,paramMappings:t}}function tb(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ve(e),{extractDenseBlock3Params:o}=rc(n,t),i=o(3,32,"dense0",!0),a=o(32,64,"dense1"),s=o(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:a,dense2:s}}}class nb extends We{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return O(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=ht(n,r).div(C(255));let i=tc(o,t.dense0,!0);return i=tc(i,t.dense1),i=tc(i,t.dense2),i=mt(i,[14,14],[2,2],"valid"),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(e){return eb(e)}extractParams(e){return tb(e)}}class zs extends pc{constructor(e=new nb){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class $h extends sr{}function rb(e,t){return N(v(e,t.weights),t.biases)}function Gl(e,t,n,r,o="same"){const{filters:i,bias:a}=t.conv;let s=ve(e,i,n,o);return s=N(s,a),s=rb(s,t.scale),r?ie(s):s}function ob(e,t){return Gl(e,t,[1,1],!0)}function Pl(e,t){return Gl(e,t,[1,1],!1)}function lc(e,t){return Gl(e,t,[2,2],!0,"valid")}function ST(e,t){function n(s,c,p){const l=e(s),h=l.length/(c*p*p);if(El(h))throw new Error(`depth has to be an integer: ${h}, weights.length: ${l.length}, numFilters: ${c}, filterSize: ${p}`);return O(()=>K(xt(l,[c,h,p,p]),[2,3,1,0]))}function r(s,c,p,l){const h=n(s,c,p),d=ce(e(c));return t.push({paramPath:`${l}/filters`},{paramPath:`${l}/bias`}),{filters:h,bias:d}}function o(s,c){const p=ce(e(s)),l=ce(e(s));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:p,biases:l}}function i(s,c,p,l){const h=r(s,c,p,`${l}/conv`),d=o(c,`${l}/scale`);return{conv:h,scale:d}}function a(s,c,p,l,h=!1){const d=i((h?.5:1)*s,c,p,`${l}/conv1`),g=i(s,c,p,`${l}/conv2`);return{conv1:d,conv2:g}}return{extractConvLayerParams:i,extractResidualLayerParams:a}}function 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c=o(3,32,3,"mobilenetv1/conv_0"),p=i(32,64,"mobilenetv1/conv_1"),l=i(64,128,"mobilenetv1/conv_2"),h=i(128,128,"mobilenetv1/conv_3"),d=i(128,256,"mobilenetv1/conv_4"),g=i(256,256,"mobilenetv1/conv_5"),x=i(256,512,"mobilenetv1/conv_6"),w=i(512,512,"mobilenetv1/conv_7"),L=i(512,512,"mobilenetv1/conv_8"),S=i(512,512,"mobilenetv1/conv_9"),I=i(512,512,"mobilenetv1/conv_10"),R=i(512,512,"mobilenetv1/conv_11"),A=i(512,1024,"mobilenetv1/conv_12"),E=i(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:p,conv_2:l,conv_3:h,conv_4:d,conv_5:g,conv_6:x,conv_7:w,conv_8:L,conv_9:S,conv_10:I,conv_11:R,conv_12:A,conv_13:E}}function s(){const 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I/(d+g-I)}function ET(e){const t=Ae(K(e,[1,0])),n=[k(t[2],t[0]),k(t[3],t[1])],r=[N(t[0],D(n[0],C(2))),N(t[1],D(n[1],C(2)))];return{sizes:n,centers:r}}function OT(e,t){const{sizes:n,centers:r}=ET(e),o=Ae(K(t,[1,0])),i=D(v(Te(D(o[2],C(5))),n[0]),C(2)),a=N(v(D(o[0],C(10)),n[0]),r[0]),s=D(v(Te(D(o[3],C(5))),n[1]),C(2)),c=N(v(D(o[1],C(10)),n[1]),r[1]);return K(Fe([k(a,i),k(c,s),N(a,i),N(c,s)]),[1,0])}function hb(e,t,n){return O(()=>{const r=e.shape[0];let o=OT(y(Gt(n.extra_dim,[r,1,1]),[-1,4]),y(e,[-1,4]));o=y(o,[r,o.shape[0]/r,4]);const i=ft(P(t,[0,0,1],[-1,-1,-1]));let a=P(i,[0,0,0],[-1,-1,1]);a=y(a,[r,a.shape[1]]);const s=Ae(o),c=Ae(a);return{boxes:s,scores:c}})}function Qn(e,t){return O(()=>{const n=e.shape[0],r=y(Zn(e,t.box_encoding_predictor),[n,-1,1,4]),o=y(Zn(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:o}})}function ub(e,t,n){return O(()=>{const r=Lt(e,n.conv_0,[1,1]),o=Lt(r,n.conv_1,[2,2]),i=Lt(o,n.conv_2,[1,1]),a=Lt(i,n.conv_3,[2,2]),s=Lt(a,n.conv_4,[1,1]),c=Lt(s,n.conv_5,[2,2]),p=Lt(c,n.conv_6,[1,1]),l=Lt(p,n.conv_7,[2,2]),h=Qn(t,n.box_predictor_0),d=Qn(e,n.box_predictor_1),g=Qn(o,n.box_predictor_2),x=Qn(a,n.box_predictor_3),w=Qn(c,n.box_predictor_4),L=Qn(l,n.box_predictor_5),S=Z([h.boxPredictionEncoding,d.boxPredictionEncoding,g.boxPredictionEncoding,x.boxPredictionEncoding,w.boxPredictionEncoding,L.boxPredictionEncoding],1),I=Z([h.classPrediction,d.classPrediction,g.classPrediction,x.classPrediction,w.classPrediction,L.classPrediction],1);return{boxPredictions:S,classPredictions:I}})}class lt{constructor({minConfidence:e,maxResults:t}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=e||.5,this._maxResults=t||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}class In extends We{constructor(){super("SsdMobilenetv1")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{const n=_(e.toBatchTensor(512,!1),"float32"),r=k(v(n,C(.007843137718737125)),C(1)),o=pb(r,t.mobilenetv1),{boxPredictions:i,classPredictions:a}=ub(o.out,o.conv11,t.prediction_layer);return hb(i,a,t.output_layer)})}async forward(e){return this.forwardInput(await ue(e))}async locateFaces(e,t={}){const{maxResults:n,minConfidence:r}=new lt(t),o=await ue(e),{boxes:i,scores:a}=this.forwardInput(o),s=i[0],c=a[0];for(let I=1;I<i.length;I++)i[I].dispose(),a[I].dispose();const p=Array.from(await c.data()),l=.5,h=lb(s,p,n,l,r),d=o.getReshapedInputDimensions(0),g=o.inputSize,x=g/d.width,w=g/d.height,L=s.arraySync(),S=h.map(I=>{const[R,A]=[Math.max(0,L[I][0]),Math.min(1,L[I][2])].map(M=>M*w),[E,F]=[Math.max(0,L[I][1]),Math.min(1,L[I][3])].map(M=>M*x);return new me(p[I],new pr(E,R,F-E,A-R),{height:o.getInputHeight(0),width:o.getInputWidth(0)})});return s.dispose(),c.dispose(),S}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(e){return cb(e)}extractParams(e){return ab(e)}}function yc(e){const t=new In;return t.extractWeights(e),t}function mh(e){return yc(e)}class fh extends In{}const db=.4,mb=[new J(.738768,.874946),new J(2.42204,2.65704),new J(4.30971,7.04493),new J(10.246,4.59428),new J(12.6868,11.8741)],fb=[new J(1.603231,2.094468),new J(6.041143,7.080126),new J(2.882459,3.518061),new J(4.266906,5.178857),new J(9.041765,10.66308)],gb=[117.001,114.697,97.404],bb="tiny_yolov2_model",wb="tiny_yolov2_separable_conv_model";const hc=e=>typeof e=="number";function Es(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hc(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hc(t.x)&&hc(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hc)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Kr(e){return O(()=>{const t=v(e,C(.10000000149011612));return N(ie(k(e,t)),t)})}function rn(e,t){return O(()=>{let n=He(e,[[0,0],[1,1],[1,1],[0,0]]);return n=ve(n,t.conv.filters,[1,1],"valid"),n=k(n,t.bn.sub),n=v(n,t.bn.truediv),n=N(n,t.conv.bias),Kr(n)})}function on(e,t){return O(()=>{let n=He(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Wr(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=N(n,t.bias),Kr(n)})}function kT(e,t){const n=Hr(e,t);function r(a,s){const c=ce(e(a)),p=ce(e(a));return t.push({paramPath:`${s}/sub`},{paramPath:`${s}/truediv`}),{sub:c,truediv:p}}function o(a,s,c){const p=n(a,s,3,`${c}/conv`),l=r(s,`${c}/bn`);return{conv:p,bn:l}}const i=Yr(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:o,extractSeparableConvParams:i}}function xb(e,t,n,r){const{extractWeights:o,getRemainingWeights:i}=Ve(e),a=[],{extractConvParams:s,extractConvWithBatchNormParams:c,extractSeparableConvParams:p}=kT(o,a);let l;if(t.withSeparableConvs){const[h,d,g,x,w,L,S,I,R]=r,A=t.isFirstLayerConv2d?s(h,d,3,"conv0"):p(h,d,"conv0"),E=p(d,g,"conv1"),F=p(g,x,"conv2"),M=p(x,w,"conv3"),j=p(w,L,"conv4"),$=p(L,S,"conv5"),U=I?p(S,I,"conv6"):void 0,Q=R?p(I,R,"conv7"):void 0,ne=s(R||I||S,5*n,1,"conv8");l={conv0:A,conv1:E,conv2:F,conv3:M,conv4:j,conv5:$,conv6:U,conv7:Q,conv8:ne}}else{const[h,d,g,x,w,L,S,I,R]=r,A=c(h,d,"conv0"),E=c(d,g,"conv1"),F=c(g,x,"conv2"),M=c(x,w,"conv3"),j=c(w,L,"conv4"),$=c(L,S,"conv5"),U=c(S,I,"conv6"),Q=c(I,R,"conv7"),ne=s(R,5*n,1,"conv8");l={conv0:A,conv1:E,conv2:F,conv3:M,conv4:j,conv5:$,conv6:U,conv7:Q,conv8:ne}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:l,paramMappings:a}}function DT(e,t){const n=it(e,t);function r(s){const c=n(`${s}/sub`,1),p=n(`${s}/truediv`,1);return{sub:c,truediv:p}}function o(s){const c=n(`${s}/filters`,4),p=n(`${s}/bias`,1);return{filters:c,bias:p}}function i(s){const c=o(`${s}/conv`),p=r(`${s}/bn`);return{conv:c,bn:p}}const a=Vr(n);return{extractConvParams:o,extractConvWithBatchNormParams:i,extractSeparableConvParams:a}}function yb(e,t){const n=[],{extractConvParams:r,extractConvWithBatchNormParams:o,extractSeparableConvParams:i}=DT(e,n);let a;if(t.withSeparableConvs){const s=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:s>7?i("conv6"):void 0,conv7:s>8?i("conv7"):void 0,conv8:r("conv8")}}else a={conv0:o("conv0"),conv1:o("conv1"),conv2:o("conv2"),conv3:o("conv3"),conv4:o("conv4"),conv5:o("conv5"),conv6:o("conv6"),conv7:o("conv7"),conv8:r("conv8")};return Ye(e,n),{params:a,paramMappings:n}}var Os;(function(e){e[e.XS=224]="XS",e[e.SM=320]="SM",e[e.MD=416]="MD",e[e.LG=608]="LG"})(Os||(Os={}));class kt{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}class Jr extends We{constructor(e){super("TinyYolov2");Es(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,t){let n=rn(e,t.conv0);return n=Re(n,[2,2],[2,2],"same"),n=rn(n,t.conv1),n=Re(n,[2,2],[2,2],"same"),n=rn(n,t.conv2),n=Re(n,[2,2],[2,2],"same"),n=rn(n,t.conv3),n=Re(n,[2,2],[2,2],"same"),n=rn(n,t.conv4),n=Re(n,[2,2],[2,2],"same"),n=rn(n,t.conv5),n=Re(n,[2,2],[1,1],"same"),n=rn(n,t.conv6),n=rn(n,t.conv7),Zn(n,t.conv8,"valid",!1)}runMobilenet(e,t){let n=this.config.isFirstLayerConv2d?Kr(Zn(e,t.conv0,"valid",!1)):on(e,t.conv0);return n=Re(n,[2,2],[2,2],"same"),n=on(n,t.conv1),n=Re(n,[2,2],[2,2],"same"),n=on(n,t.conv2),n=Re(n,[2,2],[2,2],"same"),n=on(n,t.conv3),n=Re(n,[2,2],[2,2],"same"),n=on(n,t.conv4),n=Re(n,[2,2],[2,2],"same"),n=on(n,t.conv5),n=Re(n,[2,2],[1,1],"same"),n=t.conv6?on(n,t.conv6):n,n=t.conv7?on(n,t.conv7):n,Zn(n,t.conv8,"valid",!1)}forwardInput(e,t){const{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=_(e.toBatchTensor(t,!1),"float32");return r=this.config.meanRgb?ht(r,this.config.meanRgb):r,r=r.div(C(256)),this.config.withSeparableConvs?this.runMobilenet(r,n):this.runTinyYolov2(r,n)})}async forward(e,t){return await this.forwardInput(await ue(e),t)}async detect(e,t={}){const{inputSize:n,scoreThreshold:r}=new kt(t),o=await ue(e),i=await this.forwardInput(o,n),a=O(()=>Ae(i)[0].expandDims()),s={width:o.getInputWidth(0),height:o.getInputHeight(0)},c=await this.extractBoxes(a,o.getReshapedInputDimensions(0),r);i.dispose(),a.dispose();const p=c.map(w=>w.box),l=c.map(w=>w.score),h=c.map(w=>w.classScore),d=c.map(w=>this.config.classes[w.label]),g=Fs(p.map(w=>w.rescale(n)),l,this.config.iouThreshold,!0),x=g.map(w=>new _n(l[w],h[w],d[w],p[w],s));return x}getDefaultModelName(){return""}extractParamsFromWeigthMap(e){return yb(e,this.config)}extractParams(e){const t=this.config.filterSizes||Jr.DEFAULT_FILTER_SIZES,n=t?t.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return xb(e,this.config,this.boxEncodingSize,t)}async extractBoxes(e,t,n){const{width:r,height:o}=t,i=Math.max(r,o),a=i/r,s=i/o,c=e.shape[1],p=this.config.anchors.length,[l,h,d]=O(()=>{const L=e.reshape([c,c,p,this.boxEncodingSize]),S=L.slice([0,0,0,0],[c,c,p,4]),I=L.slice([0,0,0,4],[c,c,p,1]),R=this.withClassScores?Ln(L.slice([0,0,0,5],[c,c,p,this.config.classes.length]),3):C(0);return[S,I,R]}),g=[],x=await h.array(),w=await l.array();for(let L=0;L<c;L++)for(let S=0;S<c;S++)for(let I=0;I<p;I++){const R=or(x[L][S][I][0]);if(!n||R>n){const A=(S+or(w[L][S][I][0]))/c*a,E=(L+or(w[L][S][I][1]))/c*s,F=Math.exp(w[L][S][I][2])*this.config.anchors[I].x/c*a,M=Math.exp(w[L][S][I][3])*this.config.anchors[I].y/c*s,j=A-F/2,$=E-M/2,U={row:L,col:S,anchor:I},{classScore:Q,label:ne}=this.withClassScores?await this.extractPredictedClass(d,U):{classScore:1,label:0};g.push(As({box:new hr(j,$,j+F,$+M),score:R,classScore:R*Q,label:ne},U))}}return l.dispose(),h.dispose(),d.dispose(),g}async extractPredictedClass(e,t){const{row:n,col:r,anchor:o}=t,i=await e.array();return Array(this.config.classes.length).fill(0).map((a,s)=>i[n][r][o][s]).map((a,s)=>({classScore:a,label:s})).reduce((a,s)=>a.classScore>s.classScore?a:s)}}Jr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class nr extends Jr{constructor(e=!0){const t=Object.assign({},{withSeparableConvs:e,iouThreshold:db,classes:["face"]},e?{anchors:fb,meanRgb:gb}:{anchors:mb,withClassScores:!0});super(t)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(e,t){const n=await this.detect(e,t);return n.map(r=>new me(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?wb:bb}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}function uh(e,t=!0){const n=new nr(t);return n.extractWeights(e),n}class ks extends kt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class ut{async then(e){return e(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}async function er(e,t,n,r,o=({alignedRect:i})=>i){const i=e.map(c=>pn(c)?o(c):c.detection),a=r||(t instanceof te?await Nn(t,i):await An(t,i)),s=await n(a);return a.forEach(c=>c instanceof te&&c.dispose()),s}async function Xr(e,t,n,r,o){return er([e],t,async i=>n(i[0]),r,o)}const Lb=.4,vb=[new J(1.603231,2.094468),new J(6.041143,7.080126),new J(2.882459,3.518061),new J(4.266906,5.178857),new J(9.041765,10.66308)],Sb=[117.001,114.697,97.404];class rr extends Jr{constructor(){const e={withSeparableConvs:!0,iouThreshold:Lb,classes:["face"],anchors:vb,meanRgb:Sb,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(e)}get anchors(){return this.config.anchors}async locateFaces(e,t){const n=await this.detect(e,t);return n.map(r=>new me(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}const oe={ssdMobilenetv1:new In,tinyFaceDetector:new rr,tinyYolov2:new nr,faceLandmark68Net:new sr,faceLandmark68TinyNet:new zs,faceRecognitionNet:new ir,faceExpressionNet:new Ys,ageGenderNet:new ta},vc=(e,t)=>oe.ssdMobilenetv1.locateFaces(e,t),wh=(e,t)=>oe.tinyFaceDetector.locateFaces(e,t),xh=(e,t)=>oe.tinyYolov2.locateFaces(e,t),Sc=e=>oe.faceLandmark68Net.detectLandmarks(e),yh=e=>oe.faceLandmark68TinyNet.detectLandmarks(e),Lh=e=>oe.faceRecognitionNet.computeFaceDescriptor(e),vh=e=>oe.faceExpressionNet.predictExpressions(e),Sh=e=>oe.ageGenderNet.predictAgeAndGender(e),Ic=e=>oe.ssdMobilenetv1.load(e),Ih=e=>oe.tinyFaceDetector.load(e),Th=e=>oe.tinyYolov2.load(e),Ah=e=>oe.faceLandmark68Net.load(e),Nh=e=>oe.faceLandmark68TinyNet.load(e),Rh=e=>oe.faceRecognitionNet.load(e),_h=e=>oe.faceExpressionNet.load(e),Ch=e=>oe.ageGenderNet.load(e),Eh=Ic,Oh=vc,kh=Sc;class Ib extends ut{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.extractedFaces=n}}class Ss extends Ib{async run(){const e=await this.parentTask,t=await er(e,this.input,async n=>await Promise.all(n.map(r=>oe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return e.map((n,r)=>io(n,t[r]))}withAgeAndGender(){return new Ls(this,this.input)}}class Is extends Ib{async run(){const e=await this.parentTask;if(!e)return;const t=await Xr(e,this.input,n=>oe.faceExpressionNet.predictExpressions(n),this.extractedFaces);return io(e,t)}withAgeAndGender(){return new vs(this,this.input)}}class eo extends Ss{withAgeAndGender(){return new Zr(this,this.input)}withFaceDescriptors(){return new an(this,this.input)}}class to extends Is{withAgeAndGender(){return new Qr(this,this.input)}withFaceDescriptor(){return new cn(this,this.input)}}class Tb extends ut{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.extractedFaces=n}}class Ls extends Tb{async run(){const e=await this.parentTask,t=await er(e,this.input,async n=>await Promise.all(n.map(r=>oe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return e.map((n,r)=>{const{age:o,gender:i,genderProbability:a}=t[r];return ao(oo(n,i,a),o)})}withFaceExpressions(){return new Ss(this,this.input)}}class vs extends Tb{async run(){const e=await this.parentTask;if(!e)return;const{age:t,gender:n,genderProbability:r}=await Xr(e,this.input,o=>oe.ageGenderNet.predictAgeAndGender(o),this.extractedFaces);return ao(oo(e,n,r),t)}withFaceExpressions(){return new Is(this,this.input)}}class Zr extends Ls{withFaceExpressions(){return new eo(this,this.input)}withFaceDescriptors(){return new an(this,this.input)}}class Qr extends vs{withFaceExpressions(){return new to(this,this.input)}withFaceDescriptor(){return new cn(this,this.input)}}class Ps extends ut{constructor(e,t){super();this.parentTask=e,this.input=t}}class an extends Ps{async run(){const e=await this.parentTask,t=await er(e,this.input,n=>Promise.all(n.map(r=>oe.faceRecognitionNet.computeFaceDescriptor(r))),null,n=>n.landmarks.align(null,{useDlibAlignment:!0}));return t.map((n,r)=>so(e[r],n))}withFaceExpressions(){return new eo(this,this.input)}withAgeAndGender(){return new Zr(this,this.input)}}class cn extends Ps{async run(){const e=await this.parentTask;if(!e)return;const t=await Xr(e,this.input,n=>oe.faceRecognitionNet.computeFaceDescriptor(n),null,n=>n.landmarks.align(null,{useDlibAlignment:!0}));return so(e,t)}withFaceExpressions(){return new to(this,this.input)}withAgeAndGender(){return new Qr(this,this.input)}}class $s extends ut{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?oe.faceLandmark68TinyNet:oe.faceLandmark68Net}}class js extends $s{async run(){const e=await this.parentTask,t=e.map(o=>o.detection),n=this.input instanceof te?await Nn(this.input,t):await An(this.input,t),r=await Promise.all(n.map(o=>this.landmarkNet.detectLandmarks(o)));return n.forEach(o=>o instanceof te&&o.dispose()),e.map((o,i)=>Tn(o,r[i]))}withFaceExpressions(){return new eo(this,this.input)}withAgeAndGender(){return new Zr(this,this.input)}withFaceDescriptors(){return new an(this,this.input)}}class Gs extends $s{async run(){const e=await this.parentTask;if(!e)return;const{detection:t}=e,n=this.input instanceof te?await Nn(this.input,[t]):await An(this.input,[t]),r=await this.landmarkNet.detectLandmarks(n[0]);return n.forEach(o=>o instanceof te&&o.dispose()),Tn(e,r)}withFaceExpressions(){return new to(this,this.input)}withAgeAndGender(){return new Qr(this,this.input)}withFaceDescriptor(){return new cn(this,this.input)}}class Ws extends ut{constructor(e,t=new lt){super();this.input=e,this.options=t}}class ro extends Ws{async run(){const{input:e,options:t}=this,n=t instanceof ks?r=>oe.tinyFaceDetector.locateFaces(r,t):t instanceof lt?r=>oe.ssdMobilenetv1.locateFaces(r,t):t instanceof kt?r=>oe.tinyYolov2.locateFaces(r,t):null;if(!n)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return n(e)}runAndExtendWithFaceDetections(){return new Promise(async e=>{const t=await this.run();return e(t.map(n=>ln({},n)))})}withFaceLandmarks(e=!1){return new js(this.runAndExtendWithFaceDetections(),this.input,e)}withFaceExpressions(){return new Ss(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ls(this.runAndExtendWithFaceDetections(),this.input)}}class Bs extends Ws{async run(){const e=await new ro(this.input,this.options);let t=e[0];return e.forEach(n=>{n.score>t.score&&(t=n)}),t}runAndExtendWithFaceDetection(){return new Promise(async e=>{const t=await this.run();return e(t?ln({},t):void 0)})}withFaceLandmarks(e=!1){return new Gs(this.runAndExtendWithFaceDetection(),this.input,e)}withFaceExpressions(){return new Is(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new vs(this.runAndExtendWithFaceDetection(),this.input)}}function Dh(e,t=new lt){return new Bs(e,t)}function no(e,t=new lt){return new ro(e,t)}async function Tc(e,t){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await no(e,new lt(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Fh(e,t={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await no(e,new kt(t)).withFaceLandmarks().withFaceDescriptors()}const Mh=Tc;function Vs(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((o,i)=>o-r[i]).reduce((o,i)=>o+Math.pow(i,2),0))}class Ac{constructor(e,t=.6){this._distanceThreshold=t;const n=Array.isArray(e)?e:[e];if(!n.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1;const o=()=>`person ${r++}`;this._labeledDescriptors=n.map(i=>{if(i instanceof fn)return i;if(i instanceof Float32Array)return new fn(o(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new fn(o(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(e,t){return t.map(n=>Vs(n,e)).reduce((n,r)=>n+r,0)/(t.length||1)}matchDescriptor(e){return this.labeledDescriptors.map(({descriptors:t,label:n})=>new lo(n,this.computeMeanDistance(e,t))).reduce((t,n)=>t.distance<n.distance?t:n)}findBestMatch(e){const t=this.matchDescriptor(e);return t.distance<this.distanceThreshold?t:new lo("unknown",t.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(e=>e.toJSON())}}static fromJSON(e){const t=e.labeledDescriptors.map(n=>fn.fromJSON(n));return new Ac(t,e.distanceThreshold)}}function dh(e){const t=new rr;return t.extractWeights(e),t}function Lc(e,t){const{width:n,height:r}=new Ke(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(o=>Lc(o,{width:n,height:r}));if(pn(e)){const o=e.detection.forSize(n,r),i=e.unshiftedLandmarks.forSize(o.box.width,o.box.height);return Tn(ln(e,o),i)}return It(e)?ln(e,e.detection.forSize(n,r)):e instanceof st||e instanceof me?e.forSize(n,r):e}var lh="0.5.2";return Hh();})();
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/** @license See the LICENSE file. */
+var faceapi=(()=>{var Ge=Object.defineProperty,Yn=Object.prototype.hasOwnProperty,mr=(t,e)=>()=>(e||(e={exports:{}},t(e.exports,e)),e.exports),fr=t=>Ge(t,"__esModule",{value:!0}),He=(t,e)=>{fr(t);for(var o in e)Ge(t,o,{get:e[o],enumerable:!0})},Vn=(t,e)=>{if(fr(t),typeof e=="object"||typeof e=="function")for(let o in e)!Yn.call(t,o)&&o!=="default"&&Ge(t,o,{get:()=>e[o],enumerable:!0});return t},x=t=>t&&t.__esModule?t:Vn(Ge({},"default",{value:t,enumerable:!0}),t);var lr=mr((Jn,dr)=>{He(Jn,{isNodejs:()=>Xn});function Xn(){return typeof global=="object"&&!0&&typeof dr!="undefined"&&typeof process!="undefined"&&!!process.version}});var Zr=mr(qn=>{He(qn,{AgeGenderNet:()=>vo,BoundingBox:()=>se,Box:()=>T,ComposableTask:()=>Z,ComputeAllFaceDescriptorsTask:()=>Ft,ComputeFaceDescriptorsTaskBase:()=>ao,ComputeSingleFaceDescriptorTask:()=>Mt,DetectAllFaceLandmarksTask:()=>no,DetectAllFacesTask:()=>Te,DetectFaceLandmarksTaskBase:()=>ro,DetectFacesTaskBase:()=>eo,DetectSingleFaceLandmarksTask:()=>so,DetectSingleFaceTask:()=>oo,Dimensions:()=>B,FACE_EXPRESSION_LABELS:()=>po,FaceDetection:()=>D,FaceDetectionNet:()=>_r,FaceExpressionNet:()=>mo,FaceExpressions:()=>Pt,FaceLandmark68Net:()=>te,FaceLandmark68TinyNet:()=>co,FaceLandmarkNet:()=>Hr,FaceLandmarks:()=>G,FaceLandmarks5:()=>qr,FaceLandmarks68:()=>ne,FaceMatch:()=>Re,FaceMatcher:()=>Vo,FaceRecognitionNet:()=>Qt,Gender:()=>ct,LabeledBox:()=>Ce,LabeledFaceDescriptors:()=>Ct,NetInput:()=>lt,NeuralNetwork:()=>R,ObjectDetection:()=>zt,Point:()=>w,PredictedBox:()=>Xr,Rect:()=>re,SsdMobilenetv1:()=>Bt,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>Zt,TinyFaceDetectorOptions:()=>qe,TinyYolov2:()=>qt,TinyYolov2Options:()=>it,TinyYolov2SizeType:()=>Xe,allFaces:()=>$r,allFacesSsdMobilenetv1:()=>Yo,allFacesTinyYolov2:()=>Ir,awaitMediaLoaded:()=>wo,bufferToImage:()=>xo,computeFaceDescriptor:()=>Lr,createCanvas:()=>Ot,createCanvasFromMedia:()=>oe,createFaceDetectionNet:()=>vr,createFaceRecognitionNet:()=>Gr,createSsdMobilenetv1:()=>$o,createTinyFaceDetector:()=>wr,createTinyYolov2:()=>xr,detectAllFaces:()=>Me,detectFaceLandmarks:()=>Go,detectFaceLandmarksTiny:()=>Tr,detectLandmarks:()=>Br,detectSingleFace:()=>Ur,draw:()=>Ye,env:()=>F,euclideanDistance:()=>fo,extendWithAge:()=>De,extendWithFaceDescriptor:()=>Ee,extendWithFaceDetection:()=>Lt,extendWithFaceExpressions:()=>Pe,extendWithFaceLandmarks:()=>Ut,extendWithGender:()=>Le,extractFaceTensors:()=>$t,extractFaces:()=>It,fetchImage:()=>Jr,fetchJson:()=>go,fetchNetWeights:()=>Vr,fetchOrThrow:()=>kt,getContext2dOrThrow:()=>A,getMediaDimensions:()=>Dt,imageTensorToCanvas:()=>uo,imageToSquare:()=>ho,inverseSigmoid:()=>yr,iou:()=>to,isMediaElement:()=>ke,isMediaLoaded:()=>ee,isWithAge:()=>zr,isWithFaceDetection:()=>ot,isWithFaceExpressions:()=>io,isWithFaceLandmarks:()=>Tt,isWithGender:()=>Or,loadAgeGenderModel:()=>jr,loadFaceDetectionModel:()=>Nr,loadFaceExpressionModel:()=>Sr,loadFaceLandmarkModel:()=>Cr,loadFaceLandmarkTinyModel:()=>Rr,loadFaceRecognitionModel:()=>Ar,loadSsdMobilenetv1Model:()=>Ho,loadTinyFaceDetectorModel:()=>Dr,loadTinyYolov2Model:()=>kr,loadWeightMap:()=>lo,locateFaces:()=>Wr,matchDimensions:()=>Yr,minBbox:()=>Qe,nets:()=>y,nonMaxSuppression:()=>Ke,normalize:()=>q,padToSquare:()=>Ze,predictAgeAndGender:()=>Er,recognizeFaceExpressions:()=>Pr,resizeResults:()=>Oo,resolveInput:()=>Et,shuffleArray:()=>br,sigmoid:()=>Kt,ssdMobilenetv1:()=>zo,tf:()=>hr,tinyFaceDetector:()=>Fr,tinyYolov2:()=>Mr,toNetInput:()=>M,utils:()=>Ve,validateConfig:()=>Je,version:()=>gr});const hr=x(require("@tensorflow/tfjs")),Zn=typeof process!="undefined"?process.version:!1,Kn=typeof navigator!="undefined"?navigator.userAgent:!1,gr={faceapi:ur,node:Zn,browser:Kn}});function ht(t,e,o=!1){if(t.beginPath(),e.slice(1).forEach(({x:r,y:n},s)=>{const i=e[s];t.moveTo(i.x,i.y),t.lineTo(r,n)}),o){const r=e[e.length-1],n=e[0];if(!r||!n)return;t.moveTo(r.x,r.y),t.lineTo(n.x,n.y)}t.stroke()}class B{constructor(t,e){if(!rt(t)||!rt(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new B(1/this.width,1/this.height)}}const Ve={};He(Ve,{computeReshapedDimensions:()=>qo,getCenterPoint:()=>Yt,isDimensions:()=>bo,isEven:()=>_o,isFloat:()=>Xo,isTensor:()=>Gt,isTensor1D:()=>Qn,isTensor2D:()=>Jo,isTensor3D:()=>ut,isTensor4D:()=>H,isValidNumber:()=>rt,isValidProbablitiy:()=>ae,range:()=>pt,round:()=>Ht});const Kr=x(require("@tensorflow/tfjs"));function Gt(t,e){return t instanceof Kr.Tensor&&t.shape.length===e}function Qn(t){return Gt(t,1)}function Jo(t){return Gt(t,2)}function ut(t){return Gt(t,3)}function H(t){return Gt(t,4)}function Xo(t){return t%1!==0}function _o(t){return t%2===0}function Ht(t,e=2){const o=Math.pow(10,e);return Math.floor(t*o)/o}function bo(t){return t&&t.width&&t.height}function qo({width:t,height:e},o){const r=o/Math.max(e,t);return new B(Math.round(t*r),Math.round(e*r))}function Yt(t){return t.reduce((e,o)=>e.add(o),new w(0,0)).div(new w(t.length,t.length))}function pt(t,e,o){return Array(t).fill(0).map((r,n)=>e+n*o)}function rt(t){return!!t&&t!==Infinity&&t!==-Infinity&&!isNaN(t)||t===0}function ae(t){return rt(t)&&0<=t&&t<=1}class w{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new w(this.x+t.x,this.y+t.y)}sub(t){return new w(this.x-t.x,this.y-t.y)}mul(t){return new w(this.x*t.x,this.y*t.y)}div(t){return new w(this.x/t.x,this.y/t.y)}abs(){return new w(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new w(Math.floor(this.x),Math.floor(this.y))}}class T{constructor(t,e=!0){const o=t||{},r=[o.left,o.top,o.right,o.bottom].every(rt),n=[o.x,o.y,o.width,o.height].every(rt);if(!n&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(o)}`);const[s,i,a,c]=n?[o.x,o.y,o.width,o.height]:[o.left,o.top,o.right-o.left,o.bottom-o.top];T.assertIsValidBox({x:s,y:i,width:a,height:c},"Box.constructor",e),this._x=s,this._y=i,this._width=a,this._height=c}static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(rt)}static assertIsValidBox(t,e,o=!1){if(!T.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!o&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new w(this.left,this.top)}get topRight(){return new w(this.right,this.top)}get bottomLeft(){return new w(this.left,this.bottom)}get bottomRight(){return new w(this.right,this.bottom)}round(){const[t,e,o,r]=[this.x,this.y,this.width,this.height].map(n=>Math.round(n));return new T({x:t,y:e,width:o,height:r})}floor(){const[t,e,o,r]=[this.x,this.y,this.width,this.height].map(n=>Math.floor(n));return new T({x:t,y:e,width:o,height:r})}toSquare(){let{x:t,y:e,width:o,height:r}=this;const n=Math.abs(o-r);return o<r&&(t-=n/2,o+=n),r<o&&(e-=n/2,r+=n),new T({x:t,y:e,width:o,height:r})}rescale(t){const e=bo(t)?t.width:t,o=bo(t)?t.height:t;return new T({x:this.x*e,y:this.y*o,width:this.width*e,height:this.height*o})}pad(t,e){let[o,r,n,s]=[this.x-t/2,this.y-e/2,this.width+t,this.height+e];return new T({x:o,y:r,width:n,height:s})}clipAtImageBorders(t,e){const{x:o,y:r,right:n,bottom:s}=this,i=Math.max(o,0),a=Math.max(r,0),c=n-i,p=s-a,m=Math.min(c,t-i),f=Math.min(p,e-a);return new T({x:i,y:a,width:m,height:f}).floor()}shift(t,e){const{width:o,height:r}=this,n=this.x+t,s=this.y+e;return new T({x:n,y:s,width:o,height:r})}padAtBorders(t,e){const o=this.width+1,r=this.height+1;let n=1,s=1,i=o,a=r,c=this.left,p=this.top,m=this.right,f=this.bottom;return m>e&&(i=-m+e+o,m=e),f>t&&(a=-f+t+r,f=t),c<1&&(a=2-c,c=1),p<1&&(a=2-p,p=1),{dy:s,edy:a,dx:n,edx:i,y:p,ey:f,x:c,ex:m,w:o,h:r}}calibrate(t){return new T({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}}class se extends T{constructor(t,e,o,r,n=!1){super({left:t,top:e,right:o,bottom:r},n)}}class zt{constructor(t,e,o,r,n){this._imageDims=new B(n.width,n.height),this._score=t,this._classScore=e,this._className=o,this._box=new T(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new T(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new zt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}}class D extends zt{constructor(t,e,o){super(t,t,"",e,o)}forSize(t,e){const{score:o,relativeBox:r,imageDims:n}=super.forSize(t,e);return new D(o,r,n)}}function to(t,e,o=!0){const r=Math.max(0,Math.min(t.right,e.right)-Math.max(t.left,e.left)),n=Math.max(0,Math.min(t.bottom,e.bottom)-Math.max(t.top,e.top)),s=r*n;return o?s/(t.area+e.area-s):s/Math.min(t.area,e.area)}function Qe(t){const e=t.map(a=>a.x),o=t.map(a=>a.y),r=e.reduce((a,c)=>c<a?c:a,Infinity),n=o.reduce((a,c)=>c<a?c:a,Infinity),s=e.reduce((a,c)=>a<c?c:a,0),i=o.reduce((a,c)=>a<c?c:a,0);return new se(r,n,s,i)}function Ke(t,e,o,r=!0){let n=e.map((i,a)=>({score:i,boxIndex:a})).sort((i,a)=>i.score-a.score).map(i=>i.boxIndex);const s=[];for(;n.length>0;){const i=n.pop();s.push(i);const a=n,c=[];for(let p=0;p<a.length;p++){const m=a[p],f=t[i],d=t[m];c.push(to(f,d,r))}n=n.filter((p,m)=>c[m]<=o)}return s}const mt=x(require("@tensorflow/tfjs"));function q(t,e){return mt.tidy(()=>{const[o,r,n]=e,s=mt.fill([...t.shape.slice(0,3),1],o),i=mt.fill([...t.shape.slice(0,3),1],r),a=mt.fill([...t.shape.slice(0,3),1],n),c=mt.concat([s,i,a],3);return mt.sub(t,c)})}const Rt=x(require("@tensorflow/tfjs"));function Ze(t,e=!1){return Rt.tidy(()=>{const[o,r]=t.shape.slice(1);if(o===r)return t;const n=Math.abs(o-r),s=Math.round(n*(e?.5:1)),i=o>r?2:1,a=d=>{const h=t.shape.slice();return h[i]=d,Rt.fill(h,0)},c=a(s),p=n-c.shape[i],m=e&&p?a(p):null,f=[m,t,c].filter(d=>!!d).map(d=>Rt.cast(d,"float32"));return Rt.concat(f,i)})}function br(t){const e=t.slice();for(let o=e.length-1;o>0;o--){const r=Math.floor(Math.random()*(o+1)),n=e[o];e[o]=e[r],e[r]=n}return e}function Kt(t){return 1/(1+Math.exp(-t))}function yr(t){return Math.log(t/(1-t))}class re extends T{constructor(t,e,o,r,n=!1){super({x:t,y:e,width:o,height:r},n)}}const ts=.5,es=.43,os=.45;class G{constructor(t,e,o=new w(0,0)){const{width:r,height:n}=e;this._imgDims=new B(r,n),this._shift=o,this._positions=t.map(s=>s.mul(new w(r,n)).add(o))}get shift(){return new w(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new w(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new w(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){const n=t instanceof D?t.box.floor():new T(t);return this.shiftBy(n.x,n.y).align(null,e)}const{useDlibAlignment:o,minBoxPadding:r}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},e);return o?this.alignDlib():this.alignMinBbox(r)}alignDlib(){const t=this.getRefPointsForAlignment(),[e,o,r]=t,n=m=>r.sub(m).magnitude(),s=(n(e)+n(o))/2,i=Math.floor(s/os),a=Yt(t),c=Math.floor(Math.max(0,a.x-ts*i)),p=Math.floor(Math.max(0,a.y-es*i));return new re(c,p,Math.min(i,this.imageWidth+c),Math.min(i,this.imageHeight+p))}alignMinBbox(t){const e=Qe(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class qr extends G{getRefPointsForAlignment(){const t=this.positions;return[t[0],t[1],Yt([t[3],t[4]])]}}class ne extends G{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Yt)}}class Re{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Ht(this.distance)})`:""}`}}class Ce extends T{constructor(t,e){super(t);this._label=e}static assertIsValidLabeledBox(t,e){if(T.assertIsValidBox(t,e),!rt(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}get label(){return this._label}}class Ct{constructor(t,e){if(!(typeof t=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(o=>!(o instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){const e=t.descriptors.map(o=>new Float32Array(o));return new Ct(t.label,e)}}class Xr extends Ce{constructor(t,e,o,r){super(t,e);this._score=o,this._classScore=r}static assertIsValidPredictedBox(t,e){if(Ce.assertIsValidLabeledBox(t,e),!ae(t.score)||!ae(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}}function ot(t){return t.detection instanceof D}function Lt(t,e){const o={detection:e};return Object.assign({},t,o)}function Zo(){const t=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},e=function(){throw new Error("readFile - filesystem not available for browser 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e),C.createImageElement=t.createImageElement||(()=>new o),C.ImageData=t.ImageData||C.ImageData,C.Video=t.Video||C.Video,C.fetch=t.fetch||C.fetch,C.readFile=t.readFile||C.readFile}const F={getEnv:rs,setEnv:er,initialize:or,createBrowserEnv:Zo,createFileSystem:yo,createNodejsEnv:Ko,monkeyPatch:ns,isBrowser:Qo,isNodejs:tr.isNodejs};or();function Et(t){return!F.isNodejs()&&typeof t=="string"?document.getElementById(t):t}function A(t){const{Canvas:e,CanvasRenderingContext2D:o}=F.getEnv();if(t instanceof o)return t;const r=Et(t);if(!(r instanceof e))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var ft;(function(t){t.TOP_LEFT="TOP_LEFT",t.TOP_RIGHT="TOP_RIGHT",t.BOTTOM_LEFT="BOTTOM_LEFT",t.BOTTOM_RIGHT="BOTTOM_RIGHT"})(ft||(ft={}));class Fo{constructor(t={}){const{anchorPosition:e,backgroundColor:o,fontColor:r,fontSize:n,fontStyle:s,padding:i}=t;this.anchorPosition=e||ft.TOP_LEFT,this.backgroundColor=o||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=n||14,this.fontStyle=s||"Georgia",this.padding=i||4}}class ie{constructor(t,e,o={}){this.text=typeof t=="string"?[t]:t instanceof ie?t.text:t,this.anchor=e,this.options=new Fo(o)}measureWidth(t){const{padding:e}=this.options;return this.text.map(o=>t.measureText(o).width).reduce((o,r)=>o<r?r:o,0)+2*e}measureHeight(){const{fontSize:t,padding:e}=this.options;return this.text.length*t+2*e}getUpperLeft(t,e){const{anchorPosition:o}=this.options,r=o===ft.BOTTOM_RIGHT||o===ft.TOP_RIGHT,n=o===ft.BOTTOM_LEFT||o===ft.BOTTOM_RIGHT,s=this.measureWidth(t),i=this.measureHeight(),a=r?this.anchor.x-s:this.anchor.x,c=n?this.anchor.y-i:this.anchor.y;if(e){const{width:p,height:m}=e,f=Math.max(Math.min(a,p-s),0),d=Math.max(Math.min(c,m-i),0);return{x:f,y:d}}return{x:a,y:c}}draw(t){const e=Et(t),o=A(e),{backgroundColor:r,fontColor:n,fontSize:s,fontStyle:i,padding:a}=this.options;o.font=`${s}px ${i}`;const c=this.measureWidth(o),p=this.measureHeight();o.fillStyle=r;const m=this.getUpperLeft(o,e);o.fillRect(m.x,m.y,c,p),o.fillStyle=n,this.text.forEach((f,d)=>{const h=a+m.x,b=a+m.y+(d+1)*s;o.fillText(f,h,b)})}}class Qr{constructor(t={}){const{boxColor:e,lineWidth:o,label:r,drawLabelOptions:n}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=o||2,this.label=r;const s={anchorPosition:ft.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Fo(Object.assign({},s,n))}}class rr{constructor(t,e={}){this.box=new T(t),this.options=new Qr(e)}draw(t){const e=A(t),{boxColor:o,lineWidth:r}=this.options,{x:n,y:s,width:i,height:a}=this.box;e.strokeStyle=o,e.lineWidth=r,e.strokeRect(n,s,i,a);const{label:c}=this.options;c&&new ie([c],{x:n-r/2,y:s},this.options.drawLabelOptions).draw(t)}}function ss(t,e){const o=Array.isArray(e)?e:[e];o.forEach(r=>{const n=r instanceof D?r.score:ot(r)?r.detection.score:void 0,s=r instanceof D?r.box:ot(r)?r.detection.box:new T(r),i=n?`${Ht(n)}`:void 0;new rr(s,{label:i}).draw(t)})}function ee(t){const{Image:e,Video:o}=F.getEnv();return t instanceof e&&t.complete||t instanceof o&&t.readyState>=3}function wo(t){return new Promise((e,o)=>{if(t instanceof F.getEnv().Canvas||ee(t))return e(null);function r(s){if(!s.currentTarget)return;s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",n),e(s)}function 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lt{constructor(t,e=!1){if(this._imageTensors=[],this._canvases=[],this._treatAsBatchInput=!1,this._inputDimensions=[],!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((o,r)=>{if(ut(o)){this._imageTensors[r]=o,this._inputDimensions[r]=o.shape;return}if(H(o)){const s=o.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[r]=o,this._inputDimensions[r]=o.shape.slice(1);return}const n=o instanceof F.getEnv().Canvas?o:oe(o);this._canvases[r]=n,this._inputDimensions[r]=[n.height,n.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return pt(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");const e=this.getInputWidth(t),o=this.getInputHeight(t);return qo({width:e,height:o},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,K.tidy(()=>{const o=pt(this.batchSize,0,1).map(n=>{const s=this.getInput(n);if(s instanceof K.Tensor){let i=H(s)?s:s.expandDims();return i=Ze(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=K.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof F.getEnv().Canvas)return K.browser.fromPixels(ho(s,t,e));throw new 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function It(t,e){const{Canvas:o}=F.getEnv();let r=t;if(!(t instanceof o)){const i=await M(t);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const a=i.getInput(0);r=a instanceof o?a:await uo(a)}const n=A(r),s=e.map(i=>i instanceof D?i.forSize(r.width,r.height).box.floor():i).map(i=>i.clipAtImageBorders(r.width,r.height));return s.map(({x:i,y:a,width:c,height:p})=>{const m=Ot({width:c,height:p});return A(m).putImageData(n.getImageData(i,a,c,p),0,0),m})}const To=x(require("@tensorflow/tfjs"));async function $t(t,e){if(!ut(t)&&!H(t))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(H(t)&&t.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return To.tidy(()=>{const[o,r,n]=t.shape.slice(H(t)?1:0),s=e.map(a=>a instanceof D?a.forSize(r,o).box:a).map(a=>a.clipAtImageBorders(r,o)),i=s.map(({x:a,y:c,width:p,height:m})=>To.slice3d(t.as3D(o,r,n),[c,a,0],[m,p,n]));return i})}async function kt(t,e){const o=F.getEnv().fetch,r=await o(t,e);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Jr(t){const e=await kt(t),o=await e.blob();if(!o.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${o.type}, for url: ${e.url}`);return xo(o)}async function go(t){return(await kt(t)).json()}async function Vr(t){return new Float32Array(await(await kt(t)).arrayBuffer())}function Lo(t,e){const o=`${e}-weights_manifest.json`;if(!t)return{modelBaseUri:"",manifestUri:o};if(t==="/")return{modelBaseUri:"/",manifestUri:`/${o}`};const r=t.startsWith("http://")?"http://":t.startsWith("https://")?"https://":"";t=t.replace(r,"");const n=t.split("/").filter(a=>a),s=t.endsWith(".json")?n[n.length-1]:o;let i=r+(t.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return i=t.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}const tn=x(require("@tensorflow/tfjs"));async function lo(t,e){const{manifestUri:o,modelBaseUri:r}=Lo(t,e);let n=await go(o);return tn.io.loadWeights(n,r)}function Yr(t,e,o=!1){const{width:r,height:n}=o?Dt(e):e;return t.width=r,t.height=n,{width:r,height:n}}const gt=x(require("@tensorflow/tfjs"));class R{constructor(t){this._name=t,this._params=void 0,this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){const{obj:e,objProp:o}=this.traversePropertyPath(t);return e[o]}reassignParamFromPath(t,e){const{obj:o,objProp:r}=this.traversePropertyPath(t);o[r].dispose(),o[r]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{const o=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,o)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const e=await lo(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file 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S(t,e){Object.keys(t).forEach(o=>{e.some(r=>r.originalPath===o)||t[o].dispose()})}const Eo=x(require("@tensorflow/tfjs"));function pe(t,e){return function(o,r,n,s){const i=Eo.tensor4d(t(o*r*n*n),[n,n,o,r]),a=Eo.tensor1d(t(r));return e.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:a}}}const Do=x(require("@tensorflow/tfjs"));function ko(t,e){return function(o,r,n){const s=Do.tensor2d(t(o*r),[o,r]),i=Do.tensor1d(t(r));return e.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:s,bias:i}}}class nr{constructor(t,e,o){this.depthwise_filter=t,this.pointwise_filter=e,this.bias=o}}const Se=x(require("@tensorflow/tfjs"));function me(t,e){return function(o,r,n){const s=Se.tensor4d(t(3*3*o),[3,3,o,1]),i=Se.tensor4d(t(o*r),[1,1,o,r]),a=Se.tensor1d(t(r));return e.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new nr(s,i,a)}}function fe(t){return function(e){const o=t(`${e}/depthwise_filter`,4),r=t(`${e}/pointwise_filter`,4),n=t(`${e}/bias`,1);return new nr(o,r,n)}}function $(t,e){return function(o,r,n){const s=t[o];if(!Gt(s,r))throw new Error(`expected weightMap[${o}] to be a Tensor${r}D, instead have ${s}`);return e.push({originalPath:o,paramPath:n||o}),s}}function j(t){let e=t;function o(n){const s=e.slice(0,n);return e=e.slice(n),s}function r(){return e}return{extractWeights:o,getRemainingWeights:r}}function Co(t,e){const o=pe(t,e),r=me(t,e);function n(i,a,c,p=!1){const m=p?o(i,a,3,`${c}/conv0`):r(i,a,`${c}/conv0`),f=r(a,a,`${c}/conv1`),d=r(a,a,`${c}/conv2`);return{conv0:m,conv1:f,conv2:d}}function s(i,a,c,p=!1){const{conv0:m,conv1:f,conv2:d}=n(i,a,c,p),h=r(a,a,`${c}/conv3`);return{conv0:m,conv1:f,conv2:d,conv3:h}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:s}}function en(t){const e=[],{extractWeights:o,getRemainingWeights:r}=j(t),{extractDenseBlock4Params:n}=Co(o,e),s=n(3,32,"dense0",!0),i=n(32,64,"dense1"),a=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:e,params:{dense0:s,dense1:i,dense2:a,dense3:c}}}function Ro(t){return function(e){const o=t(`${e}/filters`,4),r=t(`${e}/bias`,1);return{filters:o,bias:r}}}function Ao(t,e){const o=$(t,e),r=Ro(o),n=fe(o);function s(a,c=!1){const p=c?r(`${a}/conv0`):n(`${a}/conv0`),m=n(`${a}/conv1`),f=n(`${a}/conv2`);return{conv0:p,conv1:m,conv2:f}}function i(a,c=!1){const p=c?r(`${a}/conv0`):n(`${a}/conv0`),m=n(`${a}/conv1`),f=n(`${a}/conv2`),d=n(`${a}/conv3`);return{conv0:p,conv1:m,conv2:f,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function on(t){const e=[],{extractDenseBlock4Params:o}=Ao(t,e),r={dense0:o("dense0",!0),dense1:o("dense1"),dense2:o("dense2"),dense3:o("dense3")};return S(t,e),{params:r,paramMappings:e}}const de=x(require("@tensorflow/tfjs"));class So extends R{constructor(){super("FaceFeatureExtractor")}forwardInput(t){const{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return de.tidy(()=>{const o=t.toBatchTensor(112,!0),r=[122.782,117.001,104.298],n=q(o,r).div(de.scalar(255));let s=Ae(n,e.dense0,!0);return s=Ae(s,e.dense1),s=Ae(s,e.dense2),s=Ae(s,e.dense3),s=de.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await M(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(t){return on(t)}extractParams(t){return en(t)}}const le=x(require("@tensorflow/tfjs"));function je(t,e){return le.tidy(()=>le.add(le.matMul(t,e.weights),e.bias))}function rn(t,e,o){const r=[],{extractWeights:n,getRemainingWeights:s}=j(t),i=ko(n,r),a=i(e,o,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: 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je(o.as2D(o.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){const{params:e,paramMappings:o}=this.extractClassifierParams(t);this._params=e,this._paramMappings=o}extractClassifierParams(t){return rn(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(t){const{featureExtractorMap:e,classifierMap:o}=jo(t);return this.faceFeatureExtractor.loadFromWeightMap(e),nn(o)}extractParams(t){const e=this.getClassifierChannelsIn(),o=this.getClassifierChannelsOut(),r=o*e+o,n=t.slice(0,t.length-r),s=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(n),this.extractClassifierParams(s)}}const po=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class Pt{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);po.forEach((e,o)=>{this[e]=t[o]})}asSortedArray(){return po.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}}const he=x(require("@tensorflow/tfjs"));class mo extends No{constructor(t=new So){super("FaceExpressionNet",t)}forwardInput(t){return he.tidy(()=>he.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await M(t))}async predictExpressions(t){const e=await M(t),o=await this.forwardInput(e),r=await Promise.all(he.unstack(o).map(async s=>{const i=await s.data();return s.dispose(),i}));o.dispose();const n=r.map(s=>new Pt(s));return e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function io(t){return t.expressions instanceof Pt}function Pe(t,e){const o={expressions:e};return Object.assign({},t,o)}function as(t,e,o=.1,r){const n=Array.isArray(e)?e:[e];n.forEach(s=>{const i=s instanceof Pt?s:io(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const a=i.asSortedArray(),c=a.filter(f=>f.probability>o),p=ot(s)?s.detection.box.bottomLeft:r||new w(0,0),m=new ie(c.map(f=>`${f.expression} (${Ht(f.probability)})`),p);m.draw(t)})}function Tt(t){return ot(t)&&t.landmarks instanceof G&&t.unshiftedLandmarks instanceof G&&t.alignedRect instanceof D}function Ut(t,e){const{box:o}=t.detection,r=e.shiftBy(o.x,o.y),n=r.align(),{imageDims:s}=t.detection,i=new D(t.detection.score,n.rescale(s.reverse()),s),a={landmarks:r,unshiftedLandmarks:e,alignedRect:i};return Object.assign({},t,a)}class an{constructor(t={}){const{drawLines:e=!0,drawPoints:o=!0,lineWidth:r,lineColor:n,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=o,this.lineWidth=r||1,this.pointSize=s||2,this.lineColor=n||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}}class cn{constructor(t,e={}){this.faceLandmarks=t,this.options=new an(e)}draw(t){const e=A(t),{drawLines:o,drawPoints:r,lineWidth:n,lineColor:s,pointSize:i,pointColor:a}=this.options;if(o&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=n,ht(e,this.faceLandmarks.getJawOutline()),ht(e,this.faceLandmarks.getLeftEyeBrow()),ht(e,this.faceLandmarks.getRightEyeBrow()),ht(e,this.faceLandmarks.getNose()),ht(e,this.faceLandmarks.getLeftEye(),!0),ht(e,this.faceLandmarks.getRightEye(),!0),ht(e,this.faceLandmarks.getMouth(),!0)),r){e.strokeStyle=a,e.fillStyle=a;const c=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(c)}}}function is(t,e){const o=Array.isArray(e)?e:[e];o.forEach(r=>{const n=r instanceof G?r:Tt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new cn(n).draw(t)})}const Ye={};He(Ye,{AnchorPosition:()=>ft,DrawBox:()=>rr,DrawBoxOptions:()=>Qr,DrawFaceLandmarks:()=>cn,DrawFaceLandmarksOptions:()=>an,DrawTextField:()=>ie,DrawTextFieldOptions:()=>Fo,drawContour:()=>ht,drawDetections:()=>ss,drawFaceExpressions:()=>as,drawFaceLandmarks:()=>is});function cs(t,e){const o=pe(t,e),r=me(t,e);function n(i,a,c){const p=r(i,a,`${c}/separable_conv0`),m=r(a,a,`${c}/separable_conv1`),f=o(i,a,1,`${c}/expansion_conv`);return{separable_conv0:p,separable_conv1:m,expansion_conv:f}}function s(i,a){const c=r(i,i,`${a}/separable_conv0`),p=r(i,i,`${a}/separable_conv1`),m=r(i,i,`${a}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:m}}return{extractConvParams:o,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:s}}function pn(t,e){const o=[],{extractWeights:r,getRemainingWeights:n}=j(t),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:a,extractMainBlockParams:c}=cs(r,o),p=s(3,32,3,"entry_flow/conv_in"),m=a(32,64,"entry_flow/reduction_block_0"),f=a(64,128,"entry_flow/reduction_block_1"),d={conv_in:p,reduction_block_0:m,reduction_block_1:f},h={};pt(e,0,1).forEach(_=>{h[`main_block_${_}`]=c(128,`middle_flow/main_block_${_}`)});const b=a(128,256,"exit_flow/reduction_block"),u=i(256,512,"exit_flow/separable_conv"),v={reduction_block:b,separable_conv:u};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:o,params:{entry_flow:d,middle_flow:h,exit_flow:v}}}function ps(t,e){const o=$(t,e),r=Ro(o),n=fe(o);function s(a){const c=n(`${a}/separable_conv0`),p=n(`${a}/separable_conv1`),m=r(`${a}/expansion_conv`);return{separable_conv0:c,separable_conv1:p,expansion_conv:m}}function i(a){const c=n(`${a}/separable_conv0`),p=n(`${a}/separable_conv1`),m=n(`${a}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:m}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:s,extractMainBlockParams:i}}function mn(t,e){const o=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:s,extractMainBlockParams:i}=ps(t,o),a=r("entry_flow/conv_in"),c=s("entry_flow/reduction_block_0"),p=s("entry_flow/reduction_block_1"),m={conv_in:a,reduction_block_0:c,reduction_block_1:p},f={};pt(e,0,1).forEach(u=>{f[`main_block_${u}`]=i(`middle_flow/main_block_${u}`)});const d=s("exit_flow/reduction_block"),h=n("exit_flow/separable_conv"),b={reduction_block:d,separable_conv:h};return S(t,o),{params:{entry_flow:m,middle_flow:f,exit_flow:b},paramMappings:o}}const k=x(require("@tensorflow/tfjs"));function fn(t,e,o){return k.add(k.conv2d(t,e.filters,o,"same"),e.bias)}function sr(t,e,o=!0){let r=o?k.relu(t):t;return r=U(r,e.separable_conv0,[1,1]),r=U(k.relu(r),e.separable_conv1,[1,1]),r=k.maxPool(r,[3,3],[2,2],"same"),r=k.add(r,fn(t,e.expansion_conv,[2,2])),r}function ms(t,e){let o=U(k.relu(t),e.separable_conv0,[1,1]);return o=U(k.relu(o),e.separable_conv1,[1,1]),o=U(k.relu(o),e.separable_conv2,[1,1]),o=k.add(o,t),o}class dn extends R{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){const{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return k.tidy(()=>{const o=t.toBatchTensor(112,!0),r=[122.782,117.001,104.298],n=q(o,r).div(k.scalar(256));let s=k.relu(fn(n,e.entry_flow.conv_in,[2,2]));return s=sr(s,e.entry_flow.reduction_block_0,!1),s=sr(s,e.entry_flow.reduction_block_1),pt(this._numMainBlocks,0,1).forEach(i=>{s=ms(s,e.middle_flow[`main_block_${i}`])}),s=sr(s,e.exit_flow.reduction_block),s=k.relu(U(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await M(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(t){return mn(t,this._numMainBlocks)}extractParams(t){return pn(t,this._numMainBlocks)}}function ln(t){const e=[],{extractWeights:o,getRemainingWeights:r}=j(t),n=ko(o,e),s=n(512,1,"fc/age"),i=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:e,params:{fc:{age:s,gender:i}}}}function hn(t){const e=[],o=$(t,e);function r(s){const i=o(`${s}/weights`,2),a=o(`${s}/bias`,1);return{weights:i,bias:a}}const n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return S(t,e),{params:n,paramMappings:e}}var ct;(function(t){t.FEMALE="female",t.MALE="male"})(ct||(ct={}));const dt=x(require("@tensorflow/tfjs"));class vo extends R{constructor(t=new dn(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){const{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return dt.tidy(()=>{const o=t instanceof lt?this.faceFeatureExtractor.forwardInput(t):t,r=dt.avgPool(o,[7,7],[2,2],"valid").as2D(o.shape[0],-1),n=je(r,e.fc.age).as1D(),s=je(r,e.fc.gender);return{age:n,gender:s}})}forwardInput(t){return dt.tidy(()=>{const{age:e,gender:o}=this.runNet(t);return{age:e,gender:dt.softmax(o)}})}async forward(t){return this.forwardInput(await M(t))}async predictAgeAndGender(t){const e=await M(t),o=await this.forwardInput(e),r=dt.unstack(o.age),n=dt.unstack(o.gender),s=r.map((a,c)=>({ageTensor:a,genderTensor:n[c]})),i=await Promise.all(s.map(async({ageTensor:a,genderTensor:c})=>{const p=(await a.data())[0],m=(await c.data())[0],f=m>.5,d=f?ct.MALE:ct.FEMALE,h=f?m:1-m;return a.dispose(),c.dispose(),{age:p,gender:d,genderProbability:h}}));return o.age.dispose(),o.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){const{params:e,paramMappings:o}=this.extractClassifierParams(t);this._params=e,this._paramMappings=o}extractClassifierParams(t){return ln(t)}extractParamsFromWeigthMap(t){const{featureExtractorMap:e,classifierMap:o}=jo(t);return this.faceFeatureExtractor.loadFromWeightMap(e),hn(o)}extractParams(t){const e=512*1+1+(512*2+2),o=t.slice(0,t.length-e),r=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(o),this.extractClassifierParams(r)}}const O=x(require("@tensorflow/tfjs"));class Wo extends No{postProcess(t,e,o){const r=o.map(({width:s,height:i})=>{const a=e/Math.max(i,s);return{width:s*a,height:i*a}}),n=r.length;return O.tidy(()=>{const s=(m,f)=>O.stack([O.fill([68],m),O.fill([68],f)],1).as2D(1,136).as1D(),i=(m,f)=>{const{width:d,height:h}=r[m];return f(d,h)?Math.abs(d-h)/2:0},a=m=>i(m,(f,d)=>f<d),c=m=>i(m,(f,d)=>d<f),p=t.mul(O.fill([n,136],e)).sub(O.stack(Array.from(Array(n),(m,f)=>s(a(f),c(f))))).div(O.stack(Array.from(Array(n),(m,f)=>s(r[f].width,r[f].height))));return p})}forwardInput(t){return O.tidy(()=>{const e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([o,r])=>({height:o,width:r})))})}async forward(t){return this.forwardInput(await M(t))}async detectLandmarks(t){const e=await M(t),o=O.tidy(()=>O.unstack(this.forwardInput(e))),r=await Promise.all(o.map(async(n,s)=>{const i=Array.from(await n.data()),a=i.filter((p,m)=>_o(m)),c=i.filter((p,m)=>!_o(m));return new ne(Array(68).fill(0).map((p,m)=>new w(a[m],c[m])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return o.forEach(n=>n.dispose()),e.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}}class te extends Wo{constructor(t=new So){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function un(t){const e=[],{extractDenseBlock3Params:o}=Ao(t,e),r={dense0:o("dense0",!0),dense1:o("dense1"),dense2:o("dense2")};return S(t,e),{params:r,paramMappings:e}}function gn(t){const e=[],{extractWeights:o,getRemainingWeights:r}=j(t),{extractDenseBlock3Params:n}=Co(o,e),s=n(3,32,"dense0",!0),i=n(32,64,"dense1"),a=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:e,params:{dense0:s,dense1:i,dense2:a}}}const ue=x(require("@tensorflow/tfjs"));class xn extends R{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){const{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return ue.tidy(()=>{const o=t.toBatchTensor(112,!0),r=[122.782,117.001,104.298],n=q(o,r).div(ue.scalar(255));let s=Po(n,e.dense0,!0);return s=Po(s,e.dense1),s=Po(s,e.dense2),s=ue.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await M(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(t){return un(t)}extractParams(t){return gn(t)}}class co extends Wo{constructor(t=new xn){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class Hr extends te{}const Bo=x(require("@tensorflow/tfjs"));function wn(t,e){return Bo.add(Bo.mul(t,e.weights),e.biases)}const ge=x(require("@tensorflow/tfjs"));function ar(t,e,o,r,n="same"){const{filters:s,bias:i}=e.conv;let a=ge.conv2d(t,s,o,n);return a=ge.add(a,i),a=wn(a,e.scale),r?ge.relu(a):a}function vn(t,e){return ar(t,e,[1,1],!0)}function ir(t,e){return ar(t,e,[1,1],!1)}function Uo(t,e){return ar(t,e,[2,2],!0,"valid")}const z=x(require("@tensorflow/tfjs"));function fs(t,e){function o(a,c,p){const m=t(a),f=m.length/(c*p*p);if(Xo(f))throw new 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_n(t){const{extractWeights:e,getRemainingWeights:o}=j(t),r=[],{extractConvLayerParams:n,extractResidualLayerParams:s}=fs(e,r),i=n(4704,32,7,"conv32_down"),a=s(9216,32,3,"conv32_1"),c=s(9216,32,3,"conv32_2"),p=s(9216,32,3,"conv32_3"),m=s(36864,64,3,"conv64_down",!0),f=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),b=s(147456,128,3,"conv128_down",!0),u=s(147456,128,3,"conv128_1"),v=s(147456,128,3,"conv128_2"),_=s(589824,256,3,"conv256_down",!0),g=s(589824,256,3,"conv256_1"),E=s(589824,256,3,"conv256_2"),W=s(589824,256,3,"conv256_down_out"),I=z.tidy(()=>z.transpose(z.tensor2d(e(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),o().length!==0)throw new Error(`weights remaing after extract: ${o().length}`);const V={conv32_down:i,conv32_1:a,conv32_2:c,conv32_3:p,conv64_down:m,conv64_1:f,conv64_2:d,conv64_3:h,conv128_down:b,conv128_1:u,conv128_2:v,conv256_down:_,conv256_1:g,conv256_2:E,conv256_down_out:W,fc:I};return{params:V,paramMappings:r}}function ds(t,e){const o=$(t,e);function r(i){const a=o(`${i}/scale/weights`,1),c=o(`${i}/scale/biases`,1);return{weights:a,biases:c}}function n(i){const a=o(`${i}/conv/filters`,4),c=o(`${i}/conv/bias`,1),p=r(i);return{conv:{filters:a,bias:c},scale:p}}function s(i){return{conv1:n(`${i}/conv1`),conv2:n(`${i}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:s}}function bn(t){const e=[],{extractConvLayerParams:o,extractResidualLayerParams:r}=ds(t,e),n=o("conv32_down"),s=r("conv32_1"),i=r("conv32_2"),a=r("conv32_3"),c=r("conv64_down"),p=r("conv64_1"),m=r("conv64_2"),f=r("conv64_3"),d=r("conv128_down"),h=r("conv128_1"),b=r("conv128_2"),u=r("conv256_down"),v=r("conv256_1"),_=r("conv256_2"),g=r("conv256_down_out"),E=t.fc;if(e.push({originalPath:"fc",paramPath:"fc"}),!Jo(E))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${E}`);const 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r=t.arraySync(),n=Math.min(r[e][0],r[e][2]),s=Math.min(r[e][1],r[e][3]),i=Math.max(r[e][0],r[e][2]),a=Math.max(r[e][1],r[e][3]),c=Math.min(r[o][0],r[o][2]),p=Math.min(r[o][1],r[o][3]),m=Math.max(r[o][0],r[o][2]),f=Math.max(r[o][1],r[o][3]),d=(i-n)*(a-s),h=(m-c)*(f-p);if(d<=0||h<=0)return 0;const b=Math.max(n,c),u=Math.max(s,p),v=Math.min(i,m),_=Math.min(a,f),g=Math.max(v-b,0)*Math.max(_-u,0);return g/(d+h-g)}const l=x(require("@tensorflow/tfjs"));function vs(t){const e=l.unstack(l.transpose(t,[1,0])),o=[l.sub(e[2],e[0]),l.sub(e[3],e[1])],r=[l.add(e[0],l.div(o[0],l.scalar(2))),l.add(e[1],l.div(o[1],l.scalar(2)))];return{sizes:o,centers:r}}function _s(t,e){const{sizes:o,centers:r}=vs(t),n=l.unstack(l.transpose(e,[1,0])),s=l.div(l.mul(l.exp(l.div(n[2],l.scalar(5))),o[0]),l.scalar(2)),i=l.add(l.mul(l.div(n[0],l.scalar(10)),o[0]),r[0]),a=l.div(l.mul(l.exp(l.div(n[3],l.scalar(5))),o[1]),l.scalar(2)),c=l.add(l.mul(l.div(n[1],l.scalar(10)),o[1]),r[1]);return l.transpose(l.stack([l.sub(i,s),l.sub(c,a),l.add(i,s),l.add(c,a)]),[1,0])}function Ln(t,e,o){return l.tidy(()=>{const r=t.shape[0];let n=_s(l.reshape(l.tile(o.extra_dim,[r,1,1]),[-1,4]),l.reshape(t,[-1,4]));n=l.reshape(n,[r,n.shape[0]/r,4]);const s=l.sigmoid(l.slice(e,[0,0,1],[-1,-1,-1]));let i=l.slice(s,[0,0,0],[-1,-1,1]);i=l.reshape(i,[r,i.shape[1]]);const a=l.unstack(n),c=l.unstack(i);return{boxes:a,scores:c}})}const We=x(require("@tensorflow/tfjs"));function Jt(t,e){return We.tidy(()=>{const o=t.shape[0],r=We.reshape(Vt(t,e.box_encoding_predictor),[o,-1,1,4]),n=We.reshape(Vt(t,e.class_predictor),[o,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}const Be=x(require("@tensorflow/tfjs"));function Pn(t,e,o){return Be.tidy(()=>{const r=Q(t,o.conv_0,[1,1]),n=Q(r,o.conv_1,[2,2]),s=Q(n,o.conv_2,[1,1]),i=Q(s,o.conv_3,[2,2]),a=Q(i,o.conv_4,[1,1]),c=Q(a,o.conv_5,[2,2]),p=Q(c,o.conv_6,[1,1]),m=Q(p,o.conv_7,[2,2]),f=Jt(e,o.box_predictor_0),d=Jt(t,o.box_predictor_1),h=Jt(n,o.box_predictor_2),b=Jt(i,o.box_predictor_3),u=Jt(c,o.box_predictor_4),v=Jt(m,o.box_predictor_5),_=Be.concat([f.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,b.boxPredictionEncoding,u.boxPredictionEncoding,v.boxPredictionEncoding],1),g=Be.concat([f.classPrediction,d.classPrediction,h.classPrediction,b.classPrediction,u.classPrediction,v.classPrediction],1);return{boxPredictions:_,classPredictions:g}})}class X{constructor({minConfidence:t,maxResults:e}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof 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c.data()),m=.5,f=Tn(a,p,o,m,r),d=n.getReshapedInputDimensions(0),h=n.inputSize,b=h/d.width,u=h/d.height,v=a.arraySync(),_=f.map(g=>{const[E,W]=[Math.max(0,v[g][0]),Math.min(1,v[g][2])].map(J=>J*u),[I,V]=[Math.max(0,v[g][1]),Math.min(1,v[g][3])].map(J=>J*b);return new D(p[g],new re(I,E,V-I,W-E),{height:n.getInputHeight(0),width:n.getInputWidth(0)})});return a.dispose(),c.dispose(),_}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(t){return Fn(t)}extractParams(t){return yn(t)}}function $o(t){const e=new Bt;return e.extractWeights(t),e}function vr(t){return $o(t)}class _r extends Bt{}const En=.4,Dn=[new w(.738768,.874946),new w(2.42204,2.65704),new w(4.30971,7.04493),new w(10.246,4.59428),new w(12.6868,11.8741)],kn=[new w(1.603231,2.094468),new w(6.041143,7.080126),new w(2.882459,3.518061),new w(4.266906,5.178857),new w(9.041765,10.66308)],Cn=[117.001,114.697,97.404],Rn="tiny_yolov2_model",An="tiny_yolov2_separable_conv_model";const Io=t=>typeof t=="number";function Je(t){if(!t)throw new Error(`invalid config: ${t}`);if(typeof t.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${t.withSeparableConvs}`);if(!Io(t.iouThreshold)||t.iouThreshold<0||t.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${t.iouThreshold}`);if(!Array.isArray(t.classes)||!t.classes.length||!t.classes.every(e=>typeof e=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(t.classes)}`);if(!Array.isArray(t.anchors)||!t.anchors.length||!t.anchors.map(e=>e||{}).every(e=>Io(e.x)&&Io(e.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(t.anchors)}`);if(t.meanRgb&&(!Array.isArray(t.meanRgb)||t.meanRgb.length!==3||!t.meanRgb.every(Io)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(t.meanRgb)}`)}const tt=x(require("@tensorflow/tfjs"));function xe(t){return tt.tidy(()=>{const e=tt.mul(t,tt.scalar(.10000000149011612));return tt.add(tt.relu(tt.sub(t,e)),e)})}const et=x(require("@tensorflow/tfjs"));function wt(t,e){return et.tidy(()=>{let o=et.pad(t,[[0,0],[1,1],[1,1],[0,0]]);return o=et.conv2d(o,e.conv.filters,[1,1],"valid"),o=et.sub(o,e.bn.sub),o=et.mul(o,e.bn.truediv),o=et.add(o,e.conv.bias),xe(o)})}const jt=x(require("@tensorflow/tfjs"));function vt(t,e){return jt.tidy(()=>{let o=jt.pad(t,[[0,0],[1,1],[1,1],[0,0]]);return o=jt.separableConv2d(o,e.depthwise_filter,e.pointwise_filter,[1,1],"valid"),o=jt.add(o,e.bias),xe(o)})}const cr=x(require("@tensorflow/tfjs"));function bs(t,e){const o=pe(t,e);function r(i,a){const c=cr.tensor1d(t(i)),p=cr.tensor1d(t(i));return e.push({paramPath:`${a}/sub`},{paramPath:`${a}/truediv`}),{sub:c,truediv:p}}function n(i,a,c){const p=o(i,a,3,`${c}/conv`),m=r(a,`${c}/bn`);return{conv:p,bn:m}}const s=me(t,e);return{extractConvParams:o,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}}function Sn(t,e,o,r){const{extractWeights:n,getRemainingWeights:s}=j(t),i=[],{extractConvParams:a,extractConvWithBatchNormParams:c,extractSeparableConvParams:p}=bs(n,i);let m;if(e.withSeparableConvs){const[f,d,h,b,u,v,_,g,E]=r,W=e.isFirstLayerConv2d?a(f,d,3,"conv0"):p(f,d,"conv0"),I=p(d,h,"conv1"),V=p(h,b,"conv2"),J=p(b,u,"conv3"),_t=p(u,v,"conv4"),bt=p(v,_,"conv5"),yt=g?p(_,g,"conv6"):void 0,Nt=E?p(g,E,"conv7"):void 0,Wt=a(E||g||_,5*o,1,"conv8");m={conv0:W,conv1:I,conv2:V,conv3:J,conv4:_t,conv5:bt,conv6:yt,conv7:Nt,conv8:Wt}}else{const[f,d,h,b,u,v,_,g,E]=r,W=c(f,d,"conv0"),I=c(d,h,"conv1"),V=c(h,b,"conv2"),J=c(b,u,"conv3"),_t=c(u,v,"conv4"),bt=c(v,_,"conv5"),yt=c(_,g,"conv6"),Nt=c(g,E,"conv7"),Wt=a(E,5*o,1,"conv8");m={conv0:W,conv1:I,conv2:V,conv3:J,conv4:_t,conv5:bt,conv6:yt,conv7:Nt,conv8:Wt}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:m,paramMappings:i}}function ys(t,e){const o=$(t,e);function r(a){const c=o(`${a}/sub`,1),p=o(`${a}/truediv`,1);return{sub:c,truediv:p}}function n(a){const c=o(`${a}/filters`,4),p=o(`${a}/bias`,1);return{filters:c,bias:p}}function s(a){const c=n(`${a}/conv`),p=r(`${a}/bn`);return{conv:c,bn:p}}const i=fe(o);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function jn(t,e){const o=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=ys(t,o);let i;if(e.withSeparableConvs){const a=e.filterSizes&&e.filterSizes.length||9;i={conv0:e.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:a>7?s("conv6"):void 0,conv7:a>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return S(t,o),{params:i,paramMappings:o}}var Xe;(function(t){t[t.XS=224]="XS",t[t.SM=320]="SM",t[t.MD=416]="MD",t[t.LG=608]="LG"})(Xe||(Xe={}));class it{constructor({inputSize:t,scoreThreshold:e}={}){if(this._name="TinyYolov2Options",this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const P=x(require("@tensorflow/tfjs"));class we extends R{constructor(t){super("TinyYolov2");Je(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let o=wt(t,e.conv0);return o=P.maxPool(o,[2,2],[2,2],"same"),o=wt(o,e.conv1),o=P.maxPool(o,[2,2],[2,2],"same"),o=wt(o,e.conv2),o=P.maxPool(o,[2,2],[2,2],"same"),o=wt(o,e.conv3),o=P.maxPool(o,[2,2],[2,2],"same"),o=wt(o,e.conv4),o=P.maxPool(o,[2,2],[2,2],"same"),o=wt(o,e.conv5),o=P.maxPool(o,[2,2],[1,1],"same"),o=wt(o,e.conv6),o=wt(o,e.conv7),Vt(o,e.conv8,"valid",!1)}runMobilenet(t,e){let o=this.config.isFirstLayerConv2d?xe(Vt(t,e.conv0,"valid",!1)):vt(t,e.conv0);return o=P.maxPool(o,[2,2],[2,2],"same"),o=vt(o,e.conv1),o=P.maxPool(o,[2,2],[2,2],"same"),o=vt(o,e.conv2),o=P.maxPool(o,[2,2],[2,2],"same"),o=vt(o,e.conv3),o=P.maxPool(o,[2,2],[2,2],"same"),o=vt(o,e.conv4),o=P.maxPool(o,[2,2],[2,2],"same"),o=vt(o,e.conv5),o=P.maxPool(o,[2,2],[1,1],"same"),o=e.conv6?vt(o,e.conv6):o,o=e.conv7?vt(o,e.conv7):o,Vt(o,e.conv8,"valid",!1)}forwardInput(t,e){const{params:o}=this;if(!o)throw new Error("TinyYolov2 - load model before inference");return P.tidy(()=>{let r=P.cast(t.toBatchTensor(e,!1),"float32");return r=this.config.meanRgb?q(r,this.config.meanRgb):r,r=r.div(P.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(r,o):this.runTinyYolov2(r,o)})}async forward(t,e){return await this.forwardInput(await M(t),e)}async detect(t,e={}){const{inputSize:o,scoreThreshold:r}=new it(e),n=await M(t),s=await this.forwardInput(n,o),i=P.tidy(()=>P.unstack(s)[0].expandDims()),a={width:n.getInputWidth(0),height:n.getInputHeight(0)},c=await this.extractBoxes(i,n.getReshapedInputDimensions(0),r);s.dispose(),i.dispose();const p=c.map(u=>u.box),m=c.map(u=>u.score),f=c.map(u=>u.classScore),d=c.map(u=>this.config.classes[u.label]),h=Ke(p.map(u=>u.rescale(o)),m,this.config.iouThreshold,!0),b=h.map(u=>new zt(m[u],f[u],d[u],p[u],a));return b}getDefaultModelName(){return""}extractParamsFromWeigthMap(t){return jn(t,this.config)}extractParams(t){const e=this.config.filterSizes||we.DEFAULT_FILTER_SIZES,o=e?e.length:void 0;if(o!==7&&o!==8&&o!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${o} filterSizes in config`);return Sn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,o){const{width:r,height:n}=e,s=Math.max(r,n),i=s/r,a=s/n,c=t.shape[1],p=this.config.anchors.length,[m,f,d]=P.tidy(()=>{const v=t.reshape([c,c,p,this.boxEncodingSize]),_=v.slice([0,0,0,0],[c,c,p,4]),g=v.slice([0,0,0,4],[c,c,p,1]),E=this.withClassScores?P.softmax(v.slice([0,0,0,5],[c,c,p,this.config.classes.length]),3):P.scalar(0);return[_,g,E]}),h=[],b=await f.array(),u=await m.array();for(let v=0;v<c;v++)for(let _=0;_<c;_++)for(let g=0;g<p;g++){const E=Kt(b[v][_][g][0]);if(!o||E>o){const W=(_+Kt(u[v][_][g][0]))/c*i,I=(v+Kt(u[v][_][g][1]))/c*a,V=Math.exp(u[v][_][g][2])*this.config.anchors[g].x/c*i,J=Math.exp(u[v][_][g][3])*this.config.anchors[g].y/c*a,_t=W-V/2,bt=I-J/2,yt={row:v,col:_,anchor:g},{classScore:Nt,label:Wt}=this.withClassScores?await this.extractPredictedClass(d,yt):{classScore:1,label:0};h.push({box:new se(_t,bt,_t+V,bt+J),score:E,classScore:E*Nt,label:Wt,...yt})}}return m.dispose(),f.dispose(),d.dispose(),h}async extractPredictedClass(t,e){const{row:o,col:r,anchor:n}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,a)=>s[o][r][n][a]).map((i,a)=>({classScore:i,label:a})).reduce((i,a)=>i.classScore>a.classScore?i:a)}}we.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class qt extends we{constructor(t=!0){const e=Object.assign({},{withSeparableConvs:t,iouThreshold:En,classes:["face"]},t?{anchors:kn,meanRgb:Cn}:{anchors:Dn,withClassScores:!0});super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){const o=await this.detect(t,e);return o.map(r=>new D(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?An:Rn}extractParamsFromWeigthMap(t){return super.extractParamsFromWeigthMap(t)}}function xr(t,e=!0){const o=new qt(e);return o.extractWeights(t),o}class qe extends it{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Z{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const pr=x(require("@tensorflow/tfjs"));async function Xt(t,e,o,r,n=({alignedRect:s})=>s){const s=t.map(c=>Tt(c)?n(c):c.detection),i=r||(e instanceof pr.Tensor?await $t(e,s):await It(e,s)),a=await o(i);return i.forEach(c=>c instanceof pr.Tensor&&c.dispose()),a}async function ve(t,e,o,r,n){return Xt([t],e,async s=>o(s[0]),r,n)}const Nn=.4,Wn=[new w(1.603231,2.094468),new w(6.041143,7.080126),new w(2.882459,3.518061),new w(4.266906,5.178857),new w(9.041765,10.66308)],Bn=[117.001,114.697,97.404];class Zt extends we{constructor(){const t={withSeparableConvs:!0,iouThreshold:Nn,classes:["face"],anchors:Wn,meanRgb:Bn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){const o=await this.detect(t,e);return o.map(r=>new D(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(t){return super.extractParamsFromWeigthMap(t)}}const y={ssdMobilenetv1:new Bt,tinyFaceDetector:new Zt,tinyYolov2:new qt,faceLandmark68Net:new te,faceLandmark68TinyNet:new co,faceRecognitionNet:new Qt,faceExpressionNet:new mo,ageGenderNet:new vo},zo=(t,e)=>y.ssdMobilenetv1.locateFaces(t,e),Fr=(t,e)=>y.tinyFaceDetector.locateFaces(t,e),Mr=(t,e)=>y.tinyYolov2.locateFaces(t,e),Go=t=>y.faceLandmark68Net.detectLandmarks(t),Tr=t=>y.faceLandmark68TinyNet.detectLandmarks(t),Lr=t=>y.faceRecognitionNet.computeFaceDescriptor(t),Pr=t=>y.faceExpressionNet.predictExpressions(t),Er=t=>y.ageGenderNet.predictAgeAndGender(t),Ho=t=>y.ssdMobilenetv1.load(t),Dr=t=>y.tinyFaceDetector.load(t),kr=t=>y.tinyYolov2.load(t),Cr=t=>y.faceLandmark68Net.load(t),Rr=t=>y.faceLandmark68TinyNet.load(t),Ar=t=>y.faceRecognitionNet.load(t),Sr=t=>y.faceExpressionNet.load(t),jr=t=>y.ageGenderNet.load(t),Nr=Ho,Wr=zo,Br=Go;class Un extends Z{constructor(t,e,o){super();this.parentTask=t,this.input=e,this.extractedFaces=o}}class $e extends Un{async run(){const t=await this.parentTask,e=await Xt(t,this.input,async o=>await Promise.all(o.map(r=>y.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((o,r)=>Pe(o,e[r]))}withAgeAndGender(){return new Ue(this,this.input)}}class Oe extends Un{async run(){const t=await this.parentTask;if(!t)return;const e=await ve(t,this.input,o=>y.faceExpressionNet.predictExpressions(o),this.extractedFaces);return Pe(t,e)}withAgeAndGender(){return new Ie(this,this.input)}}class ye extends $e{withAgeAndGender(){return new _e(this,this.input)}withFaceDescriptors(){return new Ft(this,this.input)}}class Fe extends Oe{withAgeAndGender(){return new be(this,this.input)}withFaceDescriptor(){return new Mt(this,this.input)}}class In extends Z{constructor(t,e,o){super();this.parentTask=t,this.input=e,this.extractedFaces=o}}class Ue extends In{async run(){const t=await this.parentTask,e=await Xt(t,this.input,async o=>await Promise.all(o.map(r=>y.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((o,r)=>{const{age:n,gender:s,genderProbability:i}=e[r];return De(Le(o,s,i),n)})}withFaceExpressions(){return new $e(this,this.input)}}class Ie extends In{async run(){const t=await this.parentTask;if(!t)return;const{age:e,gender:o,genderProbability:r}=await ve(t,this.input,n=>y.ageGenderNet.predictAgeAndGender(n),this.extractedFaces);return De(Le(t,o,r),e)}withFaceExpressions(){return new Oe(this,this.input)}}class _e extends Ue{withFaceExpressions(){return new ye(this,this.input)}withFaceDescriptors(){return new Ft(this,this.input)}}class be extends Ie{withFaceExpressions(){return new Fe(this,this.input)}withFaceDescriptor(){return new Mt(this,this.input)}}class ao extends Z{constructor(t,e){super();this.parentTask=t,this.input=e}}class Ft extends ao{async run(){const t=await this.parentTask,e=await Xt(t,this.input,o=>Promise.all(o.map(r=>y.faceRecognitionNet.computeFaceDescriptor(r))),null,o=>o.landmarks.align(null,{useDlibAlignment:!0}));return e.map((o,r)=>Ee(t[r],o))}withFaceExpressions(){return new ye(this,this.input)}withAgeAndGender(){return new _e(this,this.input)}}class Mt extends ao{async run(){const t=await this.parentTask;if(!t)return;const e=await ve(t,this.input,o=>y.faceRecognitionNet.computeFaceDescriptor(o),null,o=>o.landmarks.align(null,{useDlibAlignment:!0}));return Ee(t,e)}withFaceExpressions(){return new Fe(this,this.input)}withAgeAndGender(){return new be(this,this.input)}}const ze=x(require("@tensorflow/tfjs"));class ro extends Z{constructor(t,e,o){super();this.parentTask=t,this.input=e,this.useTinyLandmarkNet=o}get landmarkNet(){return this.useTinyLandmarkNet?y.faceLandmark68TinyNet:y.faceLandmark68Net}}class no extends ro{async run(){const t=await this.parentTask,e=t.map(n=>n.detection),o=this.input instanceof ze.Tensor?await $t(this.input,e):await It(this.input,e),r=await Promise.all(o.map(n=>this.landmarkNet.detectLandmarks(n)));return o.forEach(n=>n instanceof ze.Tensor&&n.dispose()),t.map((n,s)=>Ut(n,r[s]))}withFaceExpressions(){return new ye(this,this.input)}withAgeAndGender(){return new _e(this,this.input)}withFaceDescriptors(){return new Ft(this,this.input)}}class so extends ro{async run(){const t=await this.parentTask;if(!t)return;const{detection:e}=t,o=this.input instanceof ze.Tensor?await $t(this.input,[e]):await It(this.input,[e]),r=await this.landmarkNet.detectLandmarks(o[0]);return o.forEach(n=>n instanceof ze.Tensor&&n.dispose()),Ut(t,r)}withFaceExpressions(){return new Fe(this,this.input)}withAgeAndGender(){return new be(this,this.input)}withFaceDescriptor(){return new Mt(this,this.input)}}class eo extends Z{constructor(t,e=new X){super();this.input=t,this.options=e}}class Te extends eo{async run(){const{input:t,options:e}=this,o=e instanceof qe?r=>y.tinyFaceDetector.locateFaces(r,e):e instanceof X?r=>y.ssdMobilenetv1.locateFaces(r,e):e instanceof it?r=>y.tinyYolov2.locateFaces(r,e):null;if(!o)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return o(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{const e=await this.run();return t(e.map(o=>Lt({},o)))})}withFaceLandmarks(t=!1){return new no(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new $e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ue(this.runAndExtendWithFaceDetections(),this.input)}}class oo extends eo{async run(){const t=await new Te(this.input,this.options);let e=t[0];return t.forEach(o=>{o.score>e.score&&(e=o)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{const e=await this.run();return t(e?Lt({},e):void 0)})}withFaceLandmarks(t=!1){return new so(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Oe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ie(this.runAndExtendWithFaceDetection(),this.input)}}function Ur(t,e=new X){return new oo(t,e)}function Me(t,e=new X){return new Te(t,e)}async function Yo(t,e){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Me(t,new X(e?{minConfidence:e}:{})).withFaceLandmarks().withFaceDescriptors()}async function Ir(t,e={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Me(t,new it(e)).withFaceLandmarks().withFaceDescriptors()}const $r=Yo;function fo(t,e){if(t.length!==e.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const o=Array.from(t),r=Array.from(e);return Math.sqrt(o.map((n,s)=>n-r[s]).reduce((n,s)=>n+Math.pow(s,2),0))}class Vo{constructor(t,e=.6){this._distanceThreshold=e;const o=Array.isArray(t)?t:[t];if(!o.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1;const n=()=>`person ${r++}`;this._labeledDescriptors=o.map(s=>{if(s instanceof Ct)return s;if(s instanceof Float32Array)return new Ct(n(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new Ct(n(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(o=>fo(o,t)).reduce((o,r)=>o+r,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:o})=>new Re(o,this.computeMeanDistance(t,e))).reduce((e,o)=>e.distance<o.distance?e:o)}findBestMatch(t){const e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Re("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){const e=t.labeledDescriptors.map(o=>Ct.fromJSON(o));return new Vo(e,t.distanceThreshold)}}function wr(t){const e=new Zt;return e.extractWeights(t),e}function Oo(t,e){const{width:o,height:r}=new B(e.width,e.height);if(o<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:o,height:r})}`);if(Array.isArray(t))return t.map(n=>Oo(n,{width:o,height:r}));if(Tt(t)){const n=t.detection.forSize(o,r),s=t.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Ut(Lt(t,n),s)}return ot(t)?Lt(t,t.detection.forSize(o,r)):t instanceof G||t instanceof D?t.forSize(o,r):t}var ur="0.5.3";return Zr();})();
 //# sourceMappingURL=face-api.min.js.map
diff --git a/dist/face-api.min.js.map b/dist/face-api.min.js.map
index fc0dbd7..f57a33e 100644
--- a/dist/face-api.min.js.map
+++ b/dist/face-api.min.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
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"node_modules/@tensorflow/tfjs-core/dist/../src/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/types.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/engine.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/device_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/flags.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util_env.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/operation.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/complex.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor_ops_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/types.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io_utils.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/router_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/model_management.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/buffer.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cast.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clone.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/print.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/base_side_effects.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/browser_files.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/progress.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/weights_loader.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/http.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/passthrough.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reshape.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mat_mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/one_hot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/confusion_matrix.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/math.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/browser.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/serialization.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/test_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/version.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/globals.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floorDiv.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/abs.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add_n.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/axis_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/all.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/any.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asin.ts", 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"node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batch_to_space_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_to.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ceil.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clip_by_value.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_4d.ts", 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"node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dilation2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div_no_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/elu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/erf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expand_dims.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expm1.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tile.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/eye.ts", 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"build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/pointwiseConvLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/mobileNetV1.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/nonMaxSuppression.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/outputLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/boxPredictionLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/predictionLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/SsdMobilenetv1.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/index.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/const.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/config.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/leaky.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/convWithBatchNorm.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/depthwiseSeparableConv.ts", 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"build/src/globalApi/../../../src/globalApi/ComputeFaceDescriptorsTasks.ts", "build/src/globalApi/../../../src/globalApi/DetectFaceLandmarksTasks.ts", "build/src/globalApi/../../../src/globalApi/DetectFacesTasks.ts", "build/src/globalApi/../../../src/globalApi/detectFaces.ts", "build/src/globalApi/../../../src/globalApi/allFaces.ts", "build/src/../../src/euclideanDistance.ts", "build/src/globalApi/../../../src/globalApi/FaceMatcher.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/index.ts", "build/src/../../src/resizeResults.ts"],
-  "sourcesContent": ["// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],
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   "names": []
 }
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index 4ba1e92..24d19cd 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -1,6 +1,5 @@
 var __defineProperty = Object.defineProperty;
 var __hasOwnProperty = Object.prototype.hasOwnProperty;
-var __assign = Object.assign;
 var __commonJS = (callback, module) => () => {
   if (!module) {
     module = {exports: {}};
@@ -11,10 +10,10 @@ var __commonJS = (callback, module) => () => {
 var __markAsModule = (target) => {
   return __defineProperty(target, "__esModule", {value: true});
 };
-var __export = (target, all3) => {
+var __export = (target, all) => {
   __markAsModule(target);
-  for (var name in all3)
-    __defineProperty(target, name, {get: all3[name], enumerable: true});
+  for (var name in all)
+    __defineProperty(target, name, {get: all[name], enumerable: true});
 };
 var __exportStar = (target, module) => {
   __markAsModule(target);
@@ -31,642 +30,6 @@ var __toModule = (module) => {
   return __exportStar(__defineProperty({}, "default", {value: module, enumerable: true}), module);
 };
 
-// node_modules/seedrandom/lib/alea.js
-var require_alea = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function Alea(seed) {
-      var me = this, mash = Mash();
-      me.next = function() {
-        var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
-        me.s0 = me.s1;
-        me.s1 = me.s2;
-        return me.s2 = t - (me.c = t | 0);
-      };
-      me.c = 1;
-      me.s0 = mash(" ");
-      me.s1 = mash(" ");
-      me.s2 = mash(" ");
-      me.s0 -= mash(seed);
-      if (me.s0 < 0) {
-        me.s0 += 1;
-      }
-      me.s1 -= mash(seed);
-      if (me.s1 < 0) {
-        me.s1 += 1;
-      }
-      me.s2 -= mash(seed);
-      if (me.s2 < 0) {
-        me.s2 += 1;
-      }
-      mash = null;
-    }
-    function copy(f, t) {
-      t.c = f.c;
-      t.s0 = f.s0;
-      t.s1 = f.s1;
-      t.s2 = f.s2;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
-      prng.int32 = function() {
-        return xg.next() * 4294967296 | 0;
-      };
-      prng.double = function() {
-        return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
-      };
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    function Mash() {
-      var n = 4022871197;
-      var mash = function(data) {
-        data = data.toString();
-        for (var i = 0; i < data.length; i++) {
-          n += data.charCodeAt(i);
-          var h = 0.02519603282416938 * n;
-          n = h >>> 0;
-          h -= n;
-          h *= n;
-          n = h >>> 0;
-          h -= n;
-          n += h * 4294967296;
-        }
-        return (n >>> 0) * 23283064365386963e-26;
-      };
-      return mash;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.alea = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor128.js
-var require_xor128 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.next = function() {
-        var t = me.x ^ me.x << 11;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
-      };
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor128 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorwow.js
-var require_xorwow = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var t = me.x ^ me.x >>> 2;
-        me.x = me.y;
-        me.y = me.z;
-        me.z = me.w;
-        me.w = me.v;
-        return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
-      };
-      me.x = 0;
-      me.y = 0;
-      me.z = 0;
-      me.w = 0;
-      me.v = 0;
-      if (seed === (seed | 0)) {
-        me.x = seed;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 64; k++) {
-        me.x ^= strseed.charCodeAt(k) | 0;
-        if (k == strseed.length) {
-          me.d = me.x << 10 ^ me.x >>> 4;
-        }
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.x = f.x;
-      t.y = f.y;
-      t.z = f.z;
-      t.w = f.w;
-      t.v = f.v;
-      t.d = f.d;
-      return t;
-    }
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorwow = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xorshift7.js
-var require_xorshift7 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var X = me.x, i = me.i, t, v, w;
-        t = X[i];
-        t ^= t >>> 7;
-        v = t ^ t << 24;
-        t = X[i + 1 & 7];
-        v ^= t ^ t >>> 10;
-        t = X[i + 3 & 7];
-        v ^= t ^ t >>> 3;
-        t = X[i + 4 & 7];
-        v ^= t ^ t << 7;
-        t = X[i + 7 & 7];
-        t = t ^ t << 13;
-        v ^= t ^ t << 9;
-        X[i] = v;
-        me.i = i + 1 & 7;
-        return v;
-      };
-      function init(me2, seed2) {
-        var j, w, X = [];
-        if (seed2 === (seed2 | 0)) {
-          w = X[0] = seed2;
-        } else {
-          seed2 = "" + seed2;
-          for (j = 0; j < seed2.length; ++j) {
-            X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
-          }
-        }
-        while (X.length < 8)
-          X.push(0);
-        for (j = 0; j < 8 && X[j] === 0; ++j)
-          ;
-        if (j == 8)
-          w = X[7] = -1;
-        else
-          w = X[j];
-        me2.x = X;
-        me2.i = 0;
-        for (j = 256; j > 0; --j) {
-          me2.next();
-        }
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.x = f.x.slice();
-      t.i = f.i;
-      return t;
-    }
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.x)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xorshift7 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/xor4096.js
-var require_xor4096 = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this;
-      me.next = function() {
-        var w = me.w, X = me.X, i = me.i, t, v;
-        me.w = w = w + 1640531527 | 0;
-        v = X[i + 34 & 127];
-        t = X[i = i + 1 & 127];
-        v ^= v << 13;
-        t ^= t << 17;
-        v ^= v >>> 15;
-        t ^= t >>> 12;
-        v = X[i] = v ^ t;
-        me.i = i;
-        return v + (w ^ w >>> 16) | 0;
-      };
-      function init(me2, seed2) {
-        var t, v, i, j, w, X = [], limit = 128;
-        if (seed2 === (seed2 | 0)) {
-          v = seed2;
-          seed2 = null;
-        } else {
-          seed2 = seed2 + "\0";
-          v = 0;
-          limit = Math.max(limit, seed2.length);
-        }
-        for (i = 0, j = -32; j < limit; ++j) {
-          if (seed2)
-            v ^= seed2.charCodeAt((j + 32) % seed2.length);
-          if (j === 0)
-            w = v;
-          v ^= v << 10;
-          v ^= v >>> 15;
-          v ^= v << 4;
-          v ^= v >>> 13;
-          if (j >= 0) {
-            w = w + 1640531527 | 0;
-            t = X[j & 127] ^= v + w;
-            i = t == 0 ? i + 1 : 0;
-          }
-        }
-        if (i >= 128) {
-          X[(seed2 && seed2.length || 0) & 127] = -1;
-        }
-        i = 127;
-        for (j = 4 * 128; j > 0; --j) {
-          v = X[i + 34 & 127];
-          t = X[i = i + 1 & 127];
-          v ^= v << 13;
-          t ^= t << 17;
-          v ^= v >>> 15;
-          t ^= t >>> 12;
-          X[i] = v ^ t;
-        }
-        me2.w = w;
-        me2.X = X;
-        me2.i = i;
-      }
-      init(me, seed);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.w = f.w;
-      t.X = f.X.slice();
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      if (seed == null)
-        seed = +new Date();
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (state.X)
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.xor4096 = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/lib/tychei.js
-var require_tychei = __commonJS((exports, module) => {
-  (function(global2, module2, define2) {
-    function XorGen(seed) {
-      var me = this, strseed = "";
-      me.next = function() {
-        var b = me.b, c = me.c, d = me.d, a = me.a;
-        b = b << 25 ^ b >>> 7 ^ c;
-        c = c - d | 0;
-        d = d << 24 ^ d >>> 8 ^ a;
-        a = a - b | 0;
-        me.b = b = b << 20 ^ b >>> 12 ^ c;
-        me.c = c = c - d | 0;
-        me.d = d << 16 ^ c >>> 16 ^ a;
-        return me.a = a - b | 0;
-      };
-      me.a = 0;
-      me.b = 0;
-      me.c = 2654435769 | 0;
-      me.d = 1367130551;
-      if (seed === Math.floor(seed)) {
-        me.a = seed / 4294967296 | 0;
-        me.b = seed | 0;
-      } else {
-        strseed += seed;
-      }
-      for (var k = 0; k < strseed.length + 20; k++) {
-        me.b ^= strseed.charCodeAt(k) | 0;
-        me.next();
-      }
-    }
-    function copy(f, t) {
-      t.a = f.a;
-      t.b = f.b;
-      t.c = f.c;
-      t.d = f.d;
-      return t;
-    }
-    ;
-    function impl(seed, opts) {
-      var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
-        return (xg.next() >>> 0) / 4294967296;
-      };
-      prng.double = function() {
-        do {
-          var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
-        } while (result === 0);
-        return result;
-      };
-      prng.int32 = xg.next;
-      prng.quick = prng;
-      if (state) {
-        if (typeof state == "object")
-          copy(state, xg);
-        prng.state = function() {
-          return copy(xg, {});
-        };
-      }
-      return prng;
-    }
-    if (module2 && module2.exports) {
-      module2.exports = impl;
-    } else if (define2 && define2.amd) {
-      define2(function() {
-        return impl;
-      });
-    } else {
-      this.tychei = impl;
-    }
-  })(exports, typeof module == "object" && module, typeof define == "function" && define);
-});
-
-// node_modules/seedrandom/seedrandom.js
-var require_seedrandom = __commonJS((exports, module) => {
-  (function(pool3, math) {
-    var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
-    function seedrandom2(seed, options, callback) {
-      var key = [];
-      options = options == true ? {entropy: true} : options || {};
-      var shortseed = mixkey(flatten2(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
-      var arc4 = new ARC4(key);
-      var prng = function() {
-        var n = arc4.g(chunks), d = startdenom, x = 0;
-        while (n < significance) {
-          n = (n + x) * width;
-          d *= width;
-          x = arc4.g(1);
-        }
-        while (n >= overflow) {
-          n /= 2;
-          d /= 2;
-          x >>>= 1;
-        }
-        return (n + x) / d;
-      };
-      prng.int32 = function() {
-        return arc4.g(4) | 0;
-      };
-      prng.quick = function() {
-        return arc4.g(4) / 4294967296;
-      };
-      prng.double = prng;
-      mixkey(tostring(arc4.S), pool3);
-      return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
-        if (state) {
-          if (state.S) {
-            copy(state, arc4);
-          }
-          prng2.state = function() {
-            return copy(arc4, {});
-          };
-        }
-        if (is_math_call) {
-          math[rngname] = prng2;
-          return seed2;
-        } else
-          return prng2;
-      })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
-    }
-    math["seed" + rngname] = seedrandom2;
-    function ARC4(key) {
-      var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
-      if (!keylen) {
-        key = [keylen++];
-      }
-      while (i < width) {
-        s[i] = i++;
-      }
-      for (i = 0; i < width; i++) {
-        s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
-        s[j] = t;
-      }
-      (me.g = function(count) {
-        var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
-        while (count--) {
-          t2 = s2[i2 = mask & i2 + 1];
-          r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
-        }
-        me.i = i2;
-        me.j = j2;
-        return r;
-      })(width);
-    }
-    function copy(f, t) {
-      t.i = f.i;
-      t.j = f.j;
-      t.S = f.S.slice();
-      return t;
-    }
-    ;
-    function flatten2(obj, depth) {
-      var result = [], typ = typeof obj, prop;
-      if (depth && typ == "object") {
-        for (prop in obj) {
-          try {
-            result.push(flatten2(obj[prop], depth - 1));
-          } catch (e) {
-          }
-        }
-      }
-      return result.length ? result : typ == "string" ? obj : obj + "\0";
-    }
-    function mixkey(seed, key) {
-      var stringseed = seed + "", smear, j = 0;
-      while (j < stringseed.length) {
-        key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
-      }
-      return tostring(key);
-    }
-    function autoseed() {
-      try {
-        var out;
-        if (nodecrypto && (out = nodecrypto.randomBytes)) {
-          out = out(width);
-        } else {
-          out = new Uint8Array(width);
-          (global2.crypto || global2.msCrypto).getRandomValues(out);
-        }
-        return tostring(out);
-      } catch (e) {
-        var browser2 = global2.navigator, plugins = browser2 && browser2.plugins;
-        return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
-      }
-    }
-    function tostring(a) {
-      return String.fromCharCode.apply(0, a);
-    }
-    mixkey(math.random(), pool3);
-    if (typeof module == "object" && module.exports) {
-      module.exports = seedrandom2;
-      try {
-        nodecrypto = require("crypto");
-      } catch (ex) {
-      }
-    } else if (typeof define == "function" && define.amd) {
-      define(function() {
-        return seedrandom2;
-      });
-    }
-  })([], Math);
-});
-
-// node_modules/seedrandom/index.js
-var require_seedrandom2 = __commonJS((exports, module) => {
-  var alea2 = require_alea();
-  var xor128 = require_xor128();
-  var xorwow = require_xorwow();
-  var xorshift7 = require_xorshift7();
-  var xor4096 = require_xor4096();
-  var tychei = require_tychei();
-  var sr = require_seedrandom();
-  sr.alea = alea2;
-  sr.xor128 = xor128;
-  sr.xorwow = xorwow;
-  sr.xorshift7 = xorshift7;
-  sr.xor4096 = xor4096;
-  sr.tychei = tychei;
-  module.exports = sr;
-});
-
 // build/src/env/isNodejs.js
 var require_isNodejs = __commonJS((exports, module) => {
   __export(exports, {
@@ -677,19257 +40,6 @@ var require_isNodejs = __commonJS((exports, module) => {
   }
 });
 
-// node_modules/@tensorflow/tfjs-core/dist/environment.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
-class Environment {
-  constructor(global2) {
-    this.global = global2;
-    this.flags = {};
-    this.flagRegistry = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  setPlatform(platformName, platform) {
-    if (this.platform != null) {
-      console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
-    }
-    this.platformName = platformName;
-    this.platform = platform;
-  }
-  registerFlag(flagName, evaluationFn, setHook) {
-    this.flagRegistry[flagName] = {evaluationFn, setHook};
-    if (this.urlFlags[flagName] != null) {
-      const flagValue = this.urlFlags[flagName];
-      console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
-      this.set(flagName, flagValue);
-    }
-  }
-  async getAsync(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    this.flags[flagName] = await this.evaluateFlag(flagName);
-    return this.flags[flagName];
-  }
-  get(flagName) {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-    const flagValue = this.evaluateFlag(flagName);
-    if (flagValue instanceof Promise) {
-      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
-    }
-    this.flags[flagName] = flagValue;
-    return this.flags[flagName];
-  }
-  getNumber(flagName) {
-    return this.get(flagName);
-  }
-  getBool(flagName) {
-    return this.get(flagName);
-  }
-  getFlags() {
-    return this.flags;
-  }
-  get features() {
-    return this.flags;
-  }
-  set(flagName, value) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
-    }
-    this.flags[flagName] = value;
-    if (this.flagRegistry[flagName].setHook != null) {
-      this.flagRegistry[flagName].setHook(value);
-    }
-  }
-  evaluateFlag(flagName) {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
-    }
-    return this.flagRegistry[flagName].evaluationFn();
-  }
-  setFlags(flags3) {
-    this.flags = Object.assign({}, flags3);
-  }
-  reset() {
-    this.flags = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-  populateURLFlags() {
-    if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
-      return;
-    }
-    const urlParams = getQueryParams(this.global.location.search);
-    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
-      keyValues.forEach((keyValue) => {
-        const [key, value] = keyValue.split(":");
-        this.urlFlags[key] = parseValue(key, value);
-      });
-    }
-  }
-}
-function getQueryParams(queryString) {
-  const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
-    decodeParam(params, t[0], t[1]);
-    return t.join("=");
-  });
-  return params;
-}
-function decodeParam(params, name, value) {
-  params[decodeURIComponent(name)] = decodeURIComponent(value || "");
-}
-function parseValue(flagName, value) {
-  value = value.toLowerCase();
-  if (value === "true" || value === "false") {
-    return value === "true";
-  } else if (`${+value}` === value) {
-    return +value;
-  }
-  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
-}
-function env() {
-  return ENV;
-}
-let ENV = null;
-function setEnvironmentGlobal(environment12) {
-  ENV = environment12;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/global_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let globalNameSpace;
-function getGlobalNamespace() {
-  if (globalNameSpace == null) {
-    let ns;
-    if (typeof window !== "undefined") {
-      ns = window;
-    } else if (typeof global !== "undefined") {
-      ns = global;
-    } else if (typeof process !== "undefined") {
-      ns = process;
-    } else if (typeof self !== "undefined") {
-      ns = self;
-    } else {
-      throw new Error("Could not find a global object");
-    }
-    globalNameSpace = ns;
-  }
-  return globalNameSpace;
-}
-function getGlobalMap() {
-  const ns = getGlobalNamespace();
-  if (ns._tfGlobals == null) {
-    ns._tfGlobals = new Map();
-  }
-  return ns._tfGlobals;
-}
-function getGlobal(key, init) {
-  const globalMap = getGlobalMap();
-  if (globalMap.has(key)) {
-    return globalMap.get(key);
-  } else {
-    const singleton = init();
-    globalMap.set(key, singleton);
-    return globalMap.get(key);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
-const Abs = "Abs";
-const Acos = "Acos";
-const Acosh = "Acosh";
-const Add = "Add";
-const AddN = "AddN";
-const All = "All";
-const Any = "Any";
-const ArgMax = "ArgMax";
-const ArgMin = "ArgMin";
-const Asin = "Asin";
-const Asinh = "Asinh";
-const Atan = "Atan";
-const Atanh = "Atanh";
-const Atan2 = "Atan2";
-const AvgPool = "AvgPool";
-const AvgPoolBackprop = "AvgPoolBackprop";
-const AvgPool3D = "AvgPool3D";
-const AvgPool3DBackprop = "AvgPool3DBackprop";
-const BatchMatMul = "BatchMatMul";
-const BatchToSpaceND = "BatchToSpaceND";
-const BroadcastTo = "BroadcastTo";
-const Cast = "Cast";
-const Ceil = "Ceil";
-const ClipByValue = "ClipByValue";
-const Complex = "Complex";
-const Concat = "Concat";
-const Conv2D = "Conv2D";
-const Conv2DBackpropFilter = "Conv2DBackpropFilter";
-const Conv2DBackpropInput = "Conv2DBackpropInput";
-const Conv3D = "Conv3D";
-const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
-const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
-const Cos = "Cos";
-const Cosh = "Cosh";
-const Cumsum = "Cumsum";
-const CropAndResize = "CropAndResize";
-const DepthToSpace = "DepthToSpace";
-const DepthwiseConv2dNative = "DepthwiseConv2dNative";
-const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
-const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
-const Diag = "Diag";
-const Dilation2D = "Dilation2D";
-const Dilation2DBackpropInput = "Dilation2DBackpropInput";
-const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
-const Div = "Div";
-const Elu = "Elu";
-const EluGrad = "EluGrad";
-const Erf = "Erf";
-const Equal = "Equal";
-const Exp = "Exp";
-const Expm1 = "Expm1";
-const FFT = "FFT";
-const Fill = "Fill";
-const FlipLeftRight = "FlipLeftRight";
-const Floor = "Floor";
-const FloorDiv = "FloorDiv";
-const FusedBatchNorm = "FusedBatchNorm";
-const GatherV2 = "GatherV2";
-const GatherNd = "GatherNd";
-const Greater = "Greater";
-const GreaterEqual = "GreaterEqual";
-const Identity = "Identity";
-const IFFT = "IFFT";
-const Imag = "Imag";
-const IsFinite = "IsFinite";
-const IsInf = "IsInf";
-const IsNan = "IsNan";
-const Less = "Less";
-const LessEqual = "LessEqual";
-const LinSpace = "LinSpace";
-const Log = "Log";
-const Log1p = "Log1p";
-const LogicalAnd = "LogicalAnd";
-const LogicalNot = "LogicalNot";
-const LogicalOr = "LogicalOr";
-const LogSoftmax = "LogSoftmax";
-const LRN = "LRN";
-const LRNBackprop = "LRNBackprop";
-const Max = "Max";
-const Maximum = "Maximum";
-const MaxPool = "MaxPool";
-const MaxPoolBackprop = "MaxPoolBackprop";
-const MaxPool3D = "MaxPool3D";
-const MaxPool3DBackprop = "MaxPool3DBackprop";
-const MaxPoolWithArgmax = "MaxPoolWithArgmax";
-const Mean = "Mean";
-const Min = "Min";
-const Minimum = "Minimum";
-const Mod = "Mod";
-const Multiply = "Multiply";
-const Negate = "Negate";
-const NotEqual = "NotEqual";
-const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
-const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
-const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
-const OnesLike = "OnesLike";
-const OneHot = "OneHot";
-const PadV2 = "PadV2";
-const Pool = "Pool";
-const Pow = "Pow";
-const Prelu = "Prelu";
-const Prod = "Prod";
-const Range = "Range";
-const Real = "Real";
-const Reciprocal = "Reciprocal";
-const Relu = "Relu";
-const Reshape = "Reshape";
-const ResizeNearestNeighbor = "ResizeNearestNeighbor";
-const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
-const ResizeBilinear = "ResizeBilinear";
-const ResizeBilinearGrad = "ResizeBilinearGrad";
-const Relu6 = "Relu6";
-const Reverse = "Reverse";
-const Round = "Round";
-const Rsqrt = "Rsqrt";
-const ScatterNd = "ScatterNd";
-const SelectV2 = "SelectV2";
-const Selu = "Selu";
-const Slice = "Slice";
-const Sin = "Sin";
-const Sinh = "Sinh";
-const Sign = "Sign";
-const Sigmoid = "Sigmoid";
-const Softplus = "Softplus";
-const Sqrt = "Sqrt";
-const Sum = "Sum";
-const SpaceToBatchND = "SpaceToBatchND";
-const SplitV = "SplitV";
-const Softmax = "Softmax";
-const SquaredDifference = "SquaredDifference";
-const Square = "Square";
-const Sub = "Sub";
-const SparseToDense = "SparseToDense";
-const StridedSlice = "StridedSlice";
-const Tan = "Tan";
-const Tanh = "Tanh";
-const Tile = "Tile";
-const TopK = "TopK";
-const Transpose = "Transpose";
-const Unpack = "Unpack";
-const UnsortedSegmentSum = "UnsortedSegmentSum";
-const ZerosLike = "ZerosLike";
-const Step = "Step";
-const FromPixels = "FromPixels";
-const RotateWithOffset = "RotateWithOffset";
-const _FusedMatMul = "_FusedMatMul";
-const FusedConv2D = "FusedConv2D";
-const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
-
-// node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const kernelRegistry = getGlobal("kernelRegistry", () => new Map());
-const gradRegistry = getGlobal("gradRegistry", () => new Map());
-function getKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  return kernelRegistry.get(key);
-}
-function getGradient(kernelName) {
-  return gradRegistry.get(kernelName);
-}
-function getKernelsForBackend(backendName) {
-  const it = kernelRegistry.entries();
-  const result = [];
-  while (true) {
-    const {done, value} = it.next();
-    if (done) {
-      break;
-    }
-    const [key, config2] = value;
-    const [backend2] = key.split("_");
-    if (backend2 === backendName) {
-      result.push(config2);
-    }
-  }
-  return result;
-}
-function registerKernel(config2) {
-  const {kernelName, backendName} = config2;
-  const key = makeKey(kernelName, backendName);
-  if (kernelRegistry.has(key)) {
-    console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
-  }
-  kernelRegistry.set(key, config2);
-}
-function registerGradient(config2) {
-  const {kernelName} = config2;
-  if (gradRegistry.has(kernelName)) {
-    if (env().getBool("DEBUG")) {
-      console.warn(`Overriding the gradient for '${kernelName}'`);
-    }
-  }
-  gradRegistry.set(kernelName, config2);
-}
-function unregisterKernel(kernelName, backendName) {
-  const key = makeKey(kernelName, backendName);
-  if (!kernelRegistry.has(key)) {
-    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
-  }
-  kernelRegistry.delete(key);
-}
-function unregisterGradient(kernelName) {
-  if (!gradRegistry.has(kernelName)) {
-    throw new Error(`The gradient '${kernelName}' for backend is not registered`);
-  }
-  gradRegistry.delete(kernelName);
-}
-function makeKey(kernelName, backendName) {
-  return `${backendName}_${kernelName}`;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/util.js
-const util_exports = {};
-__export(util_exports, {
-  arraysEqual: () => arraysEqual,
-  assert: () => assert,
-  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
-  assertNonNull: () => assertNonNull,
-  assertShapesMatch: () => assertShapesMatch,
-  bytesFromStringArray: () => bytesFromStringArray,
-  bytesPerElement: () => bytesPerElement,
-  checkConversionForErrors: () => checkConversionForErrors,
-  clamp: () => clamp,
-  computeStrides: () => computeStrides,
-  createShuffledIndices: () => createShuffledIndices,
-  decodeString: () => decodeString,
-  distSquared: () => distSquared,
-  encodeString: () => encodeString,
-  fetch: () => fetch2,
-  flatten: () => flatten,
-  getArrayFromDType: () => getArrayFromDType,
-  getTypedArrayFromDType: () => getTypedArrayFromDType,
-  hasEncodingLoss: () => hasEncodingLoss,
-  indexToLoc: () => indexToLoc,
-  inferDtype: () => inferDtype,
-  inferFromImplicitShape: () => inferFromImplicitShape,
-  isBoolean: () => isBoolean,
-  isFunction: () => isFunction,
-  isInt: () => isInt,
-  isNumber: () => isNumber,
-  isScalarShape: () => isScalarShape,
-  isString: () => isString,
-  isTypedArray: () => isTypedArray,
-  isValidDtype: () => isValidDtype,
-  locToIndex: () => locToIndex,
-  makeOnesTypedArray: () => makeOnesTypedArray,
-  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
-  makeZerosTypedArray: () => makeZerosTypedArray,
-  nearestDivisor: () => nearestDivisor,
-  nearestLargerEven: () => nearestLargerEven,
-  now: () => now,
-  parseAxisParam: () => parseAxisParam,
-  randUniform: () => randUniform,
-  repeatedTry: () => repeatedTry,
-  rightPad: () => rightPad,
-  shuffle: () => shuffle,
-  sizeFromShape: () => sizeFromShape,
-  sizeToSquarishShape: () => sizeToSquarishShape,
-  squeezeShape: () => squeezeShape,
-  sum: () => sum,
-  tanh: () => tanh,
-  toNestedArray: () => toNestedArray,
-  toTypedArray: () => toTypedArray
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function shuffle(array) {
-  let counter = array.length;
-  let temp = 0;
-  let index = 0;
-  while (counter > 0) {
-    index = Math.random() * counter | 0;
-    counter--;
-    temp = array[counter];
-    array[counter] = array[index];
-    array[index] = temp;
-  }
-}
-function clamp(min4, x, max6) {
-  return Math.max(min4, Math.min(x, max6));
-}
-function nearestLargerEven(val) {
-  return val % 2 === 0 ? val : val + 1;
-}
-function sum(arr) {
-  let sum26 = 0;
-  for (let i = 0; i < arr.length; i++) {
-    sum26 += arr[i];
-  }
-  return sum26;
-}
-function randUniform(a, b) {
-  const r = Math.random();
-  return b * r + (1 - r) * a;
-}
-function distSquared(a, b) {
-  let result = 0;
-  for (let i = 0; i < a.length; i++) {
-    const diff = Number(a[i]) - Number(b[i]);
-    result += diff * diff;
-  }
-  return result;
-}
-function assert(expr, msg) {
-  if (!expr) {
-    throw new Error(typeof msg === "string" ? msg : msg());
-  }
-}
-function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
-  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-}
-function assertNonNull(a) {
-  assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
-}
-function flatten(arr, result = [], skipTypedArray = false) {
-  if (result == null) {
-    result = [];
-  }
-  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-    for (let i = 0; i < arr.length; ++i) {
-      flatten(arr[i], result, skipTypedArray);
-    }
-  } else {
-    result.push(arr);
-  }
-  return result;
-}
-function sizeFromShape(shape) {
-  if (shape.length === 0) {
-    return 1;
-  }
-  let size = shape[0];
-  for (let i = 1; i < shape.length; i++) {
-    size *= shape[i];
-  }
-  return size;
-}
-function isScalarShape(shape) {
-  return shape.length === 0;
-}
-function arraysEqual(n1, n2) {
-  if (n1 === n2) {
-    return true;
-  }
-  if (n1 == null || n2 == null) {
-    return false;
-  }
-  if (n1.length !== n2.length) {
-    return false;
-  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function isInt(a) {
-  return a % 1 === 0;
-}
-function tanh(x) {
-  if (Math.tanh != null) {
-    return Math.tanh(x);
-  }
-  if (x === Infinity) {
-    return 1;
-  } else if (x === -Infinity) {
-    return -1;
-  } else {
-    const e2x = Math.exp(2 * x);
-    return (e2x - 1) / (e2x + 1);
-  }
-}
-function sizeToSquarishShape(size) {
-  const width = Math.ceil(Math.sqrt(size));
-  return [width, Math.ceil(size / width)];
-}
-function createShuffledIndices(n) {
-  const shuffledIndices = new Uint32Array(n);
-  for (let i = 0; i < n; ++i) {
-    shuffledIndices[i] = i;
-  }
-  shuffle(shuffledIndices);
-  return shuffledIndices;
-}
-function rightPad(a, size) {
-  if (size <= a.length) {
-    return a;
-  }
-  return a + " ".repeat(size - a.length);
-}
-function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
-  return new Promise((resolve, reject) => {
-    let tryCount = 0;
-    const tryFn = () => {
-      if (checkFn()) {
-        resolve();
-        return;
-      }
-      tryCount++;
-      const nextBackoff = delayFn(tryCount);
-      if (maxCounter != null && tryCount >= maxCounter) {
-        reject();
-        return;
-      }
-      setTimeout(tryFn, nextBackoff);
-    };
-    tryFn();
-  });
-}
-function inferFromImplicitShape(shape, size) {
-  let shapeProd = 1;
-  let implicitIdx = -1;
-  for (let i = 0; i < shape.length; ++i) {
-    if (shape[i] >= 0) {
-      shapeProd *= shape[i];
-    } else if (shape[i] === -1) {
-      if (implicitIdx !== -1) {
-        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
-      }
-      implicitIdx = i;
-    } else if (shape[i] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
-    }
-  }
-  if (implicitIdx === -1) {
-    if (size > 0 && size !== shapeProd) {
-      throw Error(`Size(${size}) must match the product of shape ${shape}`);
-    }
-    return shape;
-  }
-  if (shapeProd === 0) {
-    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
-  }
-  if (size % shapeProd !== 0) {
-    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
-  }
-  const newShape = shape.slice();
-  newShape[implicitIdx] = size / shapeProd;
-  return newShape;
-}
-function parseAxisParam(axis, shape) {
-  const rank = shape.length;
-  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
-  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
-  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
-  return axis.map((a) => a < 0 ? rank + a : a);
-}
-function squeezeShape(shape, axis) {
-  const newShape = [];
-  const keptDims = [];
-  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
-  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
-  let j = 0;
-  for (let i = 0; i < shape.length; ++i) {
-    if (axes != null) {
-      if (axes[j] === i && shape[i] !== 1) {
-        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
-      }
-      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
-        newShape.push(shape[i]);
-        keptDims.push(i);
-      }
-      if (axes[j] <= i) {
-        j++;
-      }
-    }
-    if (shape[i] !== 1) {
-      newShape.push(shape[i]);
-      keptDims.push(i);
-    }
-  }
-  return {newShape, keptDims};
-}
-function getTypedArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function getArrayFromDType(dtype, size) {
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else if (dtype === "string") {
-    values = new Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values;
-}
-function checkConversionForErrors(vals, dtype) {
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
-    }
-  }
-}
-function isValidDtype(dtype) {
-  return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
-}
-function hasEncodingLoss(oldType, newType) {
-  if (newType === "complex64") {
-    return false;
-  }
-  if (newType === "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
-    return false;
-  }
-  if (newType === "bool" && oldType === "bool") {
-    return false;
-  }
-  return true;
-}
-function isTypedArray(a) {
-  return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
-}
-function bytesPerElement(dtype) {
-  if (dtype === "float32" || dtype === "int32") {
-    return 4;
-  } else if (dtype === "complex64") {
-    return 8;
-  } else if (dtype === "bool") {
-    return 1;
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-function bytesFromStringArray(arr) {
-  if (arr == null) {
-    return 0;
-  }
-  let bytes = 0;
-  arr.forEach((x) => bytes += x.length);
-  return bytes;
-}
-function isString(value) {
-  return typeof value === "string" || value instanceof String;
-}
-function isBoolean(value) {
-  return typeof value === "boolean";
-}
-function isNumber(value) {
-  return typeof value === "number";
-}
-function inferDtype(values) {
-  if (Array.isArray(values)) {
-    return inferDtype(values[0]);
-  }
-  if (values instanceof Float32Array) {
-    return "float32";
-  } else if (values instanceof Int32Array || values instanceof Uint8Array) {
-    return "int32";
-  } else if (isNumber(values)) {
-    return "float32";
-  } else if (isString(values)) {
-    return "string";
-  } else if (isBoolean(values)) {
-    return "bool";
-  }
-  return "float32";
-}
-function isFunction(f) {
-  return !!(f && f.constructor && f.call && f.apply);
-}
-function nearestDivisor(size, start) {
-  for (let i = start; i < size; ++i) {
-    if (size % i === 0) {
-      return i;
-    }
-  }
-  return size;
-}
-function computeStrides(shape) {
-  const rank = shape.length;
-  if (rank < 2) {
-    return [];
-  }
-  const strides = new Array(rank - 1);
-  strides[rank - 2] = shape[rank - 1];
-  for (let i = rank - 3; i >= 0; --i) {
-    strides[i] = strides[i + 1] * shape[i + 1];
-  }
-  return strides;
-}
-function toTypedArray(a, dtype) {
-  if (dtype === "string") {
-    throw new Error("Cannot convert a string[] to a TypedArray");
-  }
-  if (Array.isArray(a)) {
-    a = flatten(a);
-  }
-  if (env().getBool("DEBUG")) {
-    checkConversionForErrors(a, dtype);
-  }
-  if (noConversionNeeded(a, dtype)) {
-    return a;
-  }
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(a);
-  } else if (dtype === "int32") {
-    return new Int32Array(a);
-  } else if (dtype === "bool") {
-    const bool = new Uint8Array(a.length);
-    for (let i = 0; i < bool.length; ++i) {
-      if (Math.round(a[i]) !== 0) {
-        bool[i] = 1;
-      }
-    }
-    return bool;
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function createNestedArray(offset, shape, a) {
-  const ret = new Array();
-  if (shape.length === 1) {
-    const d = shape[0];
-    for (let i = 0; i < d; i++) {
-      ret[i] = a[offset + i];
-    }
-  } else {
-    const d = shape[0];
-    const rest = shape.slice(1);
-    const len = rest.reduce((acc, c) => acc * c);
-    for (let i = 0; i < d; i++) {
-      ret[i] = createNestedArray(offset + i * len, rest, a);
-    }
-  }
-  return ret;
-}
-function toNestedArray(shape, a) {
-  if (shape.length === 0) {
-    return a[0];
-  }
-  const size = shape.reduce((acc, c) => acc * c);
-  if (size === 0) {
-    return [];
-  }
-  if (size !== a.length) {
-    throw new Error(`[${shape}] does not match the input size ${a.length}.`);
-  }
-  return createNestedArray(0, shape, a);
-}
-function noConversionNeeded(a, dtype) {
-  return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
-}
-function makeOnesTypedArray(size, dtype) {
-  const array = makeZerosTypedArray(size, dtype);
-  for (let i = 0; i < array.length; i++) {
-    array[i] = 1;
-  }
-  return array;
-}
-function makeZerosTypedArray(size, dtype) {
-  if (dtype == null || dtype === "float32" || dtype === "complex64") {
-    return new Float32Array(size);
-  } else if (dtype === "int32") {
-    return new Int32Array(size);
-  } else if (dtype === "bool") {
-    return new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function makeZerosNestedTypedArray(shape, dtype) {
-  const size = shape.reduce((prev, curr) => prev * curr, 1);
-  if (dtype == null || dtype === "float32") {
-    return toNestedArray(shape, new Float32Array(size));
-  } else if (dtype === "int32") {
-    return toNestedArray(shape, new Int32Array(size));
-  } else if (dtype === "bool") {
-    return toNestedArray(shape, new Uint8Array(size));
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-function now() {
-  return env().platform.now();
-}
-function assertNonNegativeIntegerDimensions(shape) {
-  shape.forEach((dimSize) => {
-    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
-  });
-}
-function fetch2(path, requestInits) {
-  return env().platform.fetch(path, requestInits);
-}
-function encodeString(s, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.encode(s, encoding);
-}
-function decodeString(bytes, encoding = "utf-8") {
-  encoding = encoding || "utf-8";
-  return env().platform.decode(bytes, encoding);
-}
-function locToIndex(locs, rank, strides) {
-  if (rank === 0) {
-    return 0;
-  } else if (rank === 1) {
-    return locs[0];
-  }
-  let index = locs[locs.length - 1];
-  for (let i = 0; i < locs.length - 1; ++i) {
-    index += strides[i] * locs[i];
-  }
-  return index;
-}
-function indexToLoc(index, rank, strides) {
-  if (rank === 0) {
-    return [];
-  } else if (rank === 1) {
-    return [index];
-  }
-  const locs = new Array(rank);
-  for (let i = 0; i < locs.length - 1; ++i) {
-    locs[i] = Math.floor(index / strides[i]);
-    index -= locs[i] * strides[i];
-  }
-  locs[locs.length - 1] = index;
-  return locs;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/profiler.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Profiler {
-  constructor(backendTimer, logger) {
-    this.backendTimer = backendTimer;
-    this.logger = logger;
-    if (logger == null) {
-      this.logger = new Logger();
-    }
-  }
-  profileKernel(kernelName, inputs, f) {
-    let outputs;
-    const holdResultWrapperFn = () => {
-      outputs = f();
-    };
-    const timer = this.backendTimer.time(holdResultWrapperFn);
-    for (let i = 0; i < outputs.length; i++) {
-      const output = outputs[i];
-      output.data().then((tensorVals) => {
-        checkComputationForErrors(tensorVals, output.dtype, kernelName);
-      });
-    }
-    const kernelProfile = {
-      kernelName,
-      outputs,
-      inputs,
-      timeMs: timer.then((timing) => timing.kernelMs),
-      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
-    };
-    return kernelProfile;
-  }
-  logKernelProfile(kernelProfile) {
-    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
-    outputs.forEach((result) => {
-      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
-        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
-      });
-    });
-  }
-}
-function checkComputationForErrors(vals, dtype, kernelName) {
-  if (dtype !== "float32") {
-    return false;
-  }
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i];
-    if (isNaN(num) || !isFinite(num)) {
-      console.warn(`Found ${num} in the result of '${kernelName}'`);
-      return true;
-    }
-  }
-  return false;
-}
-class Logger {
-  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
-    const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
-    const paddedName = rightPad(name, 25);
-    const rank = result.rank;
-    const size = result.size;
-    const shape = rightPad(result.shape.toString(), 14);
-    let inputShapesDescription = "";
-    for (const name2 in inputs) {
-      const input = inputs[name2];
-      if (input != null) {
-        const inputShape = input.shape || result.shape;
-        const inputRank = inputShape.length;
-        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
-      }
-    }
-    console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tape.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFilteredNodesXToY(tape2, xs, y) {
-  const tensorsFromX = {};
-  const nodesFromX = {};
-  for (let i = 0; i < xs.length; i++) {
-    tensorsFromX[xs[i].id] = true;
-  }
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (const inputName in nodeInputs) {
-      const input = nodeInputs[inputName];
-      let anyInputFromX = false;
-      for (let j = 0; j < xs.length; j++) {
-        if (tensorsFromX[input.id]) {
-          node2.outputs.forEach((output) => tensorsFromX[output.id] = true);
-          anyInputFromX = true;
-          nodesFromX[node2.id] = true;
-          break;
-        }
-      }
-      if (anyInputFromX) {
-        break;
-      }
-    }
-  }
-  const tensorsLeadToY = {};
-  tensorsLeadToY[y.id] = true;
-  const nodesToY = {};
-  for (let i = tape2.length - 1; i >= 0; i--) {
-    const node2 = tape2[i];
-    const nodeInputs = node2.inputs;
-    for (let j = 0; j < node2.outputs.length; j++) {
-      if (tensorsLeadToY[node2.outputs[j].id]) {
-        for (const inputName in nodeInputs) {
-          tensorsLeadToY[nodeInputs[inputName].id] = true;
-          nodesToY[node2.id] = true;
-        }
-        break;
-      }
-    }
-  }
-  const filteredTape = [];
-  for (let i = 0; i < tape2.length; i++) {
-    const node2 = tape2[i];
-    if (nodesFromX[node2.id] && nodesToY[node2.id]) {
-      const prunedInputs = {};
-      for (const inputName in node2.inputs) {
-        const nodeInput = node2.inputs[inputName];
-        if (tensorsFromX[nodeInput.id]) {
-          prunedInputs[inputName] = nodeInput;
-        }
-      }
-      const prunedNode = Object.assign({}, node2);
-      prunedNode.inputs = prunedInputs;
-      prunedNode.outputs = node2.outputs;
-      filteredTape.push(prunedNode);
-    }
-  }
-  return filteredTape;
-}
-function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add29) {
-  for (let i = filteredTape.length - 1; i >= 0; i--) {
-    const node2 = filteredTape[i];
-    const dys = [];
-    node2.outputs.forEach((o) => {
-      const gradTensor = tensorAccumulatedGradientMap[o.id];
-      if (gradTensor != null) {
-        dys.push(gradTensor);
-      } else {
-        dys.push(null);
-      }
-    });
-    if (node2.gradient == null) {
-      throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);
-    }
-    const inputGradients = node2.gradient(dys);
-    for (const inputName in node2.inputs) {
-      if (!(inputName in inputGradients)) {
-        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
-      }
-      const dx = tidy2(() => inputGradients[inputName]());
-      if (dx.dtype !== "float32") {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
-      }
-      const x = node2.inputs[inputName];
-      if (!arraysEqual(dx.shape, x.shape)) {
-        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
-      }
-      if (tensorAccumulatedGradientMap[x.id] == null) {
-        tensorAccumulatedGradientMap[x.id] = dx;
-      } else {
-        const curGradient = tensorAccumulatedGradientMap[x.id];
-        tensorAccumulatedGradientMap[x.id] = add29(curGradient, dx);
-        curGradient.dispose();
-      }
-    }
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const FORMAT_LIMIT_NUM_VALS = 20;
-const FORMAT_NUM_FIRST_LAST_VALS = 3;
-const FORMAT_NUM_SIG_DIGITS = 7;
-function tensorToString(vals, shape, dtype, verbose) {
-  const strides = computeStrides(shape);
-  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
-  const rank = shape.length;
-  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
-  const lines = ["Tensor"];
-  if (verbose) {
-    lines.push(`  dtype: ${dtype}`);
-    lines.push(`  rank: ${rank}`);
-    lines.push(`  shape: [${shape}]`);
-    lines.push(`  values:`);
-  }
-  lines.push(valsLines.map((l) => "    " + l).join("\n"));
-  return lines.join("\n");
-}
-function computeMaxSizePerColumn(vals, shape, dtype, strides) {
-  const n = sizeFromShape(shape);
-  const numCols = strides[strides.length - 1];
-  const padPerCol = new Array(numCols).fill(0);
-  const rank = shape.length;
-  const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
-  if (rank > 1) {
-    for (let row = 0; row < n / numCols; row++) {
-      const offset = row * numCols;
-      for (let j = 0; j < numCols; j++) {
-        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
-      }
-    }
-  }
-  return padPerCol;
-}
-function valToString(val, pad8, dtype) {
-  let valStr;
-  if (Array.isArray(val)) {
-    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
-  } else if (isString(val)) {
-    valStr = `'${val}'`;
-  } else if (dtype === "bool") {
-    valStr = boolNumToString(val);
-  } else {
-    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
-  }
-  return rightPad(valStr, pad8);
-}
-function boolNumToString(v) {
-  return v === 0 ? "false" : "true";
-}
-function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
-  const storagePerElement = dtype === "complex64" ? 2 : 1;
-  const size = shape[0];
-  const rank = shape.length;
-  if (rank === 0) {
-    if (dtype === "complex64") {
-      const complexTuple = createComplexTuples(vals);
-      return [valToString(complexTuple[0], 0, dtype)];
-    }
-    if (dtype === "bool") {
-      return [boolNumToString(vals[0])];
-    }
-    return [vals[0].toString()];
-  }
-  if (rank === 1) {
-    if (size > FORMAT_LIMIT_NUM_VALS) {
-      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
-      let firstVals = Array.from(vals.slice(0, firstValsSize));
-      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
-      if (dtype === "complex64") {
-        firstVals = createComplexTuples(firstVals);
-        lastVals = createComplexTuples(lastVals);
-      }
-      return [
-        "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
-      ];
-    }
-    const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
-    return [
-      "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
-    ];
-  }
-  const subshape = shape.slice(1);
-  const substrides = strides.slice(1);
-  const stride = strides[0] * storagePerElement;
-  const lines = [];
-  if (size > FORMAT_LIMIT_NUM_VALS) {
-    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
-    }
-    lines.push("...");
-    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  } else {
-    for (let i = 0; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
-    }
-  }
-  const sep = rank === 2 ? "," : "";
-  lines[0] = "[" + lines[0] + sep;
-  for (let i = 1; i < lines.length - 1; i++) {
-    lines[i] = " " + lines[i] + sep;
-  }
-  let newLineSep = ",\n";
-  for (let i = 2; i < rank; i++) {
-    newLineSep += "\n";
-  }
-  lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
-  return lines;
-}
-function createComplexTuples(vals) {
-  const complexTuples = [];
-  for (let i = 0; i < vals.length; i += 2) {
-    complexTuples.push([vals[i], vals[i + 1]]);
-  }
-  return complexTuples;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class TensorBuffer {
-  constructor(shape, dtype, values) {
-    this.dtype = dtype;
-    this.shape = shape.slice();
-    this.size = sizeFromShape(shape);
-    if (values != null) {
-      const n = values.length;
-      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
-    }
-    if (dtype === "complex64") {
-      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
-    }
-    this.values = values || getArrayFromDType(dtype, this.size);
-    this.strides = computeStrides(shape);
-  }
-  set(value, ...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
-    const index = this.locToIndex(locs);
-    this.values[index] = value;
-  }
-  get(...locs) {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    let i = 0;
-    for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i]) {
-        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
-        throw new Error(msg);
-      }
-      i++;
-    }
-    let index = locs[locs.length - 1];
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      index += this.strides[i2] * locs[i2];
-    }
-    return this.values[index];
-  }
-  locToIndex(locs) {
-    if (this.rank === 0) {
-      return 0;
-    } else if (this.rank === 1) {
-      return locs[0];
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += this.strides[i] * locs[i];
-    }
-    return index;
-  }
-  indexToLoc(index) {
-    if (this.rank === 0) {
-      return [];
-    } else if (this.rank === 1) {
-      return [index];
-    }
-    const locs = new Array(this.shape.length);
-    for (let i = 0; i < locs.length - 1; ++i) {
-      locs[i] = Math.floor(index / this.strides[i]);
-      index -= locs[i] * this.strides[i];
-    }
-    locs[locs.length - 1] = index;
-    return locs;
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  toTensor() {
-    return trackerFn().makeTensor(this.values, this.shape, this.dtype);
-  }
-}
-let trackerFn = null;
-let opHandler = null;
-let deprecationWarningFn = null;
-function setTensorTracker(fn) {
-  trackerFn = fn;
-}
-function setOpHandler(handler) {
-  opHandler = handler;
-}
-function setDeprecationWarningFn(fn) {
-  deprecationWarningFn = fn;
-}
-class Tensor {
-  constructor(shape, dtype, dataId, id) {
-    this.kept = false;
-    this.isDisposedInternal = false;
-    this.shape = shape.slice();
-    this.dtype = dtype || "float32";
-    this.size = sizeFromShape(shape);
-    this.strides = computeStrides(shape);
-    this.dataId = dataId;
-    this.id = id;
-    this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
-  }
-  get rank() {
-    return this.shape.length;
-  }
-  async buffer() {
-    const vals = await this.data();
-    return opHandler.buffer(this.shape, this.dtype, vals);
-  }
-  bufferSync() {
-    return opHandler.buffer(this.shape, this.dtype, this.dataSync());
-  }
-  async array() {
-    const vals = await this.data();
-    return toNestedArray(this.shape, vals);
-  }
-  arraySync() {
-    return toNestedArray(this.shape, this.dataSync());
-  }
-  async data() {
-    this.throwIfDisposed();
-    const data = trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      const bytes = await data;
-      try {
-        return bytes.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  dataSync() {
-    this.throwIfDisposed();
-    const data = trackerFn().readSync(this.dataId);
-    if (this.dtype === "string") {
-      try {
-        return data.map((b) => decodeString(b));
-      } catch (_a) {
-        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
-      }
-    }
-    return data;
-  }
-  async bytes() {
-    this.throwIfDisposed();
-    const data = await trackerFn().read(this.dataId);
-    if (this.dtype === "string") {
-      return data;
-    } else {
-      return new Uint8Array(data.buffer);
-    }
-  }
-  dispose() {
-    if (this.isDisposed) {
-      return;
-    }
-    trackerFn().disposeTensor(this);
-    this.isDisposedInternal = true;
-  }
-  get isDisposed() {
-    return this.isDisposedInternal;
-  }
-  throwIfDisposed() {
-    if (this.isDisposed) {
-      throw new Error(`Tensor is disposed.`);
-    }
-  }
-  print(verbose = false) {
-    return opHandler.print(this, verbose);
-  }
-  clone() {
-    this.throwIfDisposed();
-    return opHandler.clone(this);
-  }
-  toString(verbose = false) {
-    const vals = this.dataSync();
-    return tensorToString(vals, this.shape, this.dtype, verbose);
-  }
-  cast(dtype) {
-    this.throwIfDisposed();
-    return opHandler.cast(this, dtype);
-  }
-  variable(trainable = true, name, dtype) {
-    this.throwIfDisposed();
-    return trackerFn().makeVariable(this, trainable, name, dtype);
-  }
-}
-Object.defineProperty(Tensor, Symbol.hasInstance, {
-  value: (instance) => {
-    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
-  }
-});
-class Variable extends Tensor {
-  constructor(initialValue, trainable, name, tensorId) {
-    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
-    this.trainable = trainable;
-    this.name = name;
-  }
-  assign(newValue) {
-    if (newValue.dtype !== this.dtype) {
-      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
-    }
-    if (!arraysEqual(newValue.shape, this.shape)) {
-      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
-    }
-    trackerFn().disposeTensor(this);
-    this.dataId = newValue.dataId;
-    trackerFn().incRef(this, null);
-  }
-  dispose() {
-    trackerFn().disposeVariable(this);
-    this.isDisposedInternal = true;
-  }
-}
-Object.defineProperty(Variable, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/types.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Rank;
-(function(Rank2) {
-  Rank2["R0"] = "R0";
-  Rank2["R1"] = "R1";
-  Rank2["R2"] = "R2";
-  Rank2["R3"] = "R3";
-  Rank2["R4"] = "R4";
-  Rank2["R5"] = "R5";
-  Rank2["R6"] = "R6";
-})(Rank || (Rank = {}));
-var UpcastInt32AndMap;
-(function(UpcastInt32AndMap2) {
-  UpcastInt32AndMap2["float32"] = "float32";
-  UpcastInt32AndMap2["int32"] = "int32";
-  UpcastInt32AndMap2["bool"] = "int32";
-  UpcastInt32AndMap2["complex64"] = "complex64";
-})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
-var UpcastBoolAndMap;
-(function(UpcastBoolAndMap2) {
-  UpcastBoolAndMap2["float32"] = "float32";
-  UpcastBoolAndMap2["int32"] = "int32";
-  UpcastBoolAndMap2["bool"] = "bool";
-  UpcastBoolAndMap2["complex64"] = "complex64";
-})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
-var UpcastFloat32AndMap;
-(function(UpcastFloat32AndMap2) {
-  UpcastFloat32AndMap2["float32"] = "float32";
-  UpcastFloat32AndMap2["int32"] = "float32";
-  UpcastFloat32AndMap2["bool"] = "float32";
-  UpcastFloat32AndMap2["complex64"] = "complex64";
-})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
-var UpcastComplex64AndMap;
-(function(UpcastComplex64AndMap2) {
-  UpcastComplex64AndMap2["float32"] = "complex64";
-  UpcastComplex64AndMap2["int32"] = "complex64";
-  UpcastComplex64AndMap2["bool"] = "complex64";
-  UpcastComplex64AndMap2["complex64"] = "complex64";
-})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
-const upcastTypeMap = {
-  float32: UpcastFloat32AndMap,
-  int32: UpcastInt32AndMap,
-  bool: UpcastBoolAndMap,
-  complex64: UpcastComplex64AndMap
-};
-function upcastType(typeA, typeB) {
-  if (typeA === "string" || typeB === "string") {
-    if (typeA === "string" && typeB === "string") {
-      return "string";
-    }
-    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
-  }
-  return upcastTypeMap[typeA][typeB];
-}
-function sumOutType(type) {
-  return upcastType(type, "int32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
-const tensor_util_exports = {};
-__export(tensor_util_exports, {
-  assertTypesMatch: () => assertTypesMatch,
-  getTensorsInContainer: () => getTensorsInContainer,
-  isTensorInList: () => isTensorInList,
-  makeTypesMatch: () => makeTypesMatch
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTypesMatch(a, b) {
-  if (a.dtype === b.dtype) {
-    return [a, b];
-  }
-  const dtype = upcastType(a.dtype, b.dtype);
-  return [a.cast(dtype), b.cast(dtype)];
-}
-function assertTypesMatch(a, b) {
-  assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
-}
-function isTensorInList(tensor17, tensorList) {
-  return tensorList.some((x) => x.id === tensor17.id);
-}
-function getTensorsInContainer(result) {
-  const list = [];
-  const seen = new Set();
-  walkTensorContainer(result, list, seen);
-  return list;
-}
-function walkTensorContainer(container, list, seen) {
-  if (container == null) {
-    return;
-  }
-  if (container instanceof Tensor) {
-    list.push(container);
-    return;
-  }
-  if (!isIterable(container)) {
-    return;
-  }
-  const iterable = container;
-  for (const k in iterable) {
-    const val = iterable[k];
-    if (!seen.has(val)) {
-      seen.add(val);
-      walkTensorContainer(val, list, seen);
-    }
-  }
-}
-function isIterable(obj) {
-  return Array.isArray(obj) || typeof obj === "object";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/engine.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class EngineState {
-  constructor() {
-    this.registeredVariables = {};
-    this.nextTapeNodeId = 0;
-    this.numBytes = 0;
-    this.numTensors = 0;
-    this.numStringTensors = 0;
-    this.numDataBuffers = 0;
-    this.gradientDepth = 0;
-    this.kernelDepth = 0;
-    this.scopeStack = [];
-    this.numDataMovesStack = [];
-    this.nextScopeId = 0;
-    this.tensorInfo = new WeakMap();
-    this.profiling = false;
-    this.activeProfile = {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};
-  }
-  dispose() {
-    for (const variableName in this.registeredVariables) {
-      this.registeredVariables[variableName].dispose();
-    }
-  }
-}
-class Engine {
-  constructor(ENV3) {
-    this.ENV = ENV3;
-    this.registry = {};
-    this.registryFactory = {};
-    this.pendingBackendInitId = 0;
-    this.state = new EngineState();
-  }
-  async ready() {
-    if (this.pendingBackendInit != null) {
-      return this.pendingBackendInit.then(() => {
-      });
-    }
-    if (this.backendInstance != null) {
-      return;
-    }
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const success = await this.initializeBackend(backendName).success;
-      if (success) {
-        await this.setBackend(backendName);
-        return;
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  get backend() {
-    if (this.pendingBackendInit != null) {
-      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-    }
-    if (this.backendInstance == null) {
-      const {name, asyncInit} = this.initializeBackendsAndReturnBest();
-      if (asyncInit) {
-        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
-      }
-      this.setBackend(name);
-    }
-    return this.backendInstance;
-  }
-  backendNames() {
-    return Object.keys(this.registryFactory);
-  }
-  findBackend(backendName) {
-    if (!(backendName in this.registry)) {
-      if (backendName in this.registryFactory) {
-        const {asyncInit} = this.initializeBackend(backendName);
-        if (asyncInit) {
-          return null;
-        }
-      } else {
-        return null;
-      }
-    }
-    return this.registry[backendName];
-  }
-  findBackendFactory(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      return null;
-    }
-    return this.registryFactory[backendName].factory;
-  }
-  registerBackend(backendName, factory, priority = 1) {
-    if (backendName in this.registryFactory) {
-      console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
-      return false;
-    }
-    this.registryFactory[backendName] = {factory, priority};
-    return true;
-  }
-  async setBackend(backendName) {
-    if (this.registryFactory[backendName] == null) {
-      throw new Error(`Backend name '${backendName}' not found in registry`);
-    }
-    this.backendName = backendName;
-    if (this.registry[backendName] == null) {
-      this.backendInstance = null;
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      const result = asyncInit ? await success : success;
-      if (!result) {
-        return false;
-      }
-    }
-    this.backendInstance = this.registry[backendName];
-    this.setupRegisteredKernels();
-    this.profiler = new Profiler(this.backendInstance);
-    return true;
-  }
-  setupRegisteredKernels() {
-    const kernels = getKernelsForBackend(this.backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.setupFunc != null) {
-        kernel.setupFunc(this.backendInstance);
-      }
-    });
-  }
-  disposeRegisteredKernels(backendName) {
-    const kernels = getKernelsForBackend(backendName);
-    kernels.forEach((kernel) => {
-      if (kernel.disposeFunc != null) {
-        kernel.disposeFunc(this.registry[backendName]);
-      }
-    });
-  }
-  initializeBackend(backendName) {
-    const registryFactoryEntry = this.registryFactory[backendName];
-    if (registryFactoryEntry == null) {
-      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
-    }
-    try {
-      const backend2 = registryFactoryEntry.factory();
-      if (Promise.resolve(backend2) === backend2) {
-        const promiseId = ++this.pendingBackendInitId;
-        const success = backend2.then((backendInstance) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.registry[backendName] = backendInstance;
-          this.pendingBackendInit = null;
-          return true;
-        }).catch((err) => {
-          if (promiseId < this.pendingBackendInitId) {
-            return false;
-          }
-          this.pendingBackendInit = null;
-          console.warn(`Initialization of backend ${backendName} failed`);
-          console.warn(err.stack || err.message);
-          return false;
-        });
-        this.pendingBackendInit = success;
-        return {success, asyncInit: true};
-      } else {
-        this.registry[backendName] = backend2;
-        return {success: true, asyncInit: false};
-      }
-    } catch (err) {
-      console.warn(`Initialization of backend ${backendName} failed`);
-      console.warn(err.stack || err.message);
-      return {success: false, asyncInit: false};
-    }
-  }
-  removeBackend(backendName) {
-    if (!(backendName in this.registryFactory)) {
-      throw new Error(`${backendName} backend not found in registry`);
-    }
-    if (this.backendName === backendName && this.pendingBackendInit != null) {
-      this.pendingBackendInitId++;
-    }
-    if (backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    delete this.registryFactory[backendName];
-    if (this.backendName === backendName) {
-      this.pendingBackendInit = null;
-      this.backendName = null;
-      this.backendInstance = null;
-    }
-  }
-  getSortedBackends() {
-    if (Object.keys(this.registryFactory).length === 0) {
-      throw new Error("No backend found in registry.");
-    }
-    return Object.keys(this.registryFactory).sort((a, b) => {
-      return this.registryFactory[b].priority - this.registryFactory[a].priority;
-    });
-  }
-  initializeBackendsAndReturnBest() {
-    const sortedBackends = this.getSortedBackends();
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      if (asyncInit || success) {
-        return {name: backendName, asyncInit};
-      }
-    }
-    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
-  }
-  moveData(backend2, dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    const srcBackend = info.backend;
-    const values = this.readSync(dataId);
-    srcBackend.disposeData(dataId);
-    info.backend = backend2;
-    backend2.move(dataId, values, info.shape, info.dtype);
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
-    }
-  }
-  tidy(nameOrFn, fn) {
-    let name = null;
-    if (fn == null) {
-      if (typeof nameOrFn !== "function") {
-        throw new Error("Please provide a function to tidy()");
-      }
-      fn = nameOrFn;
-    } else {
-      if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
-        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
-      }
-      if (typeof fn !== "function") {
-        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
-      }
-      name = nameOrFn;
-    }
-    let result;
-    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
-      result = fn();
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      return result;
-    });
-  }
-  scopedRun(start, end, f) {
-    start();
-    try {
-      const res = f();
-      end();
-      return res;
-    } catch (ex) {
-      end();
-      throw ex;
-    }
-  }
-  nextTensorId() {
-    return Engine.nextTensorId++;
-  }
-  nextVariableId() {
-    return Engine.nextVariableId++;
-  }
-  clone(x) {
-    const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
-    const inputs = {x};
-    const grad2 = (dy) => ({
-      x: () => {
-        const dtype = "float32";
-        const gradInputs = {x: dy};
-        const attrs = {dtype};
-        return ENGINE.runKernelFunc((backend2) => backend2.cast(dy, dtype), gradInputs, null, Cast, attrs);
-      }
-    });
-    const saved = [];
-    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
-    return y;
-  }
-  runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {
-    const forwardFunc = null;
-    const backwardsFunc = null;
-    return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
-  }
-  shouldCheckForMemLeaks() {
-    return this.ENV.getBool("IS_TEST");
-  }
-  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
-    const numDataIdsAfter = this.backend.numDataIds();
-    let numOutputDataIds = 0;
-    outInfos.forEach((info) => {
-      numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
-    });
-    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
-    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
-    if (dataIdsLeaked > 0) {
-      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
-    }
-  }
-  runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
-    let outputs;
-    let saved = [];
-    const isTapeOn = this.isTapeOn();
-    if (kernelName == null) {
-      kernelName = this.state.activeScope != null ? this.state.activeScope.name : "";
-    }
-    const startingBytecount = this.state.numBytes;
-    const startingNumTensors = this.state.numTensors;
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack.push(0);
-    }
-    let kernelFunc;
-    const kernel = getKernel(kernelName, this.backendName);
-    let out;
-    if (kernel != null) {
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
-        const outInfos = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
-        }
-        const outTensors = outInfos.map(({dataId, shape, dtype}) => this.makeTensorFromDataId(dataId, shape, dtype));
-        if (isTapeOn) {
-          let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
-          if (tensorsToSave == null) {
-            if (outputsToSave == null) {
-              outputsToSave = [];
-            }
-            const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);
-            tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);
-          }
-          saved = this.saveTensorsForBackwardMode(tensorsToSave);
-        }
-        return outTensors;
-      };
-    } else {
-      const saveFunc = (tensors) => {
-        if (!isTapeOn) {
-          return;
-        }
-        saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-      };
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
-        const outs = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
-        }
-        return outs;
-      };
-    }
-    let kernelProfile;
-    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-      if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
-        outputs = kernelFunc();
-      } else {
-        kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());
-        if (this.ENV.getBool("DEBUG")) {
-          this.profiler.logKernelProfile(kernelProfile);
-        }
-        outputs = kernelProfile.outputs;
-      }
-    });
-    if (isTapeOn) {
-      this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);
-    }
-    if (this.state.profiling) {
-      this.state.activeProfile.kernels.push({
-        name: kernelName,
-        bytesAdded: this.state.numBytes - startingBytecount,
-        totalBytesSnapshot: this.state.numBytes,
-        tensorsAdded: this.state.numTensors - startingNumTensors,
-        totalTensorsSnapshot: this.state.numTensors,
-        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
-        outputShapes: outputs.map((item) => item.shape),
-        kernelTimeMs: kernelProfile.timeMs,
-        extraInfo: kernelProfile.extraInfo
-      });
-    }
-    return Array.isArray(out) ? outputs : outputs[0];
-  }
-  saveTensorsForBackwardMode(tensors) {
-    const saved = tensors.map((tensor17) => this.keep(this.clone(tensor17)));
-    return saved;
-  }
-  getTensorsForGradient(kernelName, inputs, outputs) {
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      const inputsToSave = gradConfig.inputsToSave || [];
-      const outputsToSave = gradConfig.outputsToSave || [];
-      let inputTensorsToSave;
-      if (gradConfig.saveAllInputs) {
-        assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
-        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
-      } else {
-        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
-      }
-      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
-      return inputTensorsToSave.concat(outputTensorsToSave);
-    }
-    return null;
-  }
-  makeTensor(values, shape, dtype, backend2) {
-    if (values == null) {
-      throw new Error("Values passed to engine.makeTensor() are null");
-    }
-    dtype = dtype || "float32";
-    backend2 = backend2 || this.backend;
-    let backendVals = values;
-    if (dtype === "string" && isString(values[0])) {
-      backendVals = values.map((d) => encodeString(d));
-    }
-    const dataId = backend2.write(backendVals, shape, dtype);
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    if (dtype === "string") {
-      const info = this.state.tensorInfo.get(dataId);
-      const newBytes = bytesFromStringArray(backendVals);
-      this.state.numBytes += newBytes - info.bytes;
-      info.bytes = newBytes;
-    }
-    return t;
-  }
-  makeTensorFromDataId(dataId, shape, dtype, backend2) {
-    dtype = dtype || "float32";
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.incRef(t, backend2);
-    return t;
-  }
-  makeVariable(initialValue, trainable = true, name, dtype) {
-    name = name || this.nextVariableId().toString();
-    if (dtype != null && dtype !== initialValue.dtype) {
-      initialValue = initialValue.cast(dtype);
-    }
-    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
-    if (this.state.registeredVariables[v.name] != null) {
-      throw new Error(`Variable with name ${v.name} was already registered`);
-    }
-    this.state.registeredVariables[v.name] = v;
-    this.incRef(v, this.backend);
-    return v;
-  }
-  incRef(a, backend2) {
-    const refCount = this.state.tensorInfo.has(a.dataId) ? this.state.tensorInfo.get(a.dataId).refCount : 0;
-    this.state.numTensors++;
-    if (a.dtype === "string") {
-      this.state.numStringTensors++;
-    }
-    if (refCount === 0) {
-      this.state.numDataBuffers++;
-      let bytes = 0;
-      if (a.dtype !== "complex64" && a.dtype !== "string") {
-        bytes = a.size * bytesPerElement(a.dtype);
-      }
-      this.state.tensorInfo.set(a.dataId, {
-        backend: backend2 || this.backend,
-        dtype: a.dtype,
-        shape: a.shape,
-        bytes,
-        refCount: 0
-      });
-      this.state.numBytes += bytes;
-    }
-    this.state.tensorInfo.get(a.dataId).refCount++;
-    if (!(a instanceof Variable)) {
-      this.track(a);
-    }
-  }
-  disposeTensor(a) {
-    if (!this.state.tensorInfo.has(a.dataId)) {
-      return;
-    }
-    this.state.numTensors--;
-    if (a.dtype === "string") {
-      this.state.numStringTensors--;
-    }
-    const info = this.state.tensorInfo.get(a.dataId);
-    const refCount = info.refCount;
-    if (refCount <= 1) {
-      if (a.dtype !== "complex64") {
-        this.state.numBytes -= info.bytes;
-      }
-      this.state.numDataBuffers--;
-      info.backend.disposeData(a.dataId);
-      this.state.tensorInfo.delete(a.dataId);
-    } else {
-      this.state.tensorInfo.get(a.dataId).refCount--;
-    }
-  }
-  disposeVariables() {
-    for (const varName in this.state.registeredVariables) {
-      const v = this.state.registeredVariables[varName];
-      this.disposeVariable(v);
-    }
-  }
-  disposeVariable(v) {
-    this.disposeTensor(v);
-    if (this.state.registeredVariables[v.name] != null) {
-      delete this.state.registeredVariables[v.name];
-    }
-  }
-  memory() {
-    const info = this.backend.memory();
-    info.numTensors = this.state.numTensors;
-    info.numDataBuffers = this.state.numDataBuffers;
-    info.numBytes = this.state.numBytes;
-    if (this.state.numStringTensors > 0) {
-      info.unreliable = true;
-      if (info.reasons == null) {
-        info.reasons = [];
-      }
-      info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
-    }
-    return info;
-  }
-  async profile(query) {
-    this.state.profiling = true;
-    const startBytes = this.state.numBytes;
-    const startNumTensors = this.state.numTensors;
-    this.state.activeProfile.kernels = [];
-    this.state.activeProfile.result = await query();
-    this.state.profiling = false;
-    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
-    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
-    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
-    for (const kernel of this.state.activeProfile.kernels) {
-      kernel.kernelTimeMs = await kernel.kernelTimeMs;
-      kernel.extraInfo = await kernel.extraInfo;
-    }
-    return this.state.activeProfile;
-  }
-  isTapeOn() {
-    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
-  }
-  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
-    const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      gradientsFunc = gradConfig.gradFunc;
-    }
-    if (gradientsFunc != null) {
-      tapeNode.gradient = (dys) => {
-        dys = dys.map((dy, i) => {
-          if (dy == null) {
-            const output = outputs[i];
-            const vals = makeZerosTypedArray(output.size, output.dtype);
-            return this.makeTensor(vals, output.shape, output.dtype);
-          }
-          return dy;
-        });
-        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
-      };
-    }
-    this.state.activeTape.push(tapeNode);
-  }
-  keep(result) {
-    result.kept = true;
-    return result;
-  }
-  startTape() {
-    if (this.state.gradientDepth === 0) {
-      this.state.activeTape = [];
-    }
-    this.state.gradientDepth++;
-  }
-  endTape() {
-    this.state.gradientDepth--;
-  }
-  startScope(name) {
-    const scopeInfo = {
-      track: [],
-      name: "unnamed scope",
-      id: this.state.nextScopeId++
-    };
-    if (name) {
-      scopeInfo.name = name;
-    }
-    this.state.scopeStack.push(scopeInfo);
-    this.state.activeScope = scopeInfo;
-  }
-  endScope(result) {
-    const tensorsToTrackInParent = getTensorsInContainer(result);
-    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
-    for (let i = 0; i < this.state.activeScope.track.length; i++) {
-      const tensor17 = this.state.activeScope.track[i];
-      if (!tensor17.kept && !tensorsToTrackInParentSet.has(tensor17.id)) {
-        tensor17.dispose();
-      }
-    }
-    const oldScope = this.state.scopeStack.pop();
-    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
-    tensorsToTrackInParent.forEach((tensor17) => {
-      if (!tensor17.kept && tensor17.scopeId === oldScope.id) {
-        this.track(tensor17);
-      }
-    });
-  }
-  gradients(f, xs, dy, allowNoGradients = false) {
-    assert(xs.length > 0, () => "gradients() received an empty list of xs.");
-    if (dy != null && dy.dtype !== "float32") {
-      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
-    }
-    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
-    assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
-    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
-    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
-      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
-    }
-    return this.tidy("backward", () => {
-      const accumulatedGradientMap = {};
-      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
-      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
-      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
-      if (this.state.gradientDepth === 0) {
-        this.state.activeTape.forEach((node2) => {
-          for (const tensor17 of node2.saved) {
-            tensor17.dispose();
-          }
-        });
-        this.state.activeTape = null;
-      }
-      return {value: y, grads: grads2};
-    });
-  }
-  customGrad(f) {
-    assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
-    return (...inputs) => {
-      assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
-      let res;
-      const inputMap = {};
-      inputs.forEach((input, i) => {
-        inputMap[i] = input;
-      });
-      return this.runKernelFunc((_, save) => {
-        res = f(...[...inputs, save]);
-        assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
-        assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
-        return res.value;
-      }, inputMap, (dy, saved) => {
-        const gradRes = res.gradFunc(dy, saved);
-        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
-        assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
-        assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
-        const gradMap = {};
-        grads2.forEach((grad2, i) => {
-          gradMap[i] = () => grad2;
-        });
-        return gradMap;
-      });
-    };
-  }
-  readSync(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.readSync(dataId);
-  }
-  read(dataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.read(dataId);
-  }
-  async time(query) {
-    const start = now();
-    const timingInfo = await this.backend.time(query);
-    timingInfo.wallMs = now() - start;
-    return timingInfo;
-  }
-  track(result) {
-    if (this.state.activeScope != null) {
-      result.scopeId = this.state.activeScope.id;
-      this.state.activeScope.track.push(result);
-    }
-    return result;
-  }
-  get registeredVariables() {
-    return this.state.registeredVariables;
-  }
-  reset() {
-    this.pendingBackendInitId++;
-    this.state.dispose();
-    this.ENV.reset();
-    this.state = new EngineState();
-    for (const backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    this.backendName = null;
-    this.backendInstance = null;
-    this.pendingBackendInit = null;
-  }
-}
-Engine.nextTensorId = 0;
-Engine.nextVariableId = 0;
-function ones(shape) {
-  const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
-  return ENGINE.makeTensor(values, shape, "float32");
-}
-function getOrMakeEngine() {
-  const ns = getGlobalNamespace();
-  if (ns._tfengine == null) {
-    const environment12 = new Environment(ns);
-    ns._tfengine = new Engine(environment12);
-  }
-  setEnvironmentGlobal(ns._tfengine.ENV);
-  setTensorTracker(() => ns._tfengine);
-  return ns._tfengine;
-}
-const ENGINE = getOrMakeEngine();
-function add(a, b) {
-  const inputs = {a, b};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.add(a, b);
-    save([a, b]);
-    return res;
-  }, inputs, null, Add);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/device_util.js
-const device_util_exports = {};
-__export(device_util_exports, {
-  isBrowser: () => isBrowser,
-  isMobile: () => isMobile
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function _isNavigatorDefined() {
-  return typeof navigator !== "undefined" && navigator != null;
-}
-function isMobile() {
-  if (_isNavigatorDefined()) {
-    const a = navigator.userAgent || navigator.vendor || window.opera;
-    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
-  }
-  return false;
-}
-function isBrowser() {
-  return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/flags.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ENV2 = env();
-ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
-  if (debugValue) {
-    console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
-  }
-});
-ENV2.registerFlag("IS_BROWSER", () => isBrowser());
-ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
-ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-ENV2.registerFlag("PROD", () => false);
-ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
-ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-ENV2.registerFlag("IS_TEST", () => false);
-
-// node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function inferShape(val, dtype) {
-  let firstElem = val;
-  if (isTypedArray(val)) {
-    return dtype === "string" ? [] : [val.length];
-  }
-  if (!Array.isArray(val)) {
-    return [];
-  }
-  const shape = [];
-  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
-    shape.push(firstElem.length);
-    firstElem = firstElem[0];
-  }
-  if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
-    deepAssertShapeConsistency(val, shape, []);
-  }
-  return shape;
-}
-function deepAssertShapeConsistency(val, shape, indices) {
-  indices = indices || [];
-  if (!Array.isArray(val) && !isTypedArray(val)) {
-    assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
-    return;
-  }
-  assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
-  assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
-  const subShape = shape.slice(1);
-  for (let i = 0; i < val.length; ++i) {
-    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
-  }
-}
-function assertDtype(expectedDtype, actualDType, argName, functionName) {
-  if (expectedDtype == null) {
-    return;
-  }
-  if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
-  }
-}
-function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
-  if (x instanceof Tensor) {
-    assertDtype(parseAsDtype, x.dtype, argName, functionName);
-    return x;
-  }
-  let inferredDtype = inferDtype(x);
-  if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
-    inferredDtype = parseAsDtype;
-  }
-  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
-  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
-    const type = x == null ? "null" : x.constructor.name;
-    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
-  }
-  const inferredShape = inferShape(x, inferredDtype);
-  if (!isTypedArray(x) && !Array.isArray(x)) {
-    x = [x];
-  }
-  const skipTypedArray = true;
-  const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
-  return ENGINE.makeTensor(values, inferredShape, inferredDtype);
-}
-function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
-  if (!Array.isArray(arg)) {
-    throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
-  }
-  const tensors = arg;
-  return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OP_SCOPE_SUFFIX = "__op";
-function op(f) {
-  const keys = Object.keys(f);
-  if (keys.length !== 1) {
-    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
-  }
-  let opName = keys[0];
-  const fn = f[opName];
-  if (opName.endsWith("_")) {
-    opName = opName.substring(0, opName.length - 1);
-  }
-  opName = opName + OP_SCOPE_SUFFIX;
-  const f2 = (...args) => {
-    ENGINE.startScope(opName);
-    try {
-      const result = fn(...args);
-      if (result instanceof Promise) {
-        console.error("Cannot return a Promise inside of tidy.");
-      }
-      ENGINE.endScope(result);
-      return result;
-    } catch (ex) {
-      ENGINE.endScope(null);
-      throw ex;
-    }
-  };
-  Object.defineProperty(f2, "name", {value: opName, configurable: true});
-  return f2;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function complex_(real6, imag6) {
-  const $real = convertToTensor(real6, "real", "complex");
-  const $imag = convertToTensor(imag6, "imag", "complex");
-  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
-  const forward = (backend2) => {
-    return backend2.complex($real, $imag);
-  };
-  const inputs = {real: $real, imag: $imag};
-  return ENGINE.runKernelFunc(forward, inputs, null, Complex);
-}
-const complex = op({complex_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function makeTensor(values, shape, inferredShape, dtype) {
-  if (dtype == null) {
-    dtype = inferDtype(values);
-  }
-  if (dtype === "complex64") {
-    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
-  }
-  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
-    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
-  }
-  if (shape != null) {
-    assertNonNegativeIntegerDimensions(shape);
-    const providedSize = sizeFromShape(shape);
-    const inferredSize = sizeFromShape(inferredShape);
-    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-    for (let i = 0; i < inferredShape.length; ++i) {
-      const inferred = inferredShape[i];
-      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
-      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
-    }
-  }
-  if (!isTypedArray(values) && !Array.isArray(values)) {
-    values = [values];
-  }
-  shape = shape || inferredShape;
-  values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4(values, shape, dtype) {
-  const inferredShape = inferShape(values, dtype);
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/types.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DTYPE_VALUE_SIZE_MAP = {
-  float32: 4,
-  float16: 2,
-  int32: 4,
-  uint16: 2,
-  uint8: 1,
-  bool: 1,
-  complex64: 8
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const NUM_BYTES_STRING_LENGTH = 4;
-async function encodeWeights(tensors, group) {
-  const specs = [];
-  const dataPromises = [];
-  const names = Array.isArray(tensors) ? tensors.map((tensor17) => tensor17.name) : Object.keys(tensors);
-  for (let i = 0; i < names.length; ++i) {
-    const name = names[i];
-    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
-    if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
-      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
-    }
-    const spec = {name, shape: t.shape, dtype: t.dtype};
-    if (t.dtype === "string") {
-      const utf8bytes = new Promise(async (resolve) => {
-        const vals = await t.bytes();
-        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
-        const bytes = new Uint8Array(totalNumBytes);
-        let offset = 0;
-        for (let i2 = 0; i2 < vals.length; i2++) {
-          const val = vals[i2];
-          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
-          bytes.set(bytesOfLength, offset);
-          offset += NUM_BYTES_STRING_LENGTH;
-          bytes.set(val, offset);
-          offset += val.length;
-        }
-        resolve(bytes);
-      });
-      dataPromises.push(utf8bytes);
-    } else {
-      dataPromises.push(t.data());
-    }
-    if (group != null) {
-      spec.group = group;
-    }
-    specs.push(spec);
-  }
-  const tensorValues = await Promise.all(dataPromises);
-  return {data: concatenateTypedArrays(tensorValues), specs};
-}
-function decodeWeights(buffer11, specs) {
-  const out = {};
-  let float16Decode;
-  let offset = 0;
-  for (const spec of specs) {
-    const name = spec.name;
-    const dtype = spec.dtype;
-    const shape = spec.shape;
-    const size = sizeFromShape(shape);
-    let values;
-    if ("quantization" in spec) {
-      const quantization = spec.quantization;
-      if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-        if (!("min" in quantization && "scale" in quantization)) {
-          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
-        }
-      } else if (quantization.dtype === "float16") {
-        if (dtype !== "float32") {
-          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
-        }
-      } else {
-        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      }
-      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * quantizationSizeFactor);
-      const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
-      if (dtype === "float32") {
-        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
-          values = new Float32Array(quantizedArray.length);
-          for (let i = 0; i < quantizedArray.length; i++) {
-            const v = quantizedArray[i];
-            values[i] = v * quantization.scale + quantization.min;
-          }
-        } else if (quantization.dtype === "float16") {
-          if (float16Decode === void 0) {
-            float16Decode = getFloat16Decoder();
-          }
-          values = float16Decode(quantizedArray);
-        } else {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
-        }
-      } else if (dtype === "int32") {
-        if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
-          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
-        }
-        values = new Int32Array(quantizedArray.length);
-        for (let i = 0; i < quantizedArray.length; i++) {
-          const v = quantizedArray[i];
-          values[i] = Math.round(v * quantization.scale + quantization.min);
-        }
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * quantizationSizeFactor;
-    } else if (dtype === "string") {
-      const size2 = sizeFromShape(spec.shape);
-      values = [];
-      for (let i = 0; i < size2; i++) {
-        const byteLength = new Uint32Array(buffer11.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
-        offset += NUM_BYTES_STRING_LENGTH;
-        const bytes = new Uint8Array(buffer11.slice(offset, offset + byteLength));
-        values.push(bytes);
-        offset += byteLength;
-      }
-    } else {
-      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
-      const byteBuffer = buffer11.slice(offset, offset + size * dtypeFactor);
-      if (dtype === "float32") {
-        values = new Float32Array(byteBuffer);
-      } else if (dtype === "int32") {
-        values = new Int32Array(byteBuffer);
-      } else if (dtype === "bool") {
-        values = new Uint8Array(byteBuffer);
-      } else if (dtype === "complex64") {
-        values = new Float32Array(byteBuffer);
-        const real6 = new Float32Array(values.length / 2);
-        const image2 = new Float32Array(values.length / 2);
-        for (let i = 0; i < real6.length; i++) {
-          real6[i] = values[i * 2];
-          image2[i] = values[i * 2 + 1];
-        }
-        const realTensor = tensor4(real6, shape, "float32");
-        const imageTensor = tensor4(image2, shape, "float32");
-        out[name] = complex(realTensor, imageTensor);
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * dtypeFactor;
-    }
-    if (dtype !== "complex64") {
-      out[name] = tensor4(values, shape, dtype);
-    }
-  }
-  return out;
-}
-function concatenateTypedArrays(xs) {
-  if (xs === null) {
-    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
-  }
-  let totalByteLength = 0;
-  const normalizedXs = [];
-  xs.forEach((x) => {
-    totalByteLength += x.byteLength;
-    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
-    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
-      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
-    }
-  });
-  const y = new Uint8Array(totalByteLength);
-  let offset = 0;
-  normalizedXs.forEach((x) => {
-    y.set(new Uint8Array(x.buffer), offset);
-    offset += x.byteLength;
-  });
-  return y.buffer;
-}
-const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
-function stringByteLength(str) {
-  if (useNodeBuffer) {
-    return Buffer.byteLength(str);
-  }
-  return new Blob([str]).size;
-}
-function concatenateArrayBuffers(buffers) {
-  if (buffers.length === 1) {
-    return buffers[0];
-  }
-  let totalByteLength = 0;
-  buffers.forEach((buffer11) => {
-    totalByteLength += buffer11.byteLength;
-  });
-  const temp = new Uint8Array(totalByteLength);
-  let offset = 0;
-  buffers.forEach((buffer11) => {
-    temp.set(new Uint8Array(buffer11), offset);
-    offset += buffer11.byteLength;
-  });
-  return temp.buffer;
-}
-function basename(path) {
-  const SEPARATOR = "/";
-  path = path.trim();
-  while (path.endsWith(SEPARATOR)) {
-    path = path.slice(0, path.length - 1);
-  }
-  const items = path.split(SEPARATOR);
-  return items[items.length - 1];
-}
-function getModelArtifactsInfoForJSON(modelArtifacts) {
-  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-    throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  }
-  return {
-    dateSaved: new Date(),
-    modelTopologyType: "JSON",
-    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
-    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
-    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
-  };
-}
-function computeFloat16MantisaTable() {
-  const convertMantissa = (i) => {
-    let m = i << 13;
-    let e = 0;
-    while ((m & 8388608) === 0) {
-      e -= 8388608;
-      m <<= 1;
-    }
-    m &= ~8388608;
-    e += 947912704;
-    return m | e;
-  };
-  const mantisaTable = new Uint32Array(2048);
-  mantisaTable[0] = 0;
-  for (let i = 1; i < 1024; i++) {
-    mantisaTable[i] = convertMantissa(i);
-  }
-  for (let i = 1024; i < 2048; i++) {
-    mantisaTable[i] = 939524096 + (i - 1024 << 13);
-  }
-  return mantisaTable;
-}
-function computeFloat16ExponentTable() {
-  const exponentTable = new Uint32Array(64);
-  exponentTable[0] = 0;
-  exponentTable[31] = 1199570944;
-  exponentTable[32] = 2147483648;
-  exponentTable[63] = 3347054592;
-  for (let i = 1; i < 31; i++) {
-    exponentTable[i] = i << 23;
-  }
-  for (let i = 33; i < 63; i++) {
-    exponentTable[i] = 2147483648 + (i - 32 << 23);
-  }
-  return exponentTable;
-}
-function computeFloat16OffsetTable() {
-  const offsetTable = new Uint32Array(64);
-  for (let i = 0; i < 64; i++) {
-    offsetTable[i] = 1024;
-  }
-  offsetTable[0] = offsetTable[32] = 0;
-  return offsetTable;
-}
-function getFloat16Decoder() {
-  const mantisaTable = computeFloat16MantisaTable();
-  const exponentTable = computeFloat16ExponentTable();
-  const offsetTable = computeFloat16OffsetTable();
-  return (quantizedArray) => {
-    const buffer11 = new ArrayBuffer(4 * quantizedArray.length);
-    const bufferUint32View = new Uint32Array(buffer11);
-    for (let index = 0; index < quantizedArray.length; index++) {
-      const float16Bits = quantizedArray[index];
-      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
-      bufferUint32View[index] = float32Bits;
-    }
-    return new Float32Array(buffer11);
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class IORouterRegistry {
-  constructor() {
-    this.saveRouters = [];
-    this.loadRouters = [];
-  }
-  static getInstance() {
-    if (IORouterRegistry.instance == null) {
-      IORouterRegistry.instance = new IORouterRegistry();
-    }
-    return IORouterRegistry.instance;
-  }
-  static registerSaveRouter(saveRouter) {
-    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
-  }
-  static registerLoadRouter(loadRouter) {
-    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
-  }
-  static getSaveHandlers(url) {
-    return IORouterRegistry.getHandlers(url, "save");
-  }
-  static getLoadHandlers(url, loadOptions) {
-    return IORouterRegistry.getHandlers(url, "load", loadOptions);
-  }
-  static getHandlers(url, handlerType, loadOptions) {
-    const validHandlers = [];
-    const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
-    routers.forEach((router) => {
-      const handler = router(url, loadOptions);
-      if (handler !== null) {
-        validHandlers.push(handler);
-      }
-    });
-    return validHandlers;
-  }
-}
-const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
-const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
-const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
-const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const URL_SCHEME_SUFFIX = "://";
-class ModelStoreManagerRegistry {
-  constructor() {
-    this.managers = {};
-  }
-  static getInstance() {
-    if (ModelStoreManagerRegistry.instance == null) {
-      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
-    }
-    return ModelStoreManagerRegistry.instance;
-  }
-  static registerManager(scheme, manager) {
-    assert(scheme != null, () => "scheme must not be undefined or null.");
-    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
-      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
-    }
-    assert(scheme.length > 0, () => "scheme must not be an empty string.");
-    const registry = ModelStoreManagerRegistry.getInstance();
-    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
-    registry.managers[scheme] = manager;
-  }
-  static getManager(scheme) {
-    const manager = this.getInstance().managers[scheme];
-    if (manager == null) {
-      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
-    }
-    return manager;
-  }
-  static getSchemes() {
-    return Object.keys(this.getInstance().managers);
-  }
-}
-function parseURL(url) {
-  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
-    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
-  }
-  return {
-    scheme: url.split(URL_SCHEME_SUFFIX)[0],
-    path: url.split(URL_SCHEME_SUFFIX)[1]
-  };
-}
-async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
-  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
-  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
-  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
-  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
-  const loadHandler = loadHandlers[0];
-  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
-  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
-  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
-  const saveHandler = saveHandlers[0];
-  const sourceScheme = parseURL(sourceURL).scheme;
-  const sourcePath = parseURL(sourceURL).path;
-  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
-  const modelArtifacts = await loadHandler.load();
-  if (deleteSource && sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  const saveResult = await saveHandler.save(modelArtifacts);
-  if (deleteSource && !sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
-  }
-  return saveResult.modelArtifactsInfo;
-}
-async function listModels() {
-  const schemes = ModelStoreManagerRegistry.getSchemes();
-  const out = {};
-  for (const scheme of schemes) {
-    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
-    for (const path in schemeOut) {
-      const url = scheme + URL_SCHEME_SUFFIX + path;
-      out[url] = schemeOut[path];
-    }
-  }
-  return out;
-}
-async function removeModel(url) {
-  const schemeAndPath = parseURL(url);
-  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
-  return manager.removeModel(schemeAndPath.path);
-}
-async function copyModel(sourceURL, destURL) {
-  const deleteSource = false;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-async function moveModel(sourceURL, destURL) {
-  const deleteSource = true;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function buffer(shape, dtype = "float32", values) {
-  dtype = dtype || "float32";
-  assertNonNegativeIntegerDimensions(shape);
-  return new TensorBuffer(shape, dtype, values);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cast_(x, dtype) {
-  const $x = convertToTensor(x, "x", "cast");
-  if (!isValidDtype(dtype)) {
-    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
-  }
-  if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
-    throw new Error("Only strings can be casted to strings");
-  }
-  const inputs = {x: $x};
-  const attrs = {dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.cast($x, dtype), inputs, null, Cast, attrs);
-}
-const cast = op({cast_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clone_(x) {
-  const $x = convertToTensor(x, "x", "clone", null);
-  const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Identity);
-}
-const clone = op({clone_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/print.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function print(x, verbose = false) {
-  console.log(x.toString(verbose));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-getOrMakeEngine();
-const opHandler2 = {
-  buffer,
-  cast,
-  clone,
-  print
-};
-setOpHandler(opHandler2);
-
-// node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const DEFAULT_FILE_NAME_PREFIX = "model";
-const DEFAULT_JSON_EXTENSION_NAME = ".json";
-const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
-function defer(f) {
-  return new Promise((resolve) => setTimeout(resolve)).then(f);
-}
-class BrowserDownloads {
-  constructor(fileNamePrefix) {
-    if (!env().getBool("IS_BROWSER")) {
-      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    }
-    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
-      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
-    }
-    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
-      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
-    }
-    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
-    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
-  }
-  async save(modelArtifacts) {
-    if (typeof document === "undefined") {
-      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
-    }
-    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
-    } else {
-      const weightsManifest = [{
-        paths: ["./" + this.weightDataFileName],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelTopologyAndWeightManifest = {
-        modelTopology: modelArtifacts.modelTopology,
-        format: modelArtifacts.format,
-        generatedBy: modelArtifacts.generatedBy,
-        convertedBy: modelArtifacts.convertedBy,
-        weightsManifest
-      };
-      const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
-      const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
-      jsonAnchor.download = this.modelTopologyFileName;
-      jsonAnchor.href = modelTopologyAndWeightManifestURL;
-      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
-      if (modelArtifacts.weightData != null) {
-        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        weightDataAnchor.download = this.weightDataFileName;
-        weightDataAnchor.href = weightsURL;
-        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
-      }
-      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
-    }
-  }
-}
-BrowserDownloads.URL_SCHEME = "downloads://";
-class BrowserFiles {
-  constructor(files) {
-    if (files == null || files.length < 1) {
-      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
-    }
-    this.files = files;
-  }
-  async load() {
-    const jsonFile = this.files[0];
-    const weightFiles = this.files.slice(1);
-    return new Promise((resolve, reject) => {
-      const jsonReader = new FileReader();
-      jsonReader.onload = (event) => {
-        const modelJSON = JSON.parse(event.target.result);
-        const modelTopology = modelJSON.modelTopology;
-        if (modelTopology == null) {
-          reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        if (weightFiles.length === 0) {
-          resolve({modelTopology});
-        }
-        const weightsManifest = modelJSON.weightsManifest;
-        if (weightsManifest == null) {
-          reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
-          return;
-        }
-        let pathToFile;
-        try {
-          pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
-        } catch (err) {
-          reject(err);
-          return;
-        }
-        const weightSpecs = [];
-        const paths = [];
-        const perFileBuffers = [];
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            paths.push(path);
-            perFileBuffers.push(null);
-          });
-          weightSpecs.push(...weightsGroup.weights);
-        });
-        weightsManifest.forEach((weightsGroup) => {
-          weightsGroup.paths.forEach((path) => {
-            const weightFileReader = new FileReader();
-            weightFileReader.onload = (event2) => {
-              const weightData = event2.target.result;
-              const index = paths.indexOf(path);
-              perFileBuffers[index] = weightData;
-              if (perFileBuffers.indexOf(null) === -1) {
-                resolve({
-                  modelTopology,
-                  weightSpecs,
-                  weightData: concatenateArrayBuffers(perFileBuffers),
-                  format: modelJSON.format,
-                  generatedBy: modelJSON.generatedBy,
-                  convertedBy: modelJSON.convertedBy,
-                  userDefinedMetadata: modelJSON.userDefinedMetadata
-                });
-              }
-            };
-            weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
-            weightFileReader.readAsArrayBuffer(pathToFile[path]);
-          });
-        });
-      };
-      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
-      jsonReader.readAsText(jsonFile);
-    });
-  }
-  checkManifestAndWeightFiles(manifest, files) {
-    const basenames = [];
-    const fileNames = files.map((file) => basename(file.name));
-    const pathToFile = {};
-    for (const group of manifest) {
-      group.paths.forEach((path) => {
-        const pathBasename = basename(path);
-        if (basenames.indexOf(pathBasename) !== -1) {
-          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
-        }
-        basenames.push(pathBasename);
-        if (fileNames.indexOf(pathBasename) === -1) {
-          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
-        } else {
-          pathToFile[path] = files[fileNames.indexOf(pathBasename)];
-        }
-      });
-    }
-    if (basenames.length !== files.length) {
-      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
-    }
-    return pathToFile;
-  }
-}
-const browserDownloadsRouter = (url) => {
-  if (!env().getBool("IS_BROWSER")) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
-      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
-function browserDownloads(fileNamePrefix = "model") {
-  return new BrowserDownloads(fileNamePrefix);
-}
-function browserFiles(files) {
-  return new BrowserFiles(files);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/progress.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
-  checkPromises(promises);
-  startFraction = startFraction == null ? 0 : startFraction;
-  endFraction = endFraction == null ? 1 : endFraction;
-  checkFraction(startFraction, endFraction);
-  let resolvedPromise = 0;
-  const registerMonitor = (promise) => {
-    promise.then((value) => {
-      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
-      onProgress(fraction);
-      return value;
-    });
-    return promise;
-  };
-  function checkPromises(promises2) {
-    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
-  }
-  function checkFraction(startFraction2, endFraction2) {
-    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
-    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
-    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
-  }
-  return Promise.all(promises.map(registerMonitor));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
-  if (loadOptions == null) {
-    loadOptions = {};
-  }
-  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
-  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
-  const fetchStartFraction = 0;
-  const fetchEndFraction = 0.5;
-  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
-  const bufferPromises = responses.map((response) => response.arrayBuffer());
-  const bufferStartFraction = 0.5;
-  const bufferEndFraction = 1;
-  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
-  return buffers;
-}
-async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
-  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
-  const loadWeights2 = weightsLoaderFactory(fetchWeights);
-  return loadWeights2(manifest, filePathPrefix, weightNames);
-}
-function weightsLoaderFactory(fetchWeightsFunction) {
-  return async (manifest, filePathPrefix = "", weightNames) => {
-    const groupIndicesToFetchMap = manifest.map(() => false);
-    const groupWeightsToFetch = {};
-    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
-    const allManifestWeightNames = [];
-    manifest.forEach((manifestGroupConfig, groupIndex) => {
-      let groupOffset = 0;
-      manifestGroupConfig.weights.forEach((weightsEntry) => {
-        const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
-        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
-        const enqueueWeightsForFetchingFn = () => {
-          groupIndicesToFetchMap[groupIndex] = true;
-          if (groupWeightsToFetch[groupIndex] == null) {
-            groupWeightsToFetch[groupIndex] = [];
-          }
-          groupWeightsToFetch[groupIndex].push({
-            manifestEntry: weightsEntry,
-            groupOffset,
-            sizeBytes: weightsBytes
-          });
-        };
-        if (weightNames != null) {
-          weightNames.forEach((weightName, weightIndex) => {
-            if (weightName === weightsEntry.name) {
-              enqueueWeightsForFetchingFn();
-              weightsFound[weightIndex] = true;
-            }
-          });
-        } else {
-          enqueueWeightsForFetchingFn();
-        }
-        allManifestWeightNames.push(weightsEntry.name);
-        groupOffset += weightsBytes;
-      });
-    });
-    if (!weightsFound.every((found) => found)) {
-      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
-      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
-    }
-    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
-      if (shouldFetch) {
-        accumulator.push(i);
-      }
-      return accumulator;
-    }, []);
-    const fetchUrls = [];
-    groupIndicesToFetch.forEach((i) => {
-      manifest[i].paths.forEach((filepath) => {
-        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
-        fetchUrls.push(fetchUrl);
-      });
-    });
-    const buffers = await fetchWeightsFunction(fetchUrls);
-    const weightsTensorMap = {};
-    let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach((i) => {
-      const numBuffers = manifest[i].paths.length;
-      let groupBytes = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        groupBytes += buffers[bufferIndexOffset + i2].byteLength;
-      }
-      const groupBuffer = new ArrayBuffer(groupBytes);
-      const groupByteBuffer = new Uint8Array(groupBuffer);
-      let groupBufferOffset = 0;
-      for (let i2 = 0; i2 < numBuffers; i2++) {
-        const buffer11 = new Uint8Array(buffers[bufferIndexOffset + i2]);
-        groupByteBuffer.set(buffer11, groupBufferOffset);
-        groupBufferOffset += buffer11.byteLength;
-      }
-      const weightsEntries = groupWeightsToFetch[i];
-      weightsEntries.forEach((weightsEntry) => {
-        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
-        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
-        for (const name in nameToTensorMap) {
-          weightsTensorMap[name] = nameToTensorMap[name];
-        }
-      });
-      bufferIndexOffset += numBuffers;
-    });
-    return weightsTensorMap;
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/http.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
-const JSON_TYPE = "application/json";
-class HTTPRequest {
-  constructor(path, loadOptions) {
-    this.DEFAULT_METHOD = "POST";
-    if (loadOptions == null) {
-      loadOptions = {};
-    }
-    this.weightPathPrefix = loadOptions.weightPathPrefix;
-    this.onProgress = loadOptions.onProgress;
-    this.weightUrlConverter = loadOptions.weightUrlConverter;
-    if (loadOptions.fetchFunc != null) {
-      assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
-      this.fetch = loadOptions.fetchFunc;
-    } else {
-      this.fetch = env().platform.fetch;
-    }
-    assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
-    if (Array.isArray(path)) {
-      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
-    }
-    this.path = path;
-    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
-      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
-    }
-    this.requestInit = loadOptions.requestInit || {};
-  }
-  async save(modelArtifacts) {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
-    }
-    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
-    init.body = new FormData();
-    const weightsManifest = [{
-      paths: ["./model.weights.bin"],
-      weights: modelArtifacts.weightSpecs
-    }];
-    const modelTopologyAndWeightManifest = {
-      modelTopology: modelArtifacts.modelTopology,
-      format: modelArtifacts.format,
-      generatedBy: modelArtifacts.generatedBy,
-      convertedBy: modelArtifacts.convertedBy,
-      userDefinedMetadata: modelArtifacts.userDefinedMetadata,
-      weightsManifest
-    };
-    init.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
-    if (modelArtifacts.weightData != null) {
-      init.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
-    }
-    const response = await this.fetch(this.path, init);
-    if (response.ok) {
-      return {
-        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
-        responses: [response]
-      };
-    } else {
-      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
-    }
-  }
-  async load() {
-    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
-    if (!modelConfigRequest.ok) {
-      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
-    }
-    let modelConfig;
-    try {
-      modelConfig = await modelConfigRequest.json();
-    } catch (e) {
-      let message = `Failed to parse model JSON of response from ${this.path}.`;
-      if (this.path.endsWith(".pb")) {
-        message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
-      } else {
-        message += " Please make sure the server is serving valid JSON for this request.";
-      }
-      throw new Error(message);
-    }
-    const modelTopology = modelConfig.modelTopology;
-    const weightsManifest = modelConfig.weightsManifest;
-    const generatedBy = modelConfig.generatedBy;
-    const convertedBy = modelConfig.convertedBy;
-    const format = modelConfig.format;
-    const userDefinedMetadata = modelConfig.userDefinedMetadata;
-    if (modelTopology == null && weightsManifest == null) {
-      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    }
-    let weightSpecs;
-    let weightData;
-    if (weightsManifest != null) {
-      const results = await this.loadWeights(weightsManifest);
-      [weightSpecs, weightData] = results;
-    }
-    return {
-      modelTopology,
-      weightSpecs,
-      weightData,
-      userDefinedMetadata,
-      generatedBy,
-      convertedBy,
-      format
-    };
-  }
-  async loadWeights(weightsManifest) {
-    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
-    const [prefix, suffix] = parseUrl(weightPath);
-    const pathPrefix = this.weightPathPrefix || prefix;
-    const weightSpecs = [];
-    for (const entry of weightsManifest) {
-      weightSpecs.push(...entry.weights);
-    }
-    const fetchURLs = [];
-    const urlPromises = [];
-    for (const weightsGroup of weightsManifest) {
-      for (const path of weightsGroup.paths) {
-        if (this.weightUrlConverter != null) {
-          urlPromises.push(this.weightUrlConverter(path));
-        } else {
-          fetchURLs.push(pathPrefix + path + suffix);
-        }
-      }
-    }
-    if (this.weightUrlConverter) {
-      fetchURLs.push(...await Promise.all(urlPromises));
-    }
-    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
-      requestInit: this.requestInit,
-      fetchFunc: this.fetch,
-      onProgress: this.onProgress
-    });
-    return [weightSpecs, concatenateArrayBuffers(buffers)];
-  }
-}
-HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
-function parseUrl(url) {
-  const lastSlash = url.lastIndexOf("/");
-  const lastSearchParam = url.lastIndexOf("?");
-  const prefix = url.substring(0, lastSlash);
-  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
-  return [prefix + "/", suffix];
-}
-function isHTTPScheme(url) {
-  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
-}
-const httpRouter = (url, loadOptions) => {
-  if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
-    return null;
-  } else {
-    let isHTTP = true;
-    if (Array.isArray(url)) {
-      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
-    } else {
-      isHTTP = isHTTPScheme(url);
-    }
-    if (isHTTP) {
-      return http(url, loadOptions);
-    }
-  }
-  return null;
-};
-IORouterRegistry.registerSaveRouter(httpRouter);
-IORouterRegistry.registerLoadRouter(httpRouter);
-function http(path, loadOptions) {
-  return new HTTPRequest(path, loadOptions);
-}
-function browserHTTPRequest(path, loadOptions) {
-  return http(path, loadOptions);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class PassthroughLoader {
-  constructor(modelArtifacts) {
-    this.modelArtifacts = modelArtifacts;
-  }
-  async load() {
-    return this.modelArtifacts;
-  }
-}
-class PassthroughSaver {
-  constructor(saveHandler) {
-    this.saveHandler = saveHandler;
-  }
-  async save(modelArtifacts) {
-    return this.saveHandler(modelArtifacts);
-  }
-}
-function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
-  if (arguments.length === 1) {
-    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
-    if (isModelArtifacts) {
-      return new PassthroughLoader(modelArtifacts);
-    } else {
-      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-      return new PassthroughLoader({modelTopology: modelArtifacts});
-    }
-  } else {
-    console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
-    return new PassthroughLoader({
-      modelTopology: modelArtifacts,
-      weightSpecs,
-      weightData,
-      trainingConfig
-    });
-  }
-}
-function withSaveHandler(saveHandler) {
-  return new PassthroughSaver(saveHandler);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/io/io.js
-const io_exports = {};
-__export(io_exports, {
-  browserFiles: () => browserFiles,
-  browserHTTPRequest: () => browserHTTPRequest,
-  concatenateArrayBuffers: () => concatenateArrayBuffers,
-  copyModel: () => copyModel,
-  decodeWeights: () => decodeWeights,
-  encodeWeights: () => encodeWeights,
-  fromMemory: () => fromMemory,
-  getLoadHandlers: () => getLoadHandlers,
-  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
-  getSaveHandlers: () => getSaveHandlers,
-  http: () => http,
-  isHTTPScheme: () => isHTTPScheme,
-  listModels: () => listModels,
-  loadWeights: () => loadWeights,
-  moveModel: () => moveModel,
-  registerLoadRouter: () => registerLoadRouter,
-  registerSaveRouter: () => registerSaveRouter,
-  removeModel: () => removeModel,
-  weightsLoaderFactory: () => weightsLoaderFactory,
-  withSaveHandler: () => withSaveHandler
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reshape_(x, shape) {
-  const $x = convertToTensor(x, "x", "reshape", null);
-  shape = inferFromImplicitShape(shape, $x.size);
-  assert($x.size === sizeFromShape(shape), () => "new shape and old shape must have the same number of elements.");
-  const inputs = {x: $x};
-  const attrs = {shape};
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.reshape($x, shape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Reshape, attrs);
-}
-const reshape = op({reshape_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function matMul_(a, b, transposeA = false, transposeB = false) {
-  let $a = convertToTensor(a, "a", "matMul");
-  let $b = convertToTensor(b, "b", "matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  const forward = (backend2, save) => {
-    save([a3D, b3D]);
-    return backend2.batchMatMul(a3D, b3D, transposeA, transposeB);
-  };
-  const inputs = {a: a3D, b: b3D};
-  const attrs = {transposeA, transposeB};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, BatchMatMul, attrs);
-  return reshape(res, outShape);
-}
-const matMul = op({matMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function oneHot_(indices, depth, onValue = 1, offValue = 0) {
-  if (depth < 2) {
-    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
-  }
-  const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
-  const outShape = [...$indices.shape, depth];
-  const forward = (backend2, save) => {
-    save([$indices]);
-    return reshape(backend2.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);
-  };
-  const inputs = {indices: $indices};
-  const attrs = {depth, onValue, offValue};
-  return ENGINE.runKernelFunc(forward, inputs, null, OneHot, attrs);
-}
-const oneHot = op({oneHot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function transpose_(x, perm) {
-  const $x = convertToTensor(x, "x", "transpose");
-  if (perm == null) {
-    perm = $x.shape.map((s, i) => i).reverse();
-  }
-  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
-  perm.forEach((axis) => {
-    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
-  });
-  if ($x.rank <= 1) {
-    return $x.clone();
-  }
-  const inputs = {x: $x};
-  const attrs = {perm};
-  return ENGINE.runKernelFunc((backend2) => backend2.transpose($x, perm), inputs, null, Transpose, attrs);
-}
-const transpose = op({transpose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function confusionMatrix_(labels, predictions, numClasses) {
-  const $labels = convertToTensor(labels, "labels", "confusionMatrix");
-  const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
-  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
-  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
-  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
-  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
-  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
-  const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
-  const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
-  const oneHotLabelsT = transpose(oneHotLabels);
-  return cast(matMul(oneHotLabelsT, oneHotPredictions), "int32");
-}
-const confusionMatrix = op({confusionMatrix_});
-
-// node_modules/@tensorflow/tfjs-core/dist/math.js
-const math_exports = {};
-__export(math_exports, {
-  confusionMatrix: () => confusionMatrix
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor3d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 3) {
-    throw new Error("tensor3d() requires shape to have three numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
-    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
-const browser_exports = {};
-__export(browser_exports, {
-  fromPixels: () => fromPixels,
-  toPixels: () => toPixels
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-let fromPixels2DContext;
-function fromPixels_(pixels, numChannels = 3) {
-  if (numChannels > 4) {
-    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
-  }
-  if (pixels == null) {
-    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-  }
-  let isPixelData = false;
-  let isImageData = false;
-  let isVideo = false;
-  let isImage = false;
-  let isCanvasLike = false;
-  if (pixels.data instanceof Uint8Array) {
-    isPixelData = true;
-  } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
-    isImageData = true;
-  } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
-    isVideo = true;
-  } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
-    isImage = true;
-  } else if (pixels.getContext != null) {
-    isCanvasLike = true;
-  } else {
-    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
-  }
-  if (isVideo) {
-    const HAVE_CURRENT_DATA_READY_STATE = 2;
-    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
-      throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-    }
-  }
-  const kernel = getKernel(FromPixels, ENGINE.backendName);
-  if (kernel != null) {
-    const inputs = {pixels};
-    const attrs = {numChannels};
-    return ENGINE.runKernel(FromPixels, inputs, attrs);
-  }
-  const [width, height] = isVideo ? [
-    pixels.videoWidth,
-    pixels.videoHeight
-  ] : [pixels.width, pixels.height];
-  let vals;
-  if (isCanvasLike) {
-    vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
-  } else if (isImageData || isPixelData) {
-    vals = pixels.data;
-  } else if (isImage || isVideo) {
-    if (fromPixels2DContext == null) {
-      fromPixels2DContext = document.createElement("canvas").getContext("2d");
-    }
-    fromPixels2DContext.canvas.width = width;
-    fromPixels2DContext.canvas.height = height;
-    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
-    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
-  }
-  let values;
-  if (numChannels === 4) {
-    values = new Int32Array(vals);
-  } else {
-    const numPixels = width * height;
-    values = new Int32Array(numPixels * numChannels);
-    for (let i = 0; i < numPixels; i++) {
-      for (let channel = 0; channel < numChannels; ++channel) {
-        values[i * numChannels + channel] = vals[i * 4 + channel];
-      }
-    }
-  }
-  const outShape = [height, width, numChannels];
-  return tensor3d(values, outShape, "int32");
-}
-async function toPixels(img, canvas) {
-  let $img = convertToTensor(img, "img", "toPixels");
-  if (!(img instanceof Tensor)) {
-    const originalImgTensor = $img;
-    $img = cast(originalImgTensor, "int32");
-    originalImgTensor.dispose();
-  }
-  if ($img.rank !== 2 && $img.rank !== 3) {
-    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
-  }
-  const [height, width] = $img.shape.slice(0, 2);
-  const depth = $img.rank === 2 ? 1 : $img.shape[2];
-  if (depth > 4 || depth === 2) {
-    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
-  }
-  if ($img.dtype !== "float32" && $img.dtype !== "int32") {
-    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
-  }
-  const data = await $img.data();
-  const multiplier = $img.dtype === "float32" ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255];
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-      if ($img.dtype === "float32") {
-        if (value < 0 || value > 1) {
-          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
-        }
-      } else if ($img.dtype === "int32") {
-        if (value < 0 || value > 255) {
-          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
-        }
-      }
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-  if (canvas != null) {
-    canvas.width = width;
-    canvas.height = height;
-    const ctx = canvas.getContext("2d");
-    const imageData = new ImageData(bytes, width, height);
-    ctx.putImageData(imageData, 0, 0);
-  }
-  if ($img !== img) {
-    $img.dispose();
-  }
-  return bytes;
-}
-const fromPixels = op({fromPixels_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
-const gather_nd_util_exports = {};
-__export(gather_nd_util_exports, {
-  prepareAndValidate: () => prepareAndValidate
-});
-function prepareAndValidate(tensor17, indices) {
-  if (tensor17.rank < 1) {
-    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor17.rank}.`);
-  }
-  if (indices.rank < 1) {
-    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
-  }
-  if (indices.shape[indices.rank - 1] > tensor17.rank) {
-    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank - 1]} vs. ${tensor17.rank}`);
-  }
-  if (tensor17.size === 0) {
-    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor17.shape}.`);
-  }
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-  let nResult = 1;
-  for (let i = 0; i < indicesShape.length - 1; ++i) {
-    nResult *= indicesShape[i];
-  }
-  const inputShape = tensor17.shape;
-  const resultShape = indicesShape.slice();
-  resultShape.pop();
-  let sliceSize = 1;
-  for (let i = sliceRank; i < tensor17.rank; ++i) {
-    sliceSize *= inputShape[i];
-    resultShape.push(inputShape[i]);
-  }
-  const strides = [
-    ...computeStrides(tensor17.shape).map((stride) => stride / sliceSize),
-    1
-  ].slice(0, sliceRank);
-  return [resultShape, nResult, sliceSize, strides];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
-const scatter_nd_util_exports = {};
-__export(scatter_nd_util_exports, {
-  calculateShapes: () => calculateShapes,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape
-});
-function validateUpdateShape(shape, indices, updates) {
-  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
-  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
-  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
-  if (updates.rank < batchDim) {
-    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
-  }
-  if (shape.length < sliceDim + (updates.rank - batchDim)) {
-    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
-  }
-  if (updates.rank !== batchDim + shape.length - sliceDim) {
-    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
-  }
-  for (let d = 0; d < batchDim; ++d) {
-    if (updates.shape[d] !== indices.shape[d]) {
-      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
-    }
-  }
-  for (let d = 0; d < updates.rank - batchDim; ++d) {
-    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
-      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
-    }
-  }
-}
-function validateInput(updates, indices, shape) {
-  if (indices.rank < 1) {
-    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
-  }
-  if (updates.rank < 1) {
-    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
-  }
-  if (indices.dtype !== "int32") {
-    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
-  }
-  if (shape.length < 1) {
-    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
-  }
-  if (shape.length === 0) {
-    if (indices.size === 0) {
-      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
-    }
-    if (updates.size === 0) {
-      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
-    }
-  }
-  validateUpdateShape(shape, indices, updates);
-}
-function calculateShapes(updates, indices, shape) {
-  const indicesRank = indices.shape.length;
-  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
-  const totalNd = shape.length;
-  let sliceSize = 1;
-  for (let i = sliceRank; i < totalNd; ++i) {
-    sliceSize *= shape[i];
-  }
-  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
-  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
-  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
-  const outputSize = sizeFromShape(shape);
-  return {sliceRank, numUpdates, sliceSize, strides, outputSize};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
-const slice_util_exports = {};
-__export(slice_util_exports, {
-  assertParamsValid: () => assertParamsValid,
-  computeFlatOffset: () => computeFlatOffset,
-  computeOutShape: () => computeOutShape,
-  getNormalizedAxes: () => getNormalizedAxes,
-  isSliceContinous: () => isSliceContinous,
-  maskToAxes: () => maskToAxes,
-  parseSliceParams: () => parseSliceParams,
-  startForAxis: () => startForAxis,
-  startIndicesWithElidedDims: () => startIndicesWithElidedDims,
-  stopForAxis: () => stopForAxis,
-  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
-  stridesForAxis: () => stridesForAxis,
-  stridesWithElidedDims: () => stridesWithElidedDims
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsValid(input, begin, size) {
-  assert(input.rank === begin.length, () => `Error in slice${input.rank}D: Length of begin ${begin} must match the rank of the array (${input.rank}).`);
-  assert(input.rank === size.length, () => `Error in slice${input.rank}D: Length of size ${size} must match the rank of the array (${input.rank}).`);
-  for (let i = 0; i < input.rank; ++i) {
-    assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${input.rank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);
-  }
-}
-function maskToAxes(mask) {
-  const axes = [];
-  let axis = 0;
-  while (mask > 0) {
-    if (mask & 1) {
-      axes.push(axis);
-    }
-    mask /= 2;
-    axis++;
-  }
-  return axes;
-}
-function computeOutShape(begin, end, strides) {
-  const size = [];
-  for (let axis = 0; axis < begin.length; axis++) {
-    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
-  }
-  return size;
-}
-function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
-  const newStrides = [...strides];
-  for (let i = newStrides.length; i < inputShape.length; i++) {
-    newStrides.push(1);
-  }
-  for (let i = 0; i < numElidedAxes; i++) {
-    if (i === 0) {
-      newStrides[ellipsisInsertionIndex] = 1;
-    } else {
-      newStrides.splice(ellipsisInsertionIndex, 0, 1);
-      newStrides.pop();
-    }
-  }
-  return newStrides;
-}
-function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
-  if (normalizedAxis <= ellipsisInsertionIndex) {
-    return normalizedAxis;
-  }
-  return normalizedAxis - (numElidedAxes - 1);
-}
-function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
-  const elidedAxes = [];
-  for (let i = 0; i < numElidedAxes; i++) {
-    elidedAxes.push(ellipsisInsertionIndex + i);
-  }
-  return elidedAxes;
-}
-function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
-  const inputRank = inputShape.length;
-  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
-  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
-    const fullIndex = ellipsisAxes[0];
-    const numElidedAxes = numInterpolatedAxes + 1;
-    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
-    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
-    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
-  } else {
-    for (let axis = 0; axis < inputRank; axis++) {
-      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
-      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
-      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
-    }
-  }
-  return {
-    begin: normalizedBegin,
-    end: normalizedEnd,
-    strides: normalizedStrides
-  };
-}
-function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = 0;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalBegin[originalAxis];
-      if (beginMask & 1 << originalAxis) {
-        originalValue = 0;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  return newIndices;
-}
-function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = Number.MAX_SAFE_INTEGER;
-    } else {
-      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalEnd[originalAxis];
-      if (endMask & 1 << originalAxis) {
-        originalValue = Number.MAX_SAFE_INTEGER;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-  for (let i = 0; i < newIndices.length; i++) {
-    const axisSize = inputShape[i];
-    if (newIndices[i] < 0) {
-      newIndices[i] += axisSize;
-    }
-    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
-  }
-  return newIndices;
-}
-function stridesForAxis(strides, axis, ellipsisMask) {
-  let stride = strides[axis];
-  if (ellipsisMask & 1 << axis || stride == null) {
-    stride = 1;
-  }
-  return stride;
-}
-function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
-  let start = startIndices[axis];
-  const stride = strides[axis] || 1;
-  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
-    if (stride > 0) {
-      start = Number.MIN_SAFE_INTEGER;
-    } else {
-      start = Number.MAX_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (start < 0) {
-    start += axisSize;
-  }
-  start = clamp(0, start, axisSize - 1);
-  return start;
-}
-function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
-  let stop = stopIndices[axis];
-  const stride = strides[axis] || 1;
-  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
-    if (stride > 0) {
-      stop = Number.MAX_SAFE_INTEGER;
-    } else {
-      stop = Number.MIN_SAFE_INTEGER;
-    }
-  }
-  const axisSize = inputShape[axis];
-  if (stop < 0) {
-    stop += axisSize;
-  }
-  if (stride > 0) {
-    stop = clamp(0, stop, axisSize);
-  } else {
-    stop = clamp(-1, stop, axisSize - 1);
-  }
-  return stop;
-}
-function isSliceContinous(shape, begin, size) {
-  let firstNonOneAxis = size.length;
-  for (let i = 0; i < size.length; i++) {
-    if (size[i] > 1) {
-      firstNonOneAxis = i;
-      break;
-    }
-  }
-  for (let i = firstNonOneAxis + 1; i < size.length; i++) {
-    if (begin[i] > 0 || size[i] !== shape[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-function computeFlatOffset(begin, strides) {
-  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i = 0; i < begin.length - 1; i++) {
-    flatOffset += begin[i] * strides[i];
-  }
-  return flatOffset;
-}
-function parseSliceParams(x, begin, size) {
-  let begin_;
-  if (typeof begin === "number") {
-    begin_ = [begin, ...new Array(x.rank - 1).fill(0)];
-  } else if (begin.length < x.rank) {
-    begin_ = begin.concat(new Array(x.rank - begin.length).fill(0));
-  } else {
-    begin_ = begin.slice();
-  }
-  begin_.forEach((d) => {
-    assert(d !== -1, () => "slice() does not support negative begin indexing.");
-  });
-  let size_;
-  if (size == null) {
-    size_ = new Array(x.rank).fill(-1);
-  } else if (typeof size === "number") {
-    size_ = [size, ...new Array(x.rank - 1).fill(-1)];
-  } else if (size.length < x.rank) {
-    size_ = size.concat(new Array(x.rank - size.length).fill(-1));
-  } else {
-    size_ = size;
-  }
-  size_ = size_.map((d, i) => {
-    if (d >= 0) {
-      return d;
-    } else {
-      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
-      return x.shape[i] - begin_[i];
-    }
-  });
-  return [begin_, size_];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/serialization.js
-const serialization_exports = {};
-__export(serialization_exports, {
-  Serializable: () => Serializable,
-  SerializationMap: () => SerializationMap,
-  registerClass: () => registerClass
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Serializable {
-  getClassName() {
-    return this.constructor.className;
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2);
-  }
-}
-class SerializationMap {
-  constructor() {
-    this.classNameMap = {};
-  }
-  static getMap() {
-    if (SerializationMap.instance == null) {
-      SerializationMap.instance = new SerializationMap();
-    }
-    return SerializationMap.instance;
-  }
-  static register(cls) {
-    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
-  }
-}
-function registerClass(cls) {
-  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
-  assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
-  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
-  SerializationMap.register(cls);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/test_util.js
-const test_util_exports = {};
-__export(test_util_exports, {
-  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
-  expectArrayBuffersEqual: () => expectArrayBuffersEqual,
-  expectArraysClose: () => expectArraysClose,
-  expectArraysEqual: () => expectArraysEqual,
-  expectNumbersClose: () => expectNumbersClose,
-  expectPromiseToFail: () => expectPromiseToFail,
-  expectValuesInRange: () => expectValuesInRange,
-  testEpsilon: () => testEpsilon
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const TEST_EPSILON_FLOAT32 = 1e-3;
-const TEST_EPSILON_FLOAT16 = 0.1;
-function expectArraysClose(actual, expected, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon2));
-}
-function testEpsilon() {
-  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
-}
-function expectArraysPredicate(actual, expected, predicate) {
-  let checkClassType = true;
-  if (isTypedArray(actual) || isTypedArray(expected)) {
-    checkClassType = false;
-  }
-  if (isTypedArray(actual) && isTypedArray(expected)) {
-    checkClassType = true;
-  }
-  if (checkClassType) {
-    const aType = actual.constructor.name;
-    const bType = expected.constructor.name;
-    if (aType !== bType) {
-      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
-    }
-  }
-  if (Array.isArray(actual) && Array.isArray(expected)) {
-    const actualShape = inferShape(actual);
-    const expectedShape = inferShape(expected);
-    if (!arraysEqual(actualShape, expectedShape)) {
-      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
-    }
-  }
-  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
-  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
-  if (actualFlat.length !== expectedFlat.length) {
-    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-  }
-  for (let i = 0; i < expectedFlat.length; ++i) {
-    const a = actualFlat[i];
-    const e = expectedFlat[i];
-    if (!predicate(a, e)) {
-      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
-Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`);
-    }
-  }
-}
-function expectPromiseToFail(fn, done) {
-  fn().then(() => done.fail(), () => done());
-}
-function expectArraysEqual(actual, expected) {
-  const exp11 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
-  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
-    return expectArraysPredicate(actual, exp11, (a, b) => a == b);
-  }
-  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
-}
-function expectNumbersClose(a, e, epsilon2) {
-  if (epsilon2 == null) {
-    epsilon2 = testEpsilon();
-  }
-  if (!areClose(a, e, epsilon2)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
-  }
-}
-function areClose(a, e, epsilon2) {
-  if (!isFinite(a) && !isFinite(e)) {
-    return true;
-  }
-  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon2) {
-    return false;
-  }
-  return true;
-}
-function expectValuesInRange(actual, low, high) {
-  for (let i = 0; i < actual.length; i++) {
-    if (actual[i] < low || actual[i] > high) {
-      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
-    }
-  }
-}
-function expectArrayBuffersEqual(actual, expected) {
-  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/version.js
-/** @license See the LICENSE file. */
-const version = "2.4.0";
-
-// node_modules/@tensorflow/tfjs-core/dist/globals.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enableProdMode() {
-  env().set("PROD", true);
-}
-function enableDebugMode() {
-  env().set("DEBUG", true);
-}
-function disableDeprecationWarnings() {
-  env().set("DEPRECATION_WARNINGS_ENABLED", false);
-  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
-}
-function deprecationWarn(msg) {
-  if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
-    console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
-  }
-}
-setDeprecationWarningFn(deprecationWarn);
-function disposeVariables() {
-  ENGINE.disposeVariables();
-}
-function engine15() {
-  return ENGINE;
-}
-function memory() {
-  return ENGINE.memory();
-}
-function profile(f) {
-  return ENGINE.profile(f);
-}
-function tidy(nameOrFn, fn) {
-  return ENGINE.tidy(nameOrFn, fn);
-}
-function dispose(container) {
-  const tensors = getTensorsInContainer(container);
-  tensors.forEach((tensor17) => tensor17.dispose());
-}
-function keep(result) {
-  return ENGINE.keep(result);
-}
-function time(f) {
-  return ENGINE.time(f);
-}
-function setBackend(backendName) {
-  return ENGINE.setBackend(backendName);
-}
-function ready() {
-  return ENGINE.ready();
-}
-function getBackend() {
-  return ENGINE.backendName;
-}
-function removeBackend(name) {
-  ENGINE.removeBackend(name);
-}
-function findBackend(name) {
-  return ENGINE.findBackend(name);
-}
-function findBackendFactory(name) {
-  return ENGINE.findBackendFactory(name);
-}
-function registerBackend(name, factory, priority = 1) {
-  return ENGINE.registerBackend(name, factory, priority);
-}
-function backend() {
-  return ENGINE.backend;
-}
-function setPlatform(platformName, platform) {
-  env().setPlatform(platformName, platform);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function add_(a, b) {
-  let $a = convertToTensor(a, "a", "add");
-  let $b = convertToTensor(b, "b", "add");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.add($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Add);
-}
-const add2 = op({add_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floorDiv_(a, b) {
-  let $a = convertToTensor(a, "a", "floorDiv");
-  let $b = convertToTensor(b, "b", "floorDiv");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.floorDiv($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, FloorDiv);
-}
-const floorDiv = op({floorDiv_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function div_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "int32" && $b.dtype === "int32") {
-    return floorDiv($a, $b);
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.realDivide($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, Div, attrs);
-}
-const div = op({div_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mul_(a, b) {
-  let $a = convertToTensor(a, "a", "mul");
-  let $b = convertToTensor(b, "b", "mul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.multiply($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Multiply);
-}
-const mul = op({mul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function abs_(x) {
-  const $x = convertToTensor(x, "x", "abs");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "complex64") {
-      return backend2.complexAbs($x);
-    }
-    return backend2.abs($x);
-  }, inputs, null, Abs);
-}
-const abs = op({abs_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acos_(x) {
-  const $x = convertToTensor(x, "x", "acos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acos);
-}
-const acos = op({acos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function acosh_(x) {
-  const $x = convertToTensor(x, "x", "acosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.acosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Acosh);
-}
-const acosh = op({acosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addN_(tensors) {
-  assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
-  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
-  const firstTensor = $tensors[0];
-  $tensors.forEach((t) => {
-    if (t.dtype !== firstTensor.dtype) {
-      throw new Error("All tensors passed to tf.addN() must have the same dtype");
-    }
-  });
-  $tensors.forEach((t) => {
-    if (!arraysEqual(t.shape, firstTensor.shape)) {
-      throw new Error("All tensors passed to tf.addN() must have the same shape");
-    }
-  });
-  const forward = (backend2, save) => {
-    const res = backend2.addN($tensors);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  return ENGINE.runKernelFunc(forward, inputs, null, AddN);
-}
-const addN = op({addN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function axesAreInnerMostDims(axes, rank) {
-  for (let i = 0; i < axes.length; ++i) {
-    if (axes[axes.length - i - 1] !== rank - 1 - i) {
-      return false;
-    }
-  }
-  return true;
-}
-function combineLocations(outputLoc, reduceLoc, axes) {
-  const rank = outputLoc.length + reduceLoc.length;
-  const loc = [];
-  let outIdx = 0;
-  let reduceIdx = 0;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      loc.push(outputLoc[outIdx++]);
-    } else {
-      loc.push(reduceLoc[reduceIdx++]);
-    }
-  }
-  return loc;
-}
-function computeOutAndReduceShapes(aShape, axes) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      outShape.push(aShape[dim]);
-    }
-  }
-  const reduceShape = axes.map((dim) => aShape[dim]);
-  return [outShape, reduceShape];
-}
-function expandShapeToKeepDim(shape, axes) {
-  const reduceSubShape = axes.map((x) => 1);
-  return combineLocations(shape, reduceSubShape, axes);
-}
-function assertAxesAreInnerMostDims(msg, axes, rank) {
-  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
-}
-function getAxesPermutation(axes, rank) {
-  if (axesAreInnerMostDims(axes, rank)) {
-    return null;
-  }
-  const result = [];
-  for (let i = 0; i < rank; ++i) {
-    if (axes.indexOf(i) === -1) {
-      result.push(i);
-    }
-  }
-  axes.forEach((axis) => result.push(axis));
-  return result;
-}
-function getUndoAxesPermutation(axes) {
-  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
-}
-function getInnerMostAxes(numAxes, rank) {
-  const res = [];
-  for (let i = rank - numAxes; i < rank; ++i) {
-    res.push(i);
-  }
-  return res;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/all.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function all_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "all", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.all($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, All, attrs);
-}
-const all = op({all_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/any.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function any_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "any", "bool");
-  const forward = (backend2) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const res = backend2.any($x, axes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(res.shape, origAxes);
-      return reshape(res, newShape);
-    }
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Any, attrs);
-}
-const any = op({any_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMax_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMax");
-  const forward = (backend2, save) => {
-    save([$x]);
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMax($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMax, attrs);
-}
-const argMax = op({argMax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function argMin_(x, axis = 0) {
-  let $x = convertToTensor(x, "x", "argMin");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if (axis == null) {
-      axis = 0;
-    }
-    let axes = parseAxisParam(axis, $x.shape);
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    if (permutedAxes != null) {
-      $x = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    return backend2.argMin($x, axes[0]);
-  };
-  const inputs = {x: $x};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, ArgMin, attrs);
-}
-const argMin = op({argMin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asin_(x) {
-  const $x = convertToTensor(x, "x", "asin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asin);
-}
-const asin = op({asin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function asinh_(x) {
-  const $x = convertToTensor(x, "x", "asinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.asinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Asinh);
-}
-const asinh = op({asinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan_(x) {
-  const $x = convertToTensor(x, "x", "atan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atan);
-}
-const atan = op({atan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atan2_(a, b) {
-  let $a = convertToTensor(a, "a", "atan2");
-  let $b = convertToTensor(b, "b", "atan2");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.atan2($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Atan2);
-}
-const atan2 = op({atan2_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function atanh_(x) {
-  const $x = convertToTensor(x, "x", "atanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.atanh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Atanh);
-}
-const atanh = op({atanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function computeDilation2DInfo(inputShape, filterShape, strides, pad8, dataFormat = "NHWC", dilations) {
-  const inputChannels = inputShape[3];
-  const $filterShape = [...filterShape, inputChannels];
-  const $dataFormat = convertConv2DDataFormat(dataFormat);
-  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad8, null, null, $dataFormat);
-}
-function computePool2DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "channelsLast") {
-  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
-  let filterShape;
-  if (dataFormat === "channelsLast") {
-    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
-  } else if (dataFormat === "channelsFirst") {
-    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, false, dataFormat);
-}
-function computePool3DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "NDHWC") {
-  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
-  let filterShape;
-  let $dataFormat;
-  if (dataFormat === "NDHWC") {
-    $dataFormat = "channelsLast";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
-  } else if (dataFormat === "NCDHW") {
-    $dataFormat = "channelsFirst";
-    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, false, $dataFormat, roundingMode);
-}
-function computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, depthwise = false, dataFormat = "channelsLast") {
-  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideHeight, strideWidth] = parseTupleParam(strides);
-  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inHeight,
-    inWidth,
-    inChannels,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideHeight,
-    strideWidth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, depthwise = false, dataFormat = "channelsLast", roundingMode) {
-  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
-  if (dataFormat === "channelsLast") {
-    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === "channelsFirst") {
-    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
-  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
-  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
-  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-  let outShape;
-  if (dataFormat === "channelsFirst") {
-    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
-  } else if (dataFormat === "channelsLast") {
-    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
-  }
-  return {
-    batchSize,
-    dataFormat,
-    inDepth,
-    inHeight,
-    inWidth,
-    inChannels,
-    outDepth,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideDepth,
-    strideHeight,
-    strideWidth,
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterDepth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationDepth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputRows = inShape[0];
-  const inputCols = inShape[1];
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputRows, outputCols];
-}
-function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputDepth = inShape[0];
-  const inputRows = inShape[1];
-  const inputCols = inShape[2];
-  const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
-  const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
-  return [outputDepths, outputRows, outputCols, outChannels];
-}
-function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
-  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
-  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
-}
-function parseTupleParam(param) {
-  if (typeof param === "number") {
-    return [param, param, param];
-  }
-  if (param.length === 2) {
-    return [param[0], param[1], 1];
-  }
-  return param;
-}
-function parse3TupleParam(param) {
-  return typeof param === "number" ? [param, param, param] : param;
-}
-function getEffectiveFilterSize(filterSize, dilation) {
-  if (dilation <= 1) {
-    return filterSize;
-  }
-  return filterSize + (filterSize - 1) * (dilation - 1);
-}
-function getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
-  let padInfo;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {top: pad8, bottom: pad8, left: pad8, right: pad8, type: padType};
-    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad8, roundingMode);
-    outHeight = outShape[0];
-    outWidth = outShape[1];
-  } else if (pad8 === "same") {
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
-    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else if (typeof pad8 === "object") {
-    const top = dataFormat === "channelsLast" ? pad8[1][0] : pad8[2][0];
-    const bottom = dataFormat === "channelsLast" ? pad8[1][1] : pad8[2][1];
-    const left = dataFormat === "channelsLast" ? pad8[2][0] : pad8[3][0];
-    const right = dataFormat === "channelsLast" ? pad8[2][1] : pad8[3][1];
-    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
-    padInfo = {top, bottom, left, right, type: padType};
-    outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
-    outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outHeight, outWidth};
-}
-function get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
-  let padInfo;
-  let outDepth;
-  let outHeight;
-  let outWidth;
-  if (typeof pad8 === "number") {
-    const padType = pad8 === 0 ? "VALID" : "NUMBER";
-    padInfo = {
-      top: pad8,
-      bottom: pad8,
-      left: pad8,
-      right: pad8,
-      front: pad8,
-      back: pad8,
-      type: padType
-    };
-    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad8, roundingMode);
-    outDepth = outShape[0];
-    outHeight = outShape[1];
-    outWidth = outShape[2];
-  } else if (pad8 === "same") {
-    outDepth = Math.ceil(inDepth / strideDepth);
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
-    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
-    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
-    const front = Math.floor(padAlongDepth / 2);
-    const back = padAlongDepth - front;
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, front, back, type: "SAME"};
-  } else if (pad8 === "valid") {
-    padInfo = {
-      top: 0,
-      bottom: 0,
-      left: 0,
-      right: 0,
-      front: 0,
-      back: 0,
-      type: "VALID"
-    };
-    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad8}`);
-  }
-  return {padInfo, outDepth, outHeight, outWidth};
-}
-function conditionalRound(value, roundingMode) {
-  if (!roundingMode) {
-    return value;
-  }
-  switch (roundingMode) {
-    case "round":
-      return Math.round(value);
-    case "ceil":
-      return Math.ceil(value);
-    case "floor":
-      return Math.floor(value);
-    default:
-      throw new Error(`Unknown roundingMode ${roundingMode}`);
-  }
-}
-function tupleValuesAreOne(param) {
-  const [dimA, dimB, dimC] = parseTupleParam(param);
-  return dimA === 1 && dimB === 1 && dimC === 1;
-}
-function eitherStridesOrDilationsAreOne(strides, dilations) {
-  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
-}
-function convertConv2DDataFormat(dataFormat) {
-  if (dataFormat === "NHWC") {
-    return "channelsLast";
-  } else if (dataFormat === "NCHW") {
-    return "channelsFirst";
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "avgPool", "float32");
-  const dilations = 1;
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    save([x4D]);
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      return x4D.clone();
-    }
-    return backend2.avgPool(x4D, convInfo);
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool, attrs);
-  res = cast(res, $x.dtype);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPool = op({avgPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3d_(x, filterSize, strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "avgPool3d", "float32");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    save([x5D]);
-    return backend2.avgPool3d(x5D, convInfo);
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3D, attrs);
-  res = cast(res, x5D.dtype);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3d = op({avgPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function assertParamsConsistent(shapes, axis) {
-  const rank = shapes[0].length;
-  shapes.forEach((shape, i) => {
-    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
-  });
-  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
-  const firstShape = shapes[0];
-  shapes.forEach((shape, i) => {
-    for (let r = 0; r < rank; r++) {
-      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
-    }
-  });
-}
-function computeOutShape2(shapes, axis) {
-  const outputShape = shapes[0].slice();
-  for (let i = 1; i < shapes.length; i++) {
-    outputShape[axis] += shapes[i][axis];
-  }
-  return outputShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function concat_(tensors, axis = 0) {
-  assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
-  let $tensors = convertToTensorArray(tensors, "tensors", "concat");
-  if ($tensors[0].dtype === "complex64") {
-    $tensors.forEach((tensor17) => {
-      if (tensor17.dtype !== "complex64") {
-        throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor17.dtype}. `);
-      }
-    });
-  }
-  const $axis = parseAxisParam(axis, $tensors[0].shape)[0];
-  const outShape = computeOutShape2($tensors.map((t) => t.shape), $axis);
-  if (sizeFromShape(outShape) === 0) {
-    return tensor4([], outShape);
-  }
-  $tensors = $tensors.filter((t) => t.size > 0);
-  if ($tensors.length === 1) {
-    return $tensors[0];
-  }
-  const shapes = $tensors.map((t) => t.shape);
-  assertParamsConsistent(shapes, $axis);
-  const forward = (backend2, save) => {
-    const res = backend2.concat($tensors, $axis);
-    save($tensors);
-    return res;
-  };
-  const inputs = $tensors;
-  const attr = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Concat, attr);
-}
-const concat = op({concat_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoid_(x) {
-  const $x = convertToTensor(x, "x", "sigmoid");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sigmoid($x);
-    save([res]);
-    return res;
-  }, inputs, null, Sigmoid);
-}
-const sigmoid = op({sigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice");
-  if ($x.rank === 0) {
-    throw new Error("Slicing scalar is not possible");
-  }
-  const [begin_, size_] = parseSliceParams($x, begin, size);
-  assertParamsValid($x, begin_, size_);
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.slice($x, begin_, size_);
-  };
-  const inputs = {x: $x};
-  const attrs = {begin, size};
-  return ENGINE.runKernelFunc(forward, inputs, null, Slice, attrs);
-}
-const slice = op({slice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tanh_(x) {
-  const $x = convertToTensor(x, "x", "tanh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.tanh($x);
-    save([y]);
-    return y;
-  }, inputs, null, Tanh);
-}
-const tanh2 = op({tanh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
-  const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
-  const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
-  const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
-  const $data = convertToTensor(data, "data", "basicLSTMCell");
-  const $c = convertToTensor(c, "c", "basicLSTMCell");
-  const $h = convertToTensor(h, "h", "basicLSTMCell");
-  const combined = concat([$data, $h], 1);
-  const weighted = matMul(combined, $lstmKernel);
-  const res = add2(weighted, $lstmBias);
-  const batchSize = res.shape[0];
-  const sliceCols = res.shape[1] / 4;
-  const sliceSize = [batchSize, sliceCols];
-  const i = slice(res, [0, 0], sliceSize);
-  const j = slice(res, [0, sliceCols], sliceSize);
-  const f = slice(res, [0, sliceCols * 2], sliceSize);
-  const o = slice(res, [0, sliceCols * 3], sliceSize);
-  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
-  const newH = mul(tanh2(newC), sigmoid(o));
-  return [newC, newH];
-}
-const basicLSTMCell = op({basicLSTMCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchToSpaceND_(x, blockShape, crops) {
-  const $x = convertToTensor(x, "x", "batchToSpaceND");
-  const prod3 = blockShape.reduce((a, b) => a * b);
-  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
-  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
-  assert($x.shape[0] % prod3 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod3}`);
-  const forward = (backend2) => {
-    return backend2.batchToSpaceND($x, blockShape, crops);
-  };
-  const inputs = {x: $x};
-  const attrs = {blockShape, crops};
-  return ENGINE.runKernelFunc(forward, inputs, null, BatchToSpaceND, attrs);
-}
-const batchToSpaceND = op({batchToSpaceND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
-function xAs4D(x) {
-  let x4D;
-  if (x.rank === 0 || x.rank === 1) {
-    x4D = reshape(x, [1, 1, 1, x.size]);
-  } else if (x.rank === 2) {
-    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  } else {
-    x4D = x;
-  }
-  return x4D;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 1e-3;
-  }
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
-  assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
-  assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-  const x4D = xAs4D($x);
-  const forward = (backend2, save) => {
-    save([x4D, $mean, $variance, $scale]);
-    return backend2.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);
-  };
-  const inputs = {
-    x: x4D,
-    scale: $scale,
-    offset: $offset,
-    mean: $mean,
-    variance: $variance
-  };
-  const attrs = {varianceEpsilon};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, FusedBatchNorm, attrs);
-  return reshape(res, $x.shape);
-}
-function as1DOr4D(x) {
-  if (x == null) {
-    return null;
-  }
-  if (x.rank === 0) {
-    return reshape(x, [x.size]);
-  } else if (x.rank === 1) {
-    return x;
-  } else if (x.rank === 2) {
-    return reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  return x;
-}
-const batchNorm = op({batchNorm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
-function batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
-  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm2d = op({batchNorm2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
-function batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
-  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm3d = op({batchNorm3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
-function batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
-  const $x = convertToTensor(x, "x", "batchNorm");
-  const $mean = convertToTensor(mean5, "mean", "batchNorm");
-  const $variance = convertToTensor(variance, "variance", "batchNorm");
-  let $scale;
-  if (scale2 != null) {
-    $scale = convertToTensor(scale2, "scale", "batchNorm");
-  }
-  let $offset;
-  if (offset != null) {
-    $offset = convertToTensor(offset, "offset", "batchNorm");
-  }
-  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
-  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
-  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-const batchNorm4d = op({batchNorm4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function broadcastTo_(x, shape) {
-  let input = convertToTensor(x, "broadcastTo", "x");
-  const xShape = input.shape;
-  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
-    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
-  }
-  if (shape.length < input.rank) {
-    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);
-  }
-  if (shape.length > input.rank) {
-    const newShape = input.shape.slice();
-    while (newShape.length < shape.length) {
-      newShape.unshift(1);
-    }
-    input = reshape(input, newShape);
-  }
-  const inputShape = input.shape;
-  const reps = Array.from(shape);
-  for (let i = shape.length - 1; i >= 0; i--) {
-    if (inputShape[i] === shape[i]) {
-      reps[i] = 1;
-    } else if (input.shape[i] !== 1) {
-      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
-    }
-  }
-  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
-  if (axes.length === 0) {
-    return clone(input);
-  }
-  const forward = (backend2) => backend2.tile(input, reps);
-  const inputs = {x: input};
-  const attrs = {shape, inputShape};
-  return ENGINE.runKernelFunc(forward, inputs, null, BroadcastTo, attrs);
-}
-const broadcastTo = op({broadcastTo_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ceil_(x) {
-  const $x = convertToTensor(x, "x", "ceil");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.ceil($x), inputs, null, Ceil);
-}
-const ceil = op({ceil_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function clipByValue_(x, clipValueMin, clipValueMax) {
-  const $x = convertToTensor(x, "x", "clipByValue");
-  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
-  const inputs = {x: $x};
-  const attrs = {clipValueMin, clipValueMax};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.clip($x, clipValueMin, clipValueMax);
-    save([$x]);
-    return res;
-  }, inputs, null, ClipByValue, attrs);
-}
-const clipByValue = op({clipByValue_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
-function concat1d_(tensors) {
-  return concat(tensors, 0);
-}
-const concat1d = op({concat1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
-function concat2d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat2d = op({concat2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
-function concat3d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat3d = op({concat3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
-function concat4d_(tensors, axis) {
-  return concat(tensors, axis);
-}
-const concat4d = op({concat4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const forward = (backend2, save) => {
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2d(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2D, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2d = op({conv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
-function conv1d_(x, filter, stride, pad8, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv1d");
-  const $filter = convertToTensor(filter, "filter", "conv1d");
-  let x3D = $x;
-  let reshapedTo3D = false;
-  if ($x.rank === 2) {
-    reshapedTo3D = true;
-    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
-  }
-  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
-  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
-  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
-  assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
-  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
-  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
-  const strides = [1, stride];
-  const dilations = [1, dilation];
-  const conv2dDataFormat = "NHWC";
-  const res = conv2d(input4D, filter4D, strides, pad8, conv2dDataFormat, dilations, dimRoundingMode);
-  if (reshapedTo3D) {
-    return reshape(res, [res.shape[2], res.shape[3]]);
-  }
-  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
-}
-const conv1d = op({conv1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropInput_(xShape, dy, filter, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape4D = xShape;
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
-  }
-  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
-  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
-  const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
-  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    const res2 = backend2.conv2dDerInput(dy4D, filter, convInfo);
-    save([dy4D, filter]);
-    return res2;
-  };
-  const inputs = {dy: dy4D, filter};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape: xShape4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropInput, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const conv2DBackpropInput = op({conv2DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
-function conv2dTranspose_(x, filter, outputShape, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "conv2dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
-  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad8, "NHWC", dimRoundingMode);
-}
-const conv2dTranspose = op({conv2dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3d_(x, filter, strides, pad8, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
-  const $x = convertToTensor(x, "x", "conv3d");
-  const $filter = convertToTensor(filter, "filter", "conv3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
-  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
-  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
-  const forward = (backend2, save) => {
-    const convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad8);
-    const res2 = backend2.conv3d(x5D, $filter, convInfo);
-    save([x5D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x5D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3d = op({conv3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropInput_(xShape, dy, filter, strides, pad8) {
-  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
-  let xShape5D = xShape;
-  let dy5D = dy;
-  let reshapedTo5D = false;
-  if (dy.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
-  }
-  const inDepth = xShape5D[4];
-  const outDepth = dy5D.shape[4];
-  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
-  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
-  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
-  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad8);
-    return backend2.conv3dDerInput(dy5D, filter, convInfo);
-  };
-  const inputs = {dy: dy5D};
-  const attrs = {pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const conv3DBackpropInput = op({conv3DBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
-function conv3dTranspose_(x, filter, outputShape, strides, pad8) {
-  const $x = convertToTensor(x, "x", "conv3dTranspose");
-  const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
-  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad8);
-}
-const conv3dTranspose = op({conv3dTranspose_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cos_(x) {
-  const $x = convertToTensor(x, "x", "cos");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cos($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cos);
-}
-const cos = op({cos_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cosh_(x) {
-  const $x = convertToTensor(x, "x", "cosh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.cosh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Cosh);
-}
-const cosh = op({cosh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cumsum_(x, axis = 0, exclusive = false, reverse9 = false) {
-  const $x = convertToTensor(x, "x", "cumsum");
-  const forward = (backend2, save) => {
-    const permutation = getAxesPermutation([axis], $x.rank);
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-    }
-    const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
-    let value = backend2.cumsum(permutedX, permutedAxis, exclusive, reverse9);
-    save([$x]);
-    if (permutation != null) {
-      const reversePermutation = getUndoAxesPermutation(permutation);
-      value = transpose(value, reversePermutation);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, exclusive, reverse: reverse9};
-  return ENGINE.runKernelFunc(forward, inputs, null, Cumsum, attrs);
-}
-const cumsum = op({cumsum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "depthToSpace");
-  const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
-  const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
-  const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
-  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
-    ${$x.shape}`);
-  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
-    ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`);
-  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
-  const forward = (backend2) => backend2.depthToSpace($x, blockSize, dataFormat);
-  const inputs = {x: $x};
-  const attrs = {blockSize, dataFormat};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthToSpace, attrs);
-}
-const depthToSpace = op({depthToSpace_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1];
-    }
-    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-    const res2 = backend2.depthwiseConv2D(x4D, $filter, convInfo);
-    save([x4D, $filter]);
-    return res2;
-  };
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNative, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2d = op({depthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function diag_(x) {
-  const $x = convertToTensor(x, "x", "diag");
-  const forward = (backend2) => {
-    const flat = reshape($x, [$x.size]);
-    const result = backend2.diag(flat);
-    const outShape = [...x.shape, ...x.shape];
-    return reshape(result, outShape);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Diag);
-}
-const diag = op({diag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dilation2d_(x, filter, strides, pad8, dilations = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "dilation2d");
-  const $filter = convertToTensor(filter, "filter", "dilation2d");
-  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
-  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
-  assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    reshapedTo4D = true;
-  }
-  const inputs = {x: x4D, filter: $filter};
-  const attrs = {strides, pad: pad8, dilations};
-  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const dilation2d = op({dilation2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getBroadcastDims(inShape, outShape) {
-  const inRank = inShape.length;
-  const dims = [];
-  for (let i = 0; i < inRank; i++) {
-    const dim = inRank - 1 - i;
-    const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i] || 1;
-    if (b > 1 && a === 1) {
-      dims.unshift(dim);
-    }
-  }
-  return dims;
-}
-function getReductionAxes(inShape, outShape) {
-  const result = [];
-  for (let i = 0; i < outShape.length; i++) {
-    const inDim = inShape[inShape.length - i - 1];
-    const outAxis = outShape.length - i - 1;
-    const outDim = outShape[outAxis];
-    if (inDim == null || inDim === 1 && outDim > 1) {
-      result.unshift(outAxis);
-    }
-  }
-  return result;
-}
-function assertAndGetBroadcastShape(shapeA, shapeB) {
-  const result = [];
-  const l = Math.max(shapeA.length, shapeB.length);
-  for (let i = 0; i < l; i++) {
-    let a = shapeA[shapeA.length - i - 1];
-    if (a == null) {
-      a = 1;
-    }
-    let b = shapeB[shapeB.length - i - 1];
-    if (b == null) {
-      b = 1;
-    }
-    if (a === 1) {
-      result.unshift(b);
-    } else if (b === 1) {
-      result.unshift(a);
-    } else if (a !== b) {
-      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
-      throw Error(errMsg);
-    } else {
-      result.unshift(a);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function equal_(a, b) {
-  let $a = convertToTensor(a, "a", "equal");
-  let $b = convertToTensor(b, "b", "equal");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.equal($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Equal);
-}
-const equal = op({equal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function where_(condition, a, b) {
-  const $a = convertToTensor(a, "a", "where");
-  const $b = convertToTensor(b, "b", "where");
-  const $condition = convertToTensor(condition, "condition", "where", "bool");
-  const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
-  const $broadcastedA = broadcastTo($a, broadcastShape);
-  const $broadcastedB = broadcastTo($b, broadcastShape);
-  if ($condition.rank === 1) {
-    assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
-  }
-  if ($condition.rank !== 1) {
-    assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
-  }
-  const forward = (backend2, save) => {
-    const res = backend2.select($condition, $broadcastedA, $broadcastedB);
-    save([$condition]);
-    return res;
-  };
-  const inputs = {
-    condition: $condition,
-    t: $broadcastedA,
-    e: $broadcastedB
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, SelectV2);
-}
-const where = op({where_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zerosLike_(x) {
-  const $x = convertToTensor(x, "x", "zerosLike");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.zerosLike($x), inputs, null, ZerosLike);
-}
-const zerosLike = op({zerosLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function divNoNan_(a, b) {
-  let $a = convertToTensor(a, "a", "div");
-  let $b = convertToTensor(b, "b", "div");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const divResult = div($a, $b);
-  const zeros9 = zerosLike(divResult);
-  const bEqualsZero = equal($b, zeros9);
-  return where(bEqualsZero, zeros9, divResult);
-}
-const divNoNan = op({divNoNan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dot_(t1, t2) {
-  const $t1 = convertToTensor(t1, "t1", "dot");
-  const $t2 = convertToTensor(t2, "t2", "dot");
-  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
-  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
-  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
-  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
-  if ($t1.rank === 1 && $t2.rank === 1) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, []);
-  } else if ($t1.rank === 1 && $t2.rank === 2) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else if ($t1.rank === 2 && $t2.rank === 1) {
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul($t1, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else {
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul($t1, t22D);
-    return t1t2;
-  }
-}
-const dot = op({dot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function elu_(x) {
-  const $x = convertToTensor(x, "x", "elu");
-  const forward = (backend2, save) => {
-    const y = backend2.elu($x);
-    save([y]);
-    return y;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Elu);
-}
-const elu = op({elu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function erf_(x) {
-  let $x = convertToTensor(x, "x", "erf");
-  assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
-  if ($x.dtype === "int32") {
-    $x = cast($x, "float32");
-  }
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.erf($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Erf);
-}
-const erf = op({erf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function exp_(x) {
-  const $x = convertToTensor(x, "x", "exp");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.exp($x);
-    save([res]);
-    return res;
-  }, inputs, null, Exp);
-}
-const exp = op({exp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expandDims_(x, axis = 0) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "expandDims", parseAs);
-  assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
-  const newShape = $x.shape.slice();
-  if (axis < 0) {
-    assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);
-    axis = $x.rank + axis + 1;
-  }
-  newShape.splice(axis, 0, 1);
-  return reshape($x, newShape);
-}
-const expandDims = op({expandDims_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function expm1_(x) {
-  const $x = convertToTensor(x, "x", "expm1");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.expm1($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Expm1);
-}
-const expm1 = op({expm1_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile_(x, reps) {
-  const parseAs = null;
-  const $x = convertToTensor(x, "x", "tile", parseAs);
-  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
-  const forward = (backend2, save) => {
-    const res = backend2.tile($x, reps);
-    save([$x]);
-    return res;
-  };
-  const inputsToSave = [$x];
-  const inputs = {x: $x};
-  const attrs = {reps};
-  return ENGINE.runKernelFunc(forward, inputs, null, Tile, attrs, inputsToSave);
-}
-const tile = op({tile_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function eye_(numRows, numColumns, batchShape, dtype = "float32") {
-  if (numColumns == null) {
-    numColumns = numRows;
-  }
-  const buff = buffer([numRows, numColumns], dtype);
-  const n = numRows <= numColumns ? numRows : numColumns;
-  for (let i = 0; i < n; ++i) {
-    buff.set(1, i, i);
-  }
-  const out = reshape(buff.toTensor(), [numRows, numColumns]);
-  if (batchShape == null) {
-    return out;
-  } else {
-    if (batchShape.length === 1) {
-      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
-    } else if (batchShape.length === 2) {
-      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
-    } else if (batchShape.length === 3) {
-      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
-        batchShape[0],
-        batchShape[1],
-        batchShape[2],
-        1,
-        1
-      ]);
-    } else {
-      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
-    }
-  }
-}
-const eye = op({eye_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fill(shape, value, dtype) {
-  const attrs = {shape, value, dtype};
-  return ENGINE.runKernelFunc((backend2) => backend2.fill(shape, value, dtype), {}, null, Fill, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function floor_(x) {
-  const $x = convertToTensor(x, "x", "floor");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.floor($x), inputs, null, Floor);
-}
-const floor = op({floor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const PARALLELIZE_THRESHOLD = 30;
-function computeOptimalWindowSize(inSize) {
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    return inSize;
-  }
-  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
-const segment_util_exports = {};
-__export(segment_util_exports, {
-  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
-  computeOutShape: () => computeOutShape3,
-  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function segOpComputeOptimalWindowSize(inSize, numSegments) {
-  let done = false;
-  let res;
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    res = inSize;
-    done = true;
-  } else {
-    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-  }
-  while (!done) {
-    if (res > numSegments || res === inSize) {
-      done = true;
-    } else {
-      res = nearestDivisor(inSize, res + 1);
-    }
-  }
-  return res;
-}
-function computeOutShape3(aShape, axis, numSegments) {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (dim !== axis) {
-      outShape.push(aShape[dim]);
-    } else {
-      outShape.push(numSegments);
-    }
-  }
-  return outShape;
-}
-function collectGatherOpShapeInfo(x, indices, axis) {
-  const dimSize = x.shape[axis];
-  const outputShape = [];
-  let batchSize = 1;
-  let sliceSize = 1;
-  for (let i = 0; i < axis; i++) {
-    outputShape.push(x.shape[i]);
-    batchSize *= x.shape[i];
-  }
-  for (let i = 0; i < indices.rank; i++) {
-    outputShape.push(indices.shape[i]);
-  }
-  for (let i = axis + 1; i < x.rank; i++) {
-    outputShape.push(x.shape[i]);
-    sliceSize *= x.shape[i];
-  }
-  return {batchSize, sliceSize, dimSize, outputShape};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gather_(x, indices, axis = 0) {
-  const $x = convertToTensor(x, "x", "gather");
-  const $indices = convertToTensor(indices, "indices", "gather", "int32");
-  const inputs = {x: $x, indices: $indices};
-  const attrs = {axis};
-  const forward = (backend2, save) => {
-    const parsedAxis = parseAxisParam(axis, $x.shape)[0];
-    const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);
-    const res = backend2.gather($x, reshape($indices, [$indices.size]), parsedAxis);
-    save([$x, $indices]);
-    return reshape(res, shapeInfo.outputShape);
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherV2, attrs);
-}
-const gather = op({gather_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greater_(a, b) {
-  let $a = convertToTensor(a, "a", "greater");
-  let $b = convertToTensor(b, "b", "greater");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.greater($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Greater);
-}
-const greater = op({greater_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function greaterEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "greaterEqual");
-  let $b = convertToTensor(b, "b", "greaterEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.greaterEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, GreaterEqual);
-}
-const greaterEqual = op({greaterEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function imag_(input) {
-  const $input = convertToTensor(input, "input", "imag");
-  const forward = (backend2) => {
-    return backend2.imag($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Imag);
-}
-const imag = op({imag_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isFinite_(x) {
-  const $x = convertToTensor(x, "x", "isFinite");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isFinite($x), inputs, null, IsFinite);
-}
-const isFinite2 = op({isFinite_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isInf_(x) {
-  const $x = convertToTensor(x, "x", "isInf");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isInf($x), inputs, null, IsInf);
-}
-const isInf = op({isInf_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isNaN_(x) {
-  const $x = convertToTensor(x, "x", "isNaN");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.isNaN($x), inputs, null, IsNan);
-}
-const isNaN2 = op({isNaN_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maximum_(a, b) {
-  let $a = convertToTensor(a, "a", "maximum");
-  let $b = convertToTensor(b, "b", "maximum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.maximum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Maximum);
-}
-const maximum = op({maximum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scalar(value, dtype) {
-  if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
-    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
-  }
-  if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
-    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-  }
-  const shape = [];
-  const inferredShape = [];
-  return makeTensor(value, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function leakyRelu_(x, alpha = 0.2) {
-  const $x = convertToTensor(x, "x", "leakyRelu");
-  return maximum(mul(scalar(alpha), $x), $x);
-}
-const leakyRelu = op({leakyRelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function less_(a, b) {
-  let $a = convertToTensor(a, "a", "less");
-  let $b = convertToTensor(b, "b", "less");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.less($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Less);
-}
-const less = op({less_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function lessEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "lessEqual");
-  let $b = convertToTensor(b, "b", "lessEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.lessEqual($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, LessEqual);
-}
-const lessEqual = op({lessEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function linspace(start, stop, num) {
-  if (num <= 0) {
-    throw new Error("The number of values should be positive.");
-  }
-  const attrs = {start, stop, num};
-  return ENGINE.runKernelFunc((backend2) => backend2.linspace(start, stop, num), {}, null, LinSpace, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const $x = convertToTensor(x, "x", "localResponseNormalization");
-  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${$x.rank}.`);
-  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  const forward = (backend2, save) => {
-    const y = backend2.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {depthRadius, bias, alpha, beta};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, LRN, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  } else {
-    return res;
-  }
-}
-const localResponseNormalization = op({localResponseNormalization_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log_(x) {
-  const $x = convertToTensor(x, "x", "log");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log);
-}
-const log = op({log_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function log1p_(x) {
-  const $x = convertToTensor(x, "x", "log1p");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.log1p($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Log1p);
-}
-const log1p = op({log1p_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function grad(f) {
-  assert(isFunction(f), () => "The f passed in grad(f) must be a function");
-  return (x, dy) => {
-    const $x = convertToTensor(x, "x", "tf.grad", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
-      }
-      checkGrads(grads2);
-      return grads2[0];
-    });
-  };
-}
-function grads(f) {
-  assert(isFunction(f), () => "The f passed in grads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    const $args = convertToTensorArray(args, "args", "tf.grads", null);
-    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
-    return ENGINE.tidy(() => {
-      const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
-      if ($dy != null) {
-        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-      }
-      checkGrads(grads2);
-      return grads2;
-    });
-  };
-}
-function valueAndGrad(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
-  return (x, dy) => {
-    assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
-    const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
-    checkGrads(grads2);
-    return {grad: grads2[0], value};
-  };
-}
-function valueAndGrads(f) {
-  assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
-  return (args, dy) => {
-    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
-    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
-    const res = ENGINE.gradients(() => f(...args), args, dy);
-    if (dy != null) {
-      assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
-    }
-    checkGrads(res.grads);
-    return res;
-  };
-}
-function variableGrads(f, varList) {
-  assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
-  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
-  const specifiedVarList = varList != null;
-  if (!specifiedVarList) {
-    varList = [];
-    for (const varName in ENGINE.registeredVariables) {
-      varList.push(ENGINE.registeredVariables[varName]);
-    }
-  }
-  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable3) => !variable3.trainable) : null;
-  const originalVarCount = varList.length;
-  varList = varList.filter((variable3) => variable3.trainable);
-  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
-  const allowNoGradients = true;
-  const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
-  assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
-  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
-  const namedGrads = {};
-  varList.forEach((v, i) => {
-    if (grads2[i] != null) {
-      namedGrads[v.name] = grads2[i];
-    }
-  });
-  if (specifiedNonTrainable != null) {
-    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
-  }
-  return {value, grads: namedGrads};
-}
-function customGrad(f) {
-  return ENGINE.customGrad(f);
-}
-function checkGrads(grads2) {
-  const numNullGradients = grads2.filter((g) => g == null).length;
-  if (numNullGradients > 0) {
-    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function neg_(x) {
-  const $x = convertToTensor(x, "x", "neg");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.neg($x), inputs, null, Negate);
-}
-const neg = op({neg_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softplus_(x) {
-  const $x = convertToTensor(x, "x", "softplus");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.softplus($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Softplus);
-}
-const softplus = op({softplus_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSigmoid_(x) {
-  const $x = convertToTensor(x, "x", "logSigmoid");
-  const customOp = customGrad((x2) => {
-    const value = neg(softplus(neg(x2)));
-    const gradFunc = (dy) => {
-      const derX = mul(dy, sigmoid(neg(x2)));
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const logSigmoid = op({logSigmoid_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function max_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "max");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let maxInput = $x;
-    if (permutedAxes != null) {
-      maxInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, maxInput.rank);
-    }
-    const y = backend2.max(maxInput, axes);
-    if (permutedAxes != null) {
-      maxInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, parseAxisParam(axis, $x.shape));
-      res = reshape(res, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {reductionIndices: axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Max, attrs);
-}
-const max = op({max_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sub_(a, b) {
-  let $a = convertToTensor(a, "a", "sub");
-  let $b = convertToTensor(b, "b", "sub");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.subtract($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sub);
-}
-const sub = op({sub_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sum_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "sum");
-  if ($x.dtype === "bool") {
-    $x = cast($x, "int32");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.sum(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Sum, attrs);
-}
-const sum2 = op({sum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSoftmax_(logits, axis = -1) {
-  const $logits = convertToTensor(logits, "logits", "logSoftmax");
-  if (axis === -1) {
-    axis = $logits.rank - 1;
-  }
-  if (axis !== $logits.rank - 1) {
-    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
-  }
-  const forward = (backend2, save) => {
-    const keepDims = true;
-    const xMax = max(logits, axis, true);
-    const shifted = sub(logits, xMax);
-    const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
-    save([value]);
-    return value;
-  };
-  const inputs = {logits: $logits};
-  const attrs = {axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, LogSoftmax, attrs);
-}
-const logSoftmax = op({logSoftmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logSumExp_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "logSumExp");
-  const axes = parseAxisParam(axis, $x.shape);
-  const xMax = max($x, axes, true);
-  const a = sub($x, xMax);
-  const b = exp(a);
-  const c = sum2(b, axes);
-  const d = log(c);
-  const res = add2(reshape(xMax, d.shape), d);
-  if (keepDims) {
-    const newShape = expandShapeToKeepDim(res.shape, axes);
-    return reshape(res, newShape);
-  }
-  return res;
-}
-const logSumExp = op({logSumExp_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalAnd_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalAnd", "bool");
-  const $b = convertToTensor(b, "b", "logicalAnd", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalAnd($a, $b), inputs, null, LogicalAnd);
-}
-const logicalAnd = op({logicalAnd_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalNot_(x) {
-  const $x = convertToTensor(x, "x", "logicalNot", "bool");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalNot($x), inputs, null, LogicalNot);
-}
-const logicalNot = op({logicalNot_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalOr_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalOr", "bool");
-  const $b = convertToTensor(b, "b", "logicalOr", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc((backend2) => backend2.logicalOr($a, $b), inputs, null, LogicalOr);
-}
-const logicalOr = op({logicalOr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logicalXor_(a, b) {
-  const $a = convertToTensor(a, "a", "logicalXor", "bool");
-  const $b = convertToTensor(b, "b", "logicalXor", "bool");
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
-}
-const logicalXor = op({logicalXor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool_(x, filterSize, strides, pad8, dimRoundingMode) {
-  const $x = convertToTensor(x, "x", "maxPool");
-  const dilations = 1;
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    let y;
-    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
-      y = x4D.clone();
-    } else {
-      y = backend2.maxPool(x4D, convInfo);
-    }
-    save([x4D, y]);
-    return y;
-  };
-  const inputs = {x: x4D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const maxPool = op({maxPool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
-  if (dilations == null) {
-    dilations = [1, 1, 1];
-  } else {
-    deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
-  }
-  const $x = convertToTensor(x, "x", "maxPool3d");
-  let x5D = $x;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2, save) => {
-    if (dilations == null) {
-      dilations = [1, 1, 1];
-    }
-    const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
-    const y = backend2.maxPool3d(x5D, convInfo);
-    save([x5D, y]);
-    return y;
-  };
-  const inputs = {x: x5D};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3D, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3d = op({maxPool3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolWithArgmax_(x, filterSize, strides, pad8, includeBatchInIndex = false) {
-  const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
-  const inputs = {x: $x};
-  const attrs = {filterSize, strides, pad: pad8, includeBatchInIndex};
-  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
-  return {result: result[0], indexes: result[1]};
-}
-const maxPoolWithArgmax = op({maxPoolWithArgmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function zeros(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = zeros(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ones2(shape, dtype = "float32") {
-  if (dtype === "complex64") {
-    const real6 = ones2(shape, "float32");
-    const imag6 = zeros(shape, "float32");
-    return complex(real6, imag6);
-  }
-  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mean_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "mean");
-  const axes = parseAxisParam(axis, $x.shape);
-  const shapes = computeOutAndReduceShapes($x.shape, axes);
-  const reduceShape = shapes[1];
-  const reduceSize = sizeFromShape(reduceShape);
-  const customOp = customGrad((x2) => {
-    const reduceSizeScalar = scalar(reduceSize);
-    const xReduce = reduceSizeScalar.dtype === x2.dtype ? x2 : cast(x2, reduceSizeScalar.dtype);
-    const res = div(xReduce, reduceSizeScalar);
-    const value = sum2(res, axis, keepDims);
-    const gradFunc = (dy) => {
-      const expandedDyShape = x2.shape.slice();
-      axes.forEach((axis2) => {
-        expandedDyShape[axis2] = 1;
-      });
-      const expandedDy = reshape(dy, expandedDyShape);
-      const derX = div(mul(expandedDy, ones2(x2.shape, "float32")), reduceSize);
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-  return customOp($x);
-}
-const mean = op({mean_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/min.js
-function min_(x, axis = null, keepDims = false) {
-  const $x = convertToTensor(x, "x", "min");
-  const forward = (backend2, save) => {
-    const origAxes = parseAxisParam(axis, $x.shape);
-    let axes = origAxes;
-    const permutedAxes = getAxesPermutation(axes, $x.rank);
-    let minInput = $x;
-    if (permutedAxes != null) {
-      minInput = transpose($x, permutedAxes);
-      axes = getInnerMostAxes(axes.length, $x.rank);
-    }
-    const y = backend2.min(minInput, axes);
-    if (permutedAxes != null) {
-      minInput.dispose();
-    }
-    let res = y;
-    if (keepDims) {
-      const expandedShape = expandShapeToKeepDim(res.shape, origAxes);
-      res = reshape(y, expandedShape);
-      y.dispose();
-    }
-    save([$x, res]);
-    return res;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Min, attrs);
-}
-const min = op({min_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function minimum_(a, b) {
-  let $a = convertToTensor(a, "a", "minimum");
-  let $b = convertToTensor(b, "b", "minimum");
-  [$a, $b] = makeTypesMatch($a, $b);
-  if ($a.dtype === "bool") {
-    $a = cast($a, "int32");
-    $b = cast($b, "int32");
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.minimum($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Minimum);
-}
-const minimum = op({minimum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mod_(a, b) {
-  let $a = convertToTensor(a, "a", "mod");
-  let $b = convertToTensor(b, "b", "mod");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const forward = (backend2, save) => {
-    const res = backend2.mod($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, Mod);
-}
-const mod = op({mod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/square.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function square_(x) {
-  const $x = convertToTensor(x, "x", "square");
-  const attrs = {};
-  const inputsToSave = [$x];
-  const outputsToSave = [];
-  return ENGINE.runKernelFunc((backend2, save) => {
-    save([$x]);
-    return backend2.square($x);
-  }, {x: $x}, null, "Square", attrs, inputsToSave, outputsToSave);
-}
-const square = op({square_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function moments_(x, axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "moments");
-  const axes = parseAxisParam(axis, x.shape);
-  const xMean = mean(x, axes, keepDims);
-  let keepDimsShape = xMean.shape;
-  if (!keepDims) {
-    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
-  }
-  const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
-  const variance = mean(devSquared, axes, keepDims);
-  return {mean: xMean, variance};
-}
-const moments = op({moments_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
-function multiRNNCell_(lstmCells, data, c, h) {
-  const $data = convertToTensor(data, "data", "multiRNNCell");
-  const $c = convertToTensorArray(c, "c", "multiRNNCell");
-  const $h = convertToTensorArray(h, "h", "multiRNNCell");
-  let input = $data;
-  const newStates = [];
-  for (let i = 0; i < lstmCells.length; i++) {
-    const output = lstmCells[i](input, $c[i], $h[i]);
-    newStates.push(output[0]);
-    newStates.push(output[1]);
-    input = output[1];
-  }
-  const newC = [];
-  const newH = [];
-  for (let i = 0; i < newStates.length; i += 2) {
-    newC.push(newStates[i]);
-    newH.push(newStates[i + 1]);
-  }
-  return [newC, newH];
-}
-const multiRNNCell = op({multiRNNCell_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function multinomial_(logits, numSamples, seed, normalized = false) {
-  const $logits = convertToTensor(logits, "logits", "multinomial");
-  const numOutcomes = $logits.size;
-  const origRank = $logits.rank;
-  if (numOutcomes < 2) {
-    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
-  }
-  if (origRank > 2) {
-    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
-  }
-  seed = seed || Math.random();
-  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
-  const res = ENGINE.runKernelFunc((backend2) => backend2.multinomial(logits2D, normalized, numSamples, seed), {logits2D});
-  return origRank === 1 ? reshape(res, [res.size]) : res;
-}
-const multinomial = op({multinomial_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqual_(a, b) {
-  let $a = convertToTensor(a, "a", "notEqual");
-  let $b = convertToTensor(b, "b", "notEqual");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2) => backend2.notEqual($a, $b);
-  const inputs = {a: $a, b: $b};
-  return ENGINE.runKernelFunc(forward, inputs, null, NotEqual);
-}
-const notEqual = op({notEqual_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/real.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function real_(input) {
-  const $input = convertToTensor(input, "input", "real");
-  const forward = (backend2) => {
-    return backend2.real($input);
-  };
-  const inputs = {input: $input};
-  return ENGINE.runKernelFunc(forward, inputs, null, Real);
-}
-const real = op({real_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function onesLike_(x) {
-  const $x = convertToTensor(x, "x", "onesLike");
-  const forward = (backend2, save) => {
-    if ($x.dtype === "complex64") {
-      const r = onesLike(real($x));
-      const i = zerosLike(imag($x));
-      return complex(r, i);
-    }
-    return backend2.onesLike($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, OnesLike);
-}
-const onesLike = op({onesLike_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
-function outerProduct_(v1, v2) {
-  const $v1 = convertToTensor(v1, "v1", "outerProduct");
-  const $v2 = convertToTensor(v2, "v2", "outerProduct");
-  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
-  const v12D = reshape($v1, [-1, 1]);
-  const v22D = reshape($v2, [1, -1]);
-  return matMul(v12D, v22D);
-}
-const outerProduct = op({outerProduct_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pad_(x, paddings, constantValue = 0) {
-  const $x = convertToTensor(x, "x", "pad");
-  if ($x.rank === 0) {
-    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
-  }
-  const forward = (backend2, save) => {
-    save([$x]);
-    return backend2.pad($x, paddings, constantValue);
-  };
-  const attrs = {paddings, constantValue};
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, PadV2, attrs);
-}
-const pad = op({pad_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
-function pad1d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
-  return pad(x, [paddings], constantValue);
-}
-const pad1d = op({pad1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
-function pad2d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad2d = op({pad2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
-function pad3d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad3d = op({pad3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
-function pad4d_(x, paddings, constantValue = 0) {
-  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
-  return pad(x, paddings, constantValue);
-}
-const pad4d = op({pad4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function spaceToBatchND_(x, blockShape, paddings) {
-  const $x = convertToTensor(x, "x", "spaceToBatchND");
-  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
-  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-  assert($x.shape.reduce((a, b, i) => {
-    if (i > 0 && i <= blockShape.length) {
-      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
-    }
-    return a;
-  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
-  const forward = (backend2) => backend2.spaceToBatchND($x, blockShape, paddings);
-  const inputs = {x: $x};
-  const attrs = {blockShape, paddings};
-  return ENGINE.runKernelFunc(forward, inputs, null, SpaceToBatchND, attrs);
-}
-const spaceToBatchND = op({spaceToBatchND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pool_(input, windowShape, poolingType, pad8, dilations, strides) {
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  if (strides == null) {
-    strides = 1;
-  }
-  if (pad8 === 0) {
-    pad8 = "valid";
-  }
-  const $x = convertToTensor(input, "x", "maxPool");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad8);
-  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
-  let basePadding;
-  if (pad8 === "same") {
-    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
-  } else {
-    basePadding = [[0, 0], [0, 0]];
-  }
-  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
-  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
-  const convertedPad = isDilationOne ? pad8 : "valid";
-  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
-  const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
-  const y = forwardOp();
-  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
-  const padStart = basePadding.map((b) => b[0]);
-  const origPadEnd = basePadding.map((b) => b[1]);
-  const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
-  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
-  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
-  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
-  return [paddings, crops];
-}
-function withSpaceToBatchBasePaddings(filterShape, dilation) {
-  const dilatedFilterShape = filterShape.map((s, i) => {
-    return s + (s - 1) * (dilation[i] - 1);
-  });
-  const padExtraShape = dilatedFilterShape.map((s) => s - 1);
-  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
-  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
-  return padExtraShape.map((_, i) => {
-    return [padExtraStart[i], padExtraEnd[i]];
-  });
-}
-const pool = op({pool_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function pow_(base, exp11) {
-  let $base = convertToTensor(base, "base", "pow");
-  let $exp = convertToTensor(exp11, "exp", "pow");
-  [$base, $exp] = makeTypesMatch($base, $exp);
-  const inputs = {a: $base, b: $exp};
-  const forward = (backend2, save) => {
-    const y = backend2.pow($base, $exp);
-    save([$base, $exp, y]);
-    return y;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, Pow);
-}
-const pow = op({pow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prelu_(x, alpha) {
-  const $x = convertToTensor(x, "x", "prelu");
-  const $alpha = convertToTensor(alpha, "alpha", "prelu");
-  const forward = (backend2, save) => {
-    const res = backend2.prelu($x, $alpha);
-    save([$x, $alpha]);
-    return res;
-  };
-  const inputs = {x: $x, alpha: $alpha};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prelu);
-}
-const prelu = op({prelu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function prod_(x, axis = null, keepDims = false) {
-  let $x = convertToTensor(x, "x", "prod");
-  const forward = (backend2) => {
-    if ($x.dtype === "bool") {
-      $x = cast($x, "int32");
-    }
-    const axes = parseAxisParam(axis, $x.shape);
-    const permutation = getAxesPermutation(axes, $x.rank);
-    let reductionAxes = axes;
-    let permutedX = $x;
-    if (permutation != null) {
-      permutedX = transpose($x, permutation);
-      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
-    }
-    let value = backend2.prod(permutedX, reductionAxes);
-    if (keepDims) {
-      const newShape = expandShapeToKeepDim(value.shape, axes);
-      value = reshape(value, newShape);
-    }
-    return value;
-  };
-  const inputs = {x: $x};
-  const attrs = {axis, keepDims};
-  return ENGINE.runKernelFunc(forward, inputs, null, Prod, attrs);
-}
-const prod = op({prod_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rand_(shape, randFunction, dtype) {
-  const size = sizeFromShape(shape);
-  let values = null;
-  if (dtype == null || dtype === "float32") {
-    values = new Float32Array(size);
-  } else if (dtype === "int32") {
-    values = new Int32Array(size);
-  } else if (dtype === "bool") {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  for (let i = 0; i < size; i++) {
-    values[i] = randFunction();
-  }
-  return ENGINE.makeTensor(values, shape, dtype);
-}
-const rand = op({rand_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
-const seedrandom = __toModule(require_seedrandom2());
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MPRandGauss {
-  constructor(mean5, stdDeviation, dtype, truncated, seed) {
-    this.mean = mean5;
-    this.stdDev = stdDeviation;
-    this.dtype = dtype;
-    this.nextVal = NaN;
-    this.truncated = truncated;
-    if (this.truncated) {
-      this.upper = this.mean + this.stdDev * 2;
-      this.lower = this.mean - this.stdDev * 2;
-    }
-    const seedValue = seed ? seed : Math.random();
-    this.random = seedrandom.alea(seedValue.toString());
-  }
-  nextValue() {
-    if (!isNaN(this.nextVal)) {
-      const value = this.nextVal;
-      this.nextVal = NaN;
-      return value;
-    }
-    let resultX, resultY;
-    let isValid = false;
-    while (!isValid) {
-      let v1, v2, s;
-      do {
-        v1 = 2 * this.random() - 1;
-        v2 = 2 * this.random() - 1;
-        s = v1 * v1 + v2 * v2;
-      } while (s >= 1 || s === 0);
-      const mul62 = Math.sqrt(-2 * Math.log(s) / s);
-      resultX = this.mean + this.stdDev * v1 * mul62;
-      resultY = this.mean + this.stdDev * v2 * mul62;
-      if (!this.truncated || this.isValidTruncated(resultX)) {
-        isValid = true;
-      }
-    }
-    if (!this.truncated || this.isValidTruncated(resultY)) {
-      this.nextVal = this.convertValue(resultY);
-    }
-    return this.convertValue(resultX);
-  }
-  convertValue(value) {
-    if (this.dtype == null || this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-  isValidTruncated(value) {
-    return value <= this.upper && value >= this.lower;
-  }
-}
-class RandGamma {
-  constructor(alpha, beta, dtype, seed) {
-    this.alpha = alpha;
-    this.beta = 1 / beta;
-    this.dtype = dtype;
-    const seedValue = seed ? seed : Math.random();
-    this.randu = seedrandom.alea(seedValue.toString());
-    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
-    if (alpha < 1) {
-      this.d = alpha + 2 / 3;
-    } else {
-      this.d = alpha - 1 / 3;
-    }
-    this.c = 1 / Math.sqrt(9 * this.d);
-  }
-  nextValue() {
-    let x2, v0, v1, x, u, v;
-    while (true) {
-      do {
-        x = this.randn.nextValue();
-        v = 1 + this.c * x;
-      } while (v <= 0);
-      v *= v * v;
-      x2 = x * x;
-      v0 = 1 - 0.331 * x2 * x2;
-      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
-      u = this.randu();
-      if (u < v0 || Math.log(u) < v1) {
-        break;
-      }
-    }
-    v = 1 / this.beta * this.d * v;
-    if (this.alpha < 1) {
-      v *= Math.pow(this.randu(), 1 / this.alpha);
-    }
-    return this.convertValue(v);
-  }
-  convertValue(value) {
-    if (this.dtype === "float32") {
-      return value;
-    }
-    return Math.round(value);
-  }
-}
-class UniformRandom {
-  constructor(min4 = 0, max6 = 1, dtype, seed) {
-    this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
-    this.min = min4;
-    this.range = max6 - min4;
-    this.dtype = dtype;
-    if (seed == null) {
-      seed = Math.random();
-    }
-    if (typeof seed === "number") {
-      seed = seed.toString();
-    }
-    if (!this.canReturnFloat() && this.range <= 1) {
-      throw new Error(`The difference between ${min4} - ${max6} <= 1 and dtype is not float`);
-    }
-    this.random = seedrandom.alea(seed);
-  }
-  convertValue(value) {
-    if (this.canReturnFloat()) {
-      return value;
-    }
-    return Math.round(value);
-  }
-  nextValue() {
-    return this.convertValue(this.min + this.range * this.random());
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
-  if (beta == null) {
-    beta = 1;
-  }
-  if (dtype == null) {
-    dtype = "float32";
-  }
-  if (dtype !== "float32" && dtype !== "int32") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const rgamma = new RandGamma(alpha, beta, dtype, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = rgamma.nextValue();
-  }
-  return res.toTensor();
-}
-const randomGamma = op({randomGamma_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const randomNormal = op({randomNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
-  const res = buffer(shape, dtype);
-  const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = random.nextValue();
-  }
-  return res.toTensor();
-}
-const randomUniform = op({randomUniform_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor1d(values, dtype) {
-  assertNonNull(values);
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 1) {
-    throw new Error("tensor1d() requires values to be a flat/TypedArray");
-  }
-  const shape = null;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/range.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function range(start, stop, step7 = 1, dtype = "float32") {
-  if (step7 === 0) {
-    throw new Error("Cannot have a step of zero");
-  }
-  const forward = () => {
-    const sameStartStop = start === stop;
-    const increasingRangeNegativeStep = start < stop && step7 < 0;
-    const decreasingRangePositiveStep = stop < start && step7 > 1;
-    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
-      return zeros([0], dtype);
-    }
-    const numElements = Math.abs(Math.ceil((stop - start) / step7));
-    const values = makeZerosTypedArray(numElements, dtype);
-    if (stop < start && step7 === 1) {
-      step7 = -1;
-    }
-    values[0] = start;
-    for (let i = 1; i < values.length; i++) {
-      values[i] = values[i - 1] + step7;
-    }
-    return tensor1d(values, dtype);
-  };
-  const attrs = {start, stop, step: step7, dtype};
-  return ENGINE.runKernelFunc(forward, {}, null, Range, attrs);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reciprocal_(x) {
-  const $x = convertToTensor(x, "x", "reciprocal");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.reciprocal($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Reciprocal);
-}
-const reciprocal = op({reciprocal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu_(x) {
-  const $x = convertToTensor(x, "x", "relu");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu);
-}
-const relu = op({relu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function relu6_(x) {
-  const $x = convertToTensor(x, "x", "relu6");
-  const forward = (backend2, save) => {
-    save([$x]);
-    if ($x.dtype === "bool") {
-      return cast($x, "int32");
-    }
-    return backend2.relu6($x);
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Relu6);
-}
-const relu6 = op({relu6_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  const forward = (backend2) => {
-    const axes = parseAxisParam(axis, $x.shape);
-    if ($x.rank === 0) {
-      return clone($x);
-    }
-    const res = backend2.reverse($x, axes);
-    return reshape(res, $x.shape);
-  };
-  const inputs = {x: $x};
-  const attrs = {dims: axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, Reverse, attrs);
-}
-const reverse = op({reverse_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse1d_(x) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
-  return reverse($x, 0);
-}
-const reverse1d = op({reverse1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse2d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse2d = op({reverse2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse3d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse3d = op({reverse3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function reverse4d_(x, axis) {
-  const $x = convertToTensor(x, "x", "reverse");
-  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-const reverse4d = op({reverse4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/round.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function round_(x) {
-  const $x = convertToTensor(x, "x", "round");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.round($x), inputs, null, Round);
-}
-const round = op({round_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rsqrt_(x) {
-  const $x = convertToTensor(x, "x", "rsqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.rsqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Rsqrt);
-}
-const rsqrt = op({rsqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function selu_(x) {
-  const $x = convertToTensor(x, "x", "selu");
-  const forward = (backend2, save) => {
-    const res = backend2.selu($x);
-    save([$x]);
-    return res;
-  };
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc(forward, inputs, null, Selu);
-}
-const selu = op({selu_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
-function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad8, dilation = [1, 1], dataFormat = "NHWC") {
-  const $x = convertToTensor(x, "x", "separableConv2d");
-  const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
-  const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  if (dataFormat === "NCHW") {
-    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
-  }
-  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
-  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
-  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
-  const inChannels = $depthwiseFilter.shape[2];
-  const channelMultiplier = $depthwiseFilter.shape[3];
-  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
-  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad8, dataFormat, dilation);
-  const pointwiseStride = 1;
-  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const separableConv2d = op({separableConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function setdiff1dAsync_(x, y) {
-  const $x = convertToTensor(x, "x", "setdiff1d");
-  const $y = convertToTensor(y, "y", "setdiff1d");
-  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
-  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
-  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
-  const xVals = await $x.data();
-  const yVals = await $y.data();
-  const ySet = new Set(yVals);
-  let outputSize = 0;
-  for (let i = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      outputSize++;
-    }
-  }
-  const buffer11 = new TensorBuffer([outputSize], $x.dtype);
-  const indices = new TensorBuffer([outputSize], "int32");
-  for (let i = 0, p = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      buffer11.values[p] = xVals[i];
-      indices.values[p] = i;
-      p++;
-    }
-  }
-  return [buffer11.toTensor(), indices.toTensor()];
-}
-const setdiff1dAsync = setdiff1dAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sign_(x) {
-  const $x = convertToTensor(x, "x", "sign");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2) => backend2.sign($x), inputs, null, Sign);
-}
-const sign = op({sign_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sin_(x) {
-  const $x = convertToTensor(x, "x", "sin");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sin($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sin);
-}
-const sin = op({sin_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sinh_(x) {
-  const $x = convertToTensor(x, "x", "sinh");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sinh($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sinh);
-}
-const sinh = op({sinh_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice1d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice1d");
-  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, [begin], [size]);
-}
-const slice1d = op({slice1d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice2d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice2d");
-  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice2d = op({slice2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice3d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice3d");
-  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice3d = op({slice3d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function slice4d_(x, begin, size) {
-  const $x = convertToTensor(x, "x", "slice4d");
-  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-const slice4d = op({slice4d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmax_(logits, dim = -1) {
-  const $logits = convertToTensor(logits, "logits", "softmax", "float32");
-  if (dim === -1) {
-    dim = $logits.rank - 1;
-  }
-  if (dim !== $logits.rank - 1) {
-    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
-  }
-  const inputs = {logits: $logits};
-  const attrs = {dim};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const y = backend2.softmax($logits, dim);
-    save([y]);
-    return y;
-  }, inputs, null, Softmax, attrs);
-}
-const softmax = op({softmax_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = input.as2D(batch, innerDimensionSize);
-    const result = backend2.fft(input2D);
-    return result.reshape(input.shape);
-  }, inputs, null, FFT);
-}
-const fft = op({fft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function ifft_(input) {
-  assert(input.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input.dtype}.`);
-  const inputs = {input};
-  return ENGINE.runKernelFunc((backend2) => {
-    const innerDimensionSize = input.shape[input.shape.length - 1];
-    const batch = input.size / innerDimensionSize;
-    const input2D = reshape(input, [batch, innerDimensionSize]);
-    const result = backend2.ifft(input2D);
-    return reshape(result, input.shape);
-  }, inputs, null, IFFT);
-}
-const ifft = op({ifft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function irfft_(input) {
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let ret;
-  if (innerDimensionSize <= 2) {
-    const complexInput = reshape(input, [batch, innerDimensionSize]);
-    ret = ifft(complexInput);
-  } else {
-    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
-    const realInput = reshape(real(input), [batch, innerDimensionSize]);
-    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
-    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
-    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-    const r = concat([realInput, realConjugate], 1);
-    const i = concat([imagInput, imagConjugate], 1);
-    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
-    ret = ifft(complexInput);
-  }
-  ret = real(ret);
-  if (input.rank === 3 && input.shape[0] !== 0) {
-    const temp = ret;
-    const batch2 = input.shape[0];
-    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
-    temp.dispose();
-  }
-  return ret;
-}
-const irfft = op({irfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
-function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
-  let splitSizes = [];
-  if (typeof numOrSizeSplits === "number") {
-    assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
-    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
-  } else {
-    const numOfNegs = numOrSizeSplits.reduce((count, value) => {
-      if (value === -1) {
-        count += 1;
-      }
-      return count;
-    }, 0);
-    assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
-    const negIndex = numOrSizeSplits.indexOf(-1);
-    if (negIndex !== -1) {
-      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
-      numOrSizeSplits[negIndex] = x.shape[axis] - total;
-    }
-    assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
-    splitSizes = numOrSizeSplits;
-  }
-  return splitSizes;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/split.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split_(x, numOrSizeSplits, axis = 0) {
-  const $x = convertToTensor(x, "x", "split");
-  const forward = (backend2, _) => {
-    const $axis = parseAxisParam(axis, $x.shape)[0];
-    const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);
-    return backend2.split($x, splitSizes, $axis);
-  };
-  const inputs = {x: $x};
-  const attr = {numOrSizeSplits, axis};
-  return ENGINE.runKernelFunc(forward, inputs, null, SplitV, attr);
-}
-const split = op({split_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rfft_(input, fftLength) {
-  assert(input.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input.dtype}`);
-  let innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let adjustedInput;
-  if (fftLength != null && fftLength < innerDimensionSize) {
-    const begin = input.shape.map((v) => 0);
-    const size = input.shape.map((v) => v);
-    size[input.shape.length - 1] = fftLength;
-    adjustedInput = slice(input, begin, size);
-    innerDimensionSize = fftLength;
-  } else if (fftLength != null && fftLength > innerDimensionSize) {
-    const zerosShape = input.shape.map((v) => v);
-    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
-    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
-    innerDimensionSize = fftLength;
-  } else {
-    adjustedInput = input;
-  }
-  const zerosInput = zerosLike(adjustedInput);
-  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
-  const ret = fft(complexInput);
-  const half = Math.floor(innerDimensionSize / 2) + 1;
-  const realValues = real(ret);
-  const imagValues = imag(ret);
-  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
-  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
-  const outputShape = adjustedInput.shape.slice();
-  outputShape[adjustedInput.shape.length - 1] = half;
-  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
-}
-const rfft = op({rfft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sqrt_(x) {
-  const $x = convertToTensor(x, "x", "sqrt");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.sqrt($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Sqrt);
-}
-const sqrt = op({sqrt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squaredDifference_(a, b) {
-  let $a = convertToTensor(a, "a", "squaredDifference");
-  let $b = convertToTensor(b, "b", "squaredDifference");
-  [$a, $b] = makeTypesMatch($a, $b);
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-  const forward = (backend2, save) => {
-    const res = backend2.squaredDifference($a, $b);
-    save([$a, $b]);
-    return res;
-  };
-  const inputs = {a: $a, b: $b};
-  const attrs = {};
-  return ENGINE.runKernelFunc(forward, inputs, null, SquaredDifference, attrs);
-}
-const squaredDifference = op({squaredDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function squeeze_(x, axis) {
-  const $x = convertToTensor(x, "x", "squeeze");
-  return reshape($x, squeezeShape($x.shape, axis).newShape);
-}
-const squeeze = op({squeeze_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stack_(tensors, axis = 0) {
-  const $tensors = convertToTensorArray(tensors, "tensors", "stack");
-  assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
-  if ($tensors.length === 1) {
-    return expandDims($tensors[0], axis);
-  }
-  const rank = $tensors[0].rank;
-  const shape = $tensors[0].shape;
-  const dtype = $tensors[0].dtype;
-  assert(axis <= rank, () => "Axis must be <= rank of the tensor");
-  $tensors.forEach((t) => {
-    assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
-    assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
-  });
-  const expandedTensors = $tensors.map((t) => expandDims(t, axis));
-  return concat(expandedTensors, axis);
-}
-const stack = op({stack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/step.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function step_(x, alpha = 0) {
-  const $x = convertToTensor(x, "x", "step");
-  const inputs = {x: $x};
-  const attrs = {alpha};
-  return ENGINE.runKernelFunc((backend2) => backend2.step($x, alpha), inputs, null, Step, attrs);
-}
-const step = op({step_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
-  let $x = convertToTensor(x, "x", "stridedSlice");
-  const forward = (backend2) => {
-    if (strides == null) {
-      strides = new Array(begin.length);
-    }
-    const ellipsisAxes = maskToAxes(ellipsisMask);
-    if (ellipsisAxes.length > 1) {
-      throw new Error("Multiple ellipses in slice is not allowed.");
-    }
-    if (ellipsisMask !== 0 && newAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
-    }
-    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
-      throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
-    }
-    const numInterpolatedAxes = $x.rank - begin.length;
-    const expandAxes = maskToAxes(newAxisMask);
-    const newShape = $x.shape.slice();
-    expandAxes.forEach((axis) => {
-      begin[axis] = 0;
-      end[axis] = 1;
-      newShape.splice(axis, 0, 1);
-    });
-    $x = reshape($x, newShape);
-    const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
-    begin = normalizedBegin;
-    end = normalizedEnd;
-    strides = normalizedStrides;
-    const shrinkAxes = maskToAxes(shrinkAxisMask);
-    shrinkAxes.forEach((axis) => {
-      end[axis] = begin[axis] + 1;
-      strides[axis] = 1;
-    });
-    const size = computeOutShape(begin, end, strides);
-    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
-    const nonStrided = strides.every((v) => v === 1);
-    if (nonStrided) {
-      return reshape(slice($x, begin, size), outShape);
-    }
-    const res = backend2.stridedSlice($x, begin, end, strides);
-    return reshape(res, outShape);
-  };
-  const inputs = {x: $x};
-  const attrs = {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, StridedSlice, attrs);
-}
-const stridedSlice = op({stridedSlice_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tan_(x) {
-  const $x = convertToTensor(x, "x", "tan");
-  const inputs = {x: $x};
-  return ENGINE.runKernelFunc((backend2, save) => {
-    const res = backend2.tan($x);
-    save([$x]);
-    return res;
-  }, inputs, null, Tan);
-}
-const tan = op({tan_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor2d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 2) {
-    throw new Error("tensor2d() requires shape to have two numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
-    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor4d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 4) {
-    throw new Error("tensor4d() requires shape to have four numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
-    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor5d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 5) {
-    throw new Error("tensor5d() requires shape to have five numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
-    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-  }
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tensor6d(values, shape, dtype) {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 6) {
-    throw new Error("tensor6d() requires shape to have six numbers");
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
-    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-  }
-  shape = shape || inferredShape;
-  return makeTensor(values, shape, inferredShape, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topk_(x, k = 1, sorted = true) {
-  const $x = convertToTensor(x, "x", "topk");
-  if ($x.rank === 0) {
-    throw new Error("topk() expects the input to be of rank 1 or higher");
-  }
-  const lastDim = $x.shape[$x.shape.length - 1];
-  if (k > lastDim) {
-    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
-  }
-  const inputs = {x: $x};
-  const attrs = {k, sorted};
-  const [values, indices] = ENGINE.runKernelFunc((b) => b.topk($x, k, sorted), inputs, null, TopK, attrs);
-  return {values, indices};
-}
-const topk = op({topk_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
-  if (dtype != null && dtype === "bool") {
-    throw new Error(`Unsupported data type $ { dtype }`);
-  }
-  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-const truncatedNormal = op({truncatedNormal_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unsortedSegmentSum_(x, segmentIds, numSegments) {
-  const $x = convertToTensor(x, "x", "unsortedSegmentSum");
-  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
-  assert(isInt(numSegments), () => "numSegments must be of dtype int");
-  const inputs = {x: $x, segmentIds: $segmentIds};
-  const attrs = {numSegments};
-  const forward = (backend2, save) => {
-    const res = backend2.unsortedSegmentSum($x, $segmentIds, numSegments);
-    save([$segmentIds]);
-    return res;
-  };
-  return ENGINE.runKernelFunc(forward, inputs, null, UnsortedSegmentSum, attrs);
-}
-const unsortedSegmentSum = op({unsortedSegmentSum_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function unstack_(x, axis = 0) {
-  const $x = convertToTensor(x, "x", "unstack");
-  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
-  if (axis < 0) {
-    axis += $x.shape.length;
-  }
-  const inputs = {value: $x};
-  const attrs = {axis};
-  const forward = (backend2) => backend2.unstack($x, axis);
-  return ENGINE.runKernelFunc(forward, inputs, null, Unpack, attrs);
-}
-const unstack = op({unstack_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function variable(initialValue, trainable = true, name, dtype) {
-  return ENGINE.makeVariable(initialValue, trainable, name, dtype);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function whereImpl(condShape, condVals) {
-  const indices = [];
-  for (let i = 0; i < condVals.length; i++) {
-    if (condVals[i]) {
-      indices.push(i);
-    }
-  }
-  const inBuffer = buffer(condShape, "int32");
-  const out = buffer([indices.length, condShape.length], "int32");
-  for (let i = 0; i < indices.length; i++) {
-    const loc = inBuffer.indexToLoc(indices[i]);
-    const offset = i * condShape.length;
-    out.values.set(loc, offset);
-  }
-  return out.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function whereAsync_(condition) {
-  const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
-  const vals = await $condition.data();
-  const res = whereImpl($condition.shape, vals);
-  if (condition !== $condition) {
-    $condition.dispose();
-  }
-  return res;
-}
-const whereAsync = whereAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function booleanMaskAsync_(tensor17, mask, axis) {
-  const $tensor = convertToTensor(tensor17, "tensor", "boolMask");
-  const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
-  const axisFrom = axis == null ? 0 : axis;
-  const maskDim = $mask.rank;
-  const tensorShape = $tensor.shape;
-  assert(maskDim > 0, () => "mask cannot be scalar");
-  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
-  let leadingSize = 1;
-  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
-    leadingSize *= tensorShape[i];
-  }
-  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
-  const reshapedTensor = reshape($tensor, targetTensorShape);
-  const reshapedMask = reshape($mask, [-1]);
-  const positivePositions = await whereAsync(reshapedMask);
-  const indices = squeeze(positivePositions, [1]);
-  const res = gather(reshapedTensor, indices, axisFrom);
-  if (tensor17 !== $tensor) {
-    $tensor.dispose();
-  }
-  if (mask !== $mask) {
-    $mask.dispose();
-  }
-  indices.dispose();
-  reshapedTensor.dispose();
-  reshapedMask.dispose();
-  positivePositions.dispose();
-  return res;
-}
-const booleanMaskAsync = booleanMaskAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function notEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "notEqualStrict");
-  const $b = convertToTensor(b, "b", "notEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in notEqualStrict: ");
-  return notEqual($a, $b);
-}
-function lessStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessStrict");
-  const $b = convertToTensor(b, "b", "lessStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessStrict: ");
-  return less($a, $b);
-}
-function equalStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "equalStrict");
-  const $b = convertToTensor(b, "b", "equalStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in equalStrict: ");
-  return equal($a, $b);
-}
-function lessEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "lessEqualStrict");
-  const $b = convertToTensor(b, "b", "lessEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in lessEqualStrict: ");
-  return lessEqual($a, $b);
-}
-function greaterStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterStrict");
-  const $b = convertToTensor(b, "b", "greaterStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterStrict: ");
-  return greater($a, $b);
-}
-function greaterEqualStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "greaterEqualStrict");
-  const $b = convertToTensor(b, "b", "greaterEqualStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in greaterEqualStrict: ");
-  return greaterEqual($a, $b);
-}
-const equalStrict = op({equalStrict_});
-const greaterEqualStrict = op({greaterEqualStrict_});
-const greaterStrict = op({greaterStrict_});
-const lessEqualStrict = op({lessEqualStrict_});
-const lessStrict = op({lessStrict_});
-const notEqualStrict = op({notEqualStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function addStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "addStrict");
-  const $b = convertToTensor(b, "b", "addStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in addStrict: ");
-  return add2($a, $b);
-}
-function subStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "subStrict");
-  const $b = convertToTensor(b, "b", "subStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in subStrict: ");
-  return sub($a, $b);
-}
-function powStrict_(base, exp11) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  assertShapesMatch(base.shape, exp11.shape, "Error in powStrict: ");
-  return pow(base, exp11);
-}
-function mulStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "mul");
-  const $b = convertToTensor(b, "b", "mul");
-  assertShapesMatch($a.shape, $b.shape, "Error in multiplyStrict: ");
-  return mul($a, $b);
-}
-function divStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "div");
-  const $b = convertToTensor(b, "b", "div");
-  assertShapesMatch($a.shape, $b.shape, "Error in divideStrict: ");
-  return div($a, $b);
-}
-function modStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "modStrict");
-  const $b = convertToTensor(b, "b", "modStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in modStrict: ");
-  return mod($a, $b);
-}
-function minimumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "minimumStrict");
-  const $b = convertToTensor(b, "b", "minimumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in minimumStrict: ");
-  return minimum($a, $b);
-}
-function maximumStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "maximumStrict");
-  const $b = convertToTensor(b, "b", "maximumStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in maximumStrict: ");
-  return maximum($a, $b);
-}
-function squaredDifferenceStrict_(a, b) {
-  deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
-  const $a = convertToTensor(a, "a", "squaredDifferenceStrict");
-  const $b = convertToTensor(b, "b", "squaredDifferenceStrict");
-  assertShapesMatch($a.shape, $b.shape, "Error in squaredDifferenceStrict: ");
-  return squaredDifference($a, $b);
-}
-const addStrict = op({addStrict_});
-const divStrict = op({divStrict_});
-const maximumStrict = op({maximumStrict_});
-const minimumStrict = op({minimumStrict_});
-const modStrict = op({modStrict_});
-const mulStrict = op({mulStrict_});
-const powStrict = op({powStrict_});
-const squaredDifferenceStrict = op({squaredDifferenceStrict_});
-const subStrict = op({subStrict_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
-  x = convertToTensor(x, "x", "norm");
-  const norm4 = normImpl(x, ord, axis);
-  let keepDimsShape = norm4.shape;
-  if (keepDims) {
-    const axes = parseAxisParam(axis, x.shape);
-    keepDimsShape = expandShapeToKeepDim(norm4.shape, axes);
-  }
-  return reshape(norm4, keepDimsShape);
-}
-function normImpl(x, p, axis = null) {
-  if (x.rank === 0) {
-    return abs(x);
-  }
-  if (x.rank !== 1 && axis === null) {
-    return normImpl(reshape(x, [-1]), p, axis);
-  }
-  if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
-    if (p === 1) {
-      return sum2(abs(x), axis);
-    }
-    if (p === Infinity) {
-      return max(abs(x), axis);
-    }
-    if (p === -Infinity) {
-      return min(abs(x), axis);
-    }
-    if (p === "euclidean" || p === 2) {
-      return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  if (Array.isArray(axis) && axis.length === 2) {
-    if (p === 1) {
-      return max(sum2(abs(x), axis[0]), axis[1] - 1);
-    }
-    if (p === Infinity) {
-      return max(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === -Infinity) {
-      return min(sum2(abs(x), axis[1]), axis[0]);
-    }
-    if (p === "fro" || p === "euclidean") {
-      return sqrt(sum2(square(x), axis));
-    }
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-  throw new Error(`Error in norm: invalid axis: ${axis}`);
-}
-const norm = op({norm_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function movingAverage_(v, x, decay, step7, zeroDebias = true) {
-  const $v = convertToTensor(v, "v", "movingAverage");
-  const $x = convertToTensor(x, "x", "movingAverage");
-  const $decay = convertToTensor(decay, "decay", "movingAverage");
-  assertTypesMatch($v, $x);
-  assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
-  const one = scalar(1);
-  const oneMinusDecay = sub(one, $decay);
-  let update = mul(sub($x, $v), oneMinusDecay);
-  if (zeroDebias) {
-    assert(step7 != null, () => "When using zeroDebias: true, step is required.");
-    const $step = convertToTensor(step7, "step", "movingAverage");
-    update = div(update, sub(one, pow($decay, $step)));
-  }
-  return add2($v, update);
-}
-const movingAverage = op({movingAverage_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function scatterND_(indices, updates, shape) {
-  const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
-  const $updates = convertToTensor(updates, "updates", "scatterND");
-  validateInput($updates, $indices, shape);
-  const forward = (backend2) => {
-    return backend2.scatterND($indices, $updates, shape);
-  };
-  const inputs = {indices: $indices, updates: $updates};
-  const attrs = {shape};
-  return ENGINE.runKernelFunc(forward, inputs, null, ScatterNd, attrs);
-}
-const scatterND = op({scatterND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
-function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
-  if (sparseIndices.dtype !== "int32") {
-    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
-  }
-  if (sparseIndices.rank > 2) {
-    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
-  }
-  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
-  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
-  if (outputShape.length !== numDims) {
-    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
-  }
-  const numValues = sparseValues.size;
-  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
-    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
-  }
-  if (sparseValues.dtype !== defaultValues.dtype) {
-    throw new Error("sparseValues.dtype must match defaultValues.dtype");
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
-  const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
-  const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
-  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
-  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
-  const inputs = {
-    sparseIndices: $sparseIndices,
-    sparseValues: $sparseValues,
-    defaultValue: $defaultValue
-  };
-  const attrs = {outputShape};
-  return ENGINE.runKernelFunc((backend2) => backend2.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null, SparseToDense, attrs);
-}
-const sparseToDense = op({sparseToDense_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gatherND_(x, indices) {
-  const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
-  const $x = convertToTensor(x, "x", "gatherND");
-  const forward = (backend2) => {
-    return backend2.gatherND($x, $indices);
-  };
-  const inputs = {params: $x, indices: $indices};
-  return ENGINE.runKernelFunc(forward, inputs, null, GatherNd);
-}
-const gatherND = op({gatherND_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getNoiseShape(x, noiseShape) {
-  if (noiseShape == null) {
-    return x.shape.slice();
-  }
-  if (arraysEqual(x.shape, noiseShape)) {
-    return noiseShape;
-  }
-  if (x.shape.length === noiseShape.length) {
-    const newDimension = [];
-    for (let i = 0; i < x.shape.length; i++) {
-      if (noiseShape[i] == null && x.shape[i] != null) {
-        newDimension.push(x.shape[i]);
-      } else {
-        newDimension.push(noiseShape[i]);
-      }
-    }
-    return newDimension;
-  }
-  return noiseShape;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function dropout_(x, rate, noiseShape, seed) {
-  const $x = convertToTensor(x, "x", "dropout");
-  assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
-  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
-  if (rate === 0) {
-    return x instanceof Tensor ? $x.clone() : $x;
-  }
-  const $noiseShape = getNoiseShape($x, noiseShape);
-  const keepProb = 1 - rate;
-  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
-  return mul($x, multiplier);
-}
-const dropout = op({dropout_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function enclosingPowerOfTwo(value) {
-  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
-}
-function cosineWindow(windowLength, a, b) {
-  const even = 1 - windowLength % 2;
-  const newValues = new Float32Array(windowLength);
-  for (let i = 0; i < windowLength; ++i) {
-    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
-    newValues[i] = a - b * Math.cos(cosArg);
-  }
-  return tensor1d(newValues, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function inTopKAsync_(predictions, targets, k = 1) {
-  const $predictions = convertToTensor(predictions, "predictions", "inTopK");
-  const $targets = convertToTensor(targets, "targets", "inTopK");
-  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
-  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
-  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
-  const lastDim = $predictions.shape[$predictions.shape.length - 1];
-  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
-  const predictionsVals = await $predictions.data();
-  const targetsVals = await $targets.data();
-  const [batch, size] = [predictionsVals.length / lastDim, lastDim];
-  const precision = getTypedArrayFromDType("bool", batch);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = predictionsVals.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    precision[b] = 0;
-    for (let i = 0; i < k; i++) {
-      if (valAndInd[i].index === targetsVals[b]) {
-        precision[b] = 1;
-        break;
-      }
-    }
-  }
-  if (predictions !== $predictions) {
-    $predictions.dispose();
-  }
-  if (targets !== $targets) {
-    $targets.dispose();
-  }
-  return tensor4(precision, $targets.shape, "bool");
-}
-const inTopKAsync = inTopKAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv2DBackpropFilter_(x, dy, filterShape, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
-  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
-  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
-  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
-  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
-  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
-  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const dilations = 1;
-    const $dataFormat = convertConv2DDataFormat(dataFormat);
-    const convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
-    return backend2.conv2dDerFilter(x4D, dy4D, convInfo);
-  };
-  const inputs = {x: x4D, dy: dy4D};
-  const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);
-}
-const conv2DBackpropFilter = op({conv2DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getFusedDyActivation(dy, y, activation) {
-  if (activation == null || activation === "linear") {
-    return dy;
-  }
-  if (activation === "relu") {
-    return mul(dy, step(y));
-  }
-  throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
-}
-function getFusedBiasGradient(bias, dyActivation) {
-  let res = dyActivation;
-  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
-  if (reduceAxes.length > 0) {
-    res = sum2(res, reduceAxes);
-  }
-  return reshape(res, bias.shape);
-}
-function applyActivation(x, activation, preluActivationWeights) {
-  if (activation === "linear") {
-    return x;
-  } else if (activation === "relu") {
-    return relu(x);
-  } else if (activation === "elu") {
-    return elu(x);
-  } else if (activation === "relu6") {
-    return relu6(x);
-  } else if (activation === "prelu") {
-    return prelu(x, preluActivationWeights);
-  }
-  throw new Error(`Unknown fused activation ${activation}.`);
-}
-const shouldFuse = (gradientDepth, activation) => {
-  const gradientMode = gradientDepth > 0;
-  return !gradientMode || activation === "linear";
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  activation = activation || "linear";
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = conv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "conv2d");
-  const $filter = convertToTensor(filter, "filter", "conv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
-  }
-  const grad2 = (dy, saved) => {
-    const [$filter2, x4D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8);
-    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8);
-    const der = [xDer, filterDer];
-    if ($bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      der.push(biasDer);
-    }
-    return der;
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedConv2d({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const conv2d5 = op({fusedConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {
-  let x4D = x;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);
-  const inputs = {x: x4D, dy: dy4D};
-  return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);
-}
-const depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {
-  let dy4D = dy;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  const forward = (backend2) => backend2.depthwiseConv2DDerInput(dy4D, filter, convInfo);
-  const inputs = {dy: dy4D};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedDepthwiseConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = depthwiseConv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  const $x = convertToTensor(x, "x", "depthwiseConv2d");
-  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
-  let x4D = $x;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
-  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused conv2d");
-    [$bias] = makeTypesMatch($bias, $x);
-    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
-  }
-  const grad2 = (dy, saved) => {
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
-    const [$filter2, x4D2, y, bias2] = saved;
-    const dyActivation = getFusedDyActivation(dy, y, activation);
-    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, convInfo);
-    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, convInfo);
-    if (bias2 != null) {
-      const biasDer = getFusedBiasGradient($bias, dyActivation);
-      return [xDer, filterDer, biasDer];
-    }
-    return [xDer, filterDer];
-  };
-  const forward = (backend2) => {
-    const res = backend2.fusedDepthwiseConv2D({
-      input: x4D,
-      filter: $filter,
-      convInfo,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return res;
-  };
-  const inputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation};
-  if (bias == null) {
-    const customOp = customGrad((x4D2, filter2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOp(x4D, $filter);
-  } else {
-    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
-      let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
-      save([filter2, x4D2, res, bias2]);
-      if (reshapedTo4D) {
-        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-      }
-      return {value: res, gradFunc: grad2};
-    });
-    return customOpWithBias(x4D, $filter, $bias);
-  }
-}
-const depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights}) {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = matMul(a, b, transposeA, transposeB);
-    if (bias != null) {
-      result = add2(result, bias);
-    }
-    return applyActivation(result, activation, preluActivationWeights);
-  }
-  let $a = convertToTensor(a, "a", "fused matMul");
-  let $b = convertToTensor(b, "b", "fused matMul");
-  [$a, $b] = makeTypesMatch($a, $b);
-  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-  const outerDimsA = $a.shape.slice(0, -2);
-  const outerDimsB = $b.shape.slice(0, -2);
-  const batchDimA = sizeFromShape(outerDimsA);
-  const batchDimB = sizeFromShape(outerDimsB);
-  assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
-  assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
-  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
-  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
-  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-  let $bias;
-  if (bias != null) {
-    $bias = convertToTensor(bias, "bias", "fused matMul");
-    [$bias] = makeTypesMatch($bias, $a);
-    assertAndGetBroadcastShape(outShape, $bias.shape);
-  }
-  let $preluActivationWeights;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
-  }
-  const grad2 = (dy, saved) => {
-    const [a3D2, b3D2, y, $bias2] = saved;
-    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
-    let aDer;
-    let bDer;
-    if (!transposeA && !transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, true);
-      bDer = matMul(a3D2, dyActivation, true, false);
-    } else if (!transposeA && transposeB) {
-      aDer = matMul(dyActivation, b3D2, false, false);
-      bDer = matMul(dyActivation, a3D2, true, false);
-    } else if (transposeA && !transposeB) {
-      aDer = matMul(b3D2, dyActivation, false, true);
-      bDer = matMul(a3D2, dyActivation, false, false);
-    } else {
-      aDer = matMul(b3D2, dyActivation, true, true);
-      bDer = matMul(dyActivation, a3D2, true, true);
-    }
-    if (bias != null) {
-      const biasDer = getFusedBiasGradient($bias2, dyActivation);
-      return [aDer, bDer, biasDer];
-    } else {
-      return [aDer, bDer];
-    }
-  };
-  const forward = (backend2) => {
-    const y = backend2.fusedBatchMatMul({
-      a: a3D,
-      b: b3D,
-      transposeA,
-      transposeB,
-      bias: $bias,
-      activation,
-      preluActivationWeights: $preluActivationWeights
-    });
-    return y;
-  };
-  const inputs = {
-    a: a3D,
-    b: b3D,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs = {transposeA, transposeB, activation};
-  if (bias == null) {
-    const customOp = customGrad((a3D2, b3D2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOp(a3D, b3D);
-  } else {
-    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
-      const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
-      save([a3D2, b3D2, res, $bias2]);
-      return {value: reshape(res, outShape), gradFunc: grad2};
-    });
-    return customOpWithBias(a3D, b3D, $bias);
-  }
-}
-const matMul2 = op({fusedMatMul_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
-const fused_ops_exports = {};
-__export(fused_ops_exports, {
-  conv2d: () => conv2d5,
-  depthwiseConv2d: () => depthwiseConv2d2,
-  matMul: () => matMul2
-});
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hammingWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.54, 0.46);
-}
-const hammingWindow = op({hammingWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function hannWindow_(windowLength) {
-  return cosineWindow(windowLength, 0.5, 0.5);
-}
-const hannWindow = op({hannWindow_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
-  let start = 0;
-  const output = [];
-  while (start + frameLength <= signal2.size) {
-    output.push(slice(signal2, start, frameLength));
-    start += frameStep;
-  }
-  if (padEnd) {
-    while (start < signal2.size) {
-      const padLen = start + frameLength - signal2.size;
-      const pad8 = concat([
-        slice(signal2, start, frameLength - padLen),
-        fill([padLen], padValue)
-      ]);
-      output.push(pad8);
-      start += frameStep;
-    }
-  }
-  if (output.length === 0) {
-    return tensor2d([], [0, frameLength]);
-  }
-  return reshape(concat(output), [output.length, frameLength]);
-}
-const frame = op({frame_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
-  if (fftLength == null) {
-    fftLength = enclosingPowerOfTwo(frameLength);
-  }
-  const framedSignal = frame(signal2, frameLength, frameStep);
-  const windowedSignal = mul(framedSignal, windowFn(frameLength));
-  const output = [];
-  for (let i = 0; i < framedSignal.shape[0]; i++) {
-    output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
-  }
-  return concat(output);
-}
-const stft = op({stft_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function cropAndResize_(image2, boxes, boxInd, cropSize, method, extrapolationValue) {
-  const $image = convertToTensor(image2, "image", "cropAndResize");
-  const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
-  const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
-  method = method || "bilinear";
-  extrapolationValue = extrapolationValue || 0;
-  const numBoxes = $boxes.shape[0];
-  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
-  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
-  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
-  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
-  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-  assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
-  const forward = (backend2) => backend2.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
-  const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
-  const attrs = {method, extrapolationValue, cropSize};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, CropAndResize, attrs);
-  return res;
-}
-const cropAndResize = op({cropAndResize_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function flipLeftRight_(image2) {
-  const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
-  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
-  return res;
-}
-const flipLeftRight = op({flipLeftRight_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
-  const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
-  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
-  const inputs = {image: $image};
-  const attrs = {radians, fillValue, center};
-  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
-  return res;
-}
-const rotateWithOffset = op({rotateWithOffset_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  if (iouThreshold == null) {
-    iouThreshold = 0.5;
-  }
-  if (scoreThreshold == null) {
-    scoreThreshold = Number.NEGATIVE_INFINITY;
-  }
-  if (softNmsSigma == null) {
-    softNmsSigma = 0;
-  }
-  const numBoxes = boxes.shape[0];
-  maxOutputSize = Math.min(maxOutputSize, numBoxes);
-  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
-  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
-  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
-  assert(scores.rank === 1, () => "scores must be a 1D tensor");
-  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
-  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
-  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
-  return ENGINE.runKernelFunc((b) => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), {boxes: $boxes, scores: $scores}, null, NonMaxSuppressionV3, attrs);
-}
-const nonMaxSuppression = op({nonMaxSuppression_});
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/array_util.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function binaryInsert(arr, element, comparator) {
-  const index = binarySearch(arr, element, comparator);
-  const insertionPoint = index < 0 ? -(index + 1) : index;
-  arr.splice(insertionPoint, 0, element);
-}
-function binarySearch(arr, target, comparator) {
-  return binarySearch_(arr, target, comparator || defaultComparator);
-}
-function defaultComparator(a, b) {
-  return a > b ? 1 : a < b ? -1 : 0;
-}
-function binarySearch_(arr, target, comparator) {
-  let left = 0;
-  let right = arr.length;
-  let middle = 0;
-  let found = false;
-  while (left < right) {
-    middle = left + (right - left >>> 1);
-    const compareResult = comparator(target, arr[middle]);
-    if (compareResult > 0) {
-      left = middle + 1;
-    } else {
-      right = middle;
-      found = !compareResult;
-    }
-  }
-  return found ? left : -left - 1;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0).selectedIndices;
-}
-function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
-}
-function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
-  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
-}
-function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
-  const candidates = [];
-  for (let i = 0; i < scores.length; i++) {
-    if (scores[i] > scoreThreshold) {
-      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
-    }
-  }
-  candidates.sort(ascendingComparator);
-  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
-  const selectedIndices = [];
-  const selectedScores = [];
-  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
-    const candidate = candidates.pop();
-    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
-    if (originalScore < scoreThreshold) {
-      break;
-    }
-    let ignoreCandidate = false;
-    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
-      const iou3 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
-      if (iou3 >= iouThreshold) {
-        ignoreCandidate = true;
-        break;
-      }
-      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou3);
-      if (candidate.score <= scoreThreshold) {
-        break;
-      }
-    }
-    candidate.suppressBeginIndex = selectedIndices.length;
-    if (!ignoreCandidate) {
-      if (candidate.score === originalScore) {
-        selectedIndices.push(boxIndex);
-        selectedScores.push(candidate.score);
-      } else if (candidate.score > scoreThreshold) {
-        binaryInsert(candidates, candidate, ascendingComparator);
-      }
-    }
-  }
-  const validOutputs = selectedIndices.length;
-  const elemsToPad = maxOutputSize - validOutputs;
-  if (padToMaxOutputSize && elemsToPad > 0) {
-    selectedIndices.push(...new Array(elemsToPad).fill(0));
-    selectedScores.push(...new Array(elemsToPad).fill(0));
-  }
-  const result = {selectedIndices: tensor1d(selectedIndices, "int32")};
-  if (returnScoresTensor) {
-    result["selectedScores"] = tensor1d(selectedScores, "float32");
-  }
-  if (returnValidOutputs) {
-    result["validOutputs"] = scalar(validOutputs, "int32");
-  }
-  return result;
-}
-function intersectionOverUnion(boxes, i, j) {
-  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
-  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
-  const yminI = Math.min(iCoord[0], iCoord[2]);
-  const xminI = Math.min(iCoord[1], iCoord[3]);
-  const ymaxI = Math.max(iCoord[0], iCoord[2]);
-  const xmaxI = Math.max(iCoord[1], iCoord[3]);
-  const yminJ = Math.min(jCoord[0], jCoord[2]);
-  const xminJ = Math.min(jCoord[1], jCoord[3]);
-  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
-  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
-  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
-  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-  if (areaI <= 0 || areaJ <= 0) {
-    return 0;
-  }
-  const intersectionYmin = Math.max(yminI, yminJ);
-  const intersectionXmin = Math.max(xminI, xminJ);
-  const intersectionYmax = Math.min(ymaxI, ymaxJ);
-  const intersectionXmax = Math.min(xmaxI, xmaxJ);
-  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
-  return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-function suppressWeight(iouThreshold, scale2, iou3) {
-  const weight = Math.exp(scale2 * iou3 * iou3);
-  return iou3 <= iouThreshold ? weight : 0;
-}
-function ascendingComparator(c1, c2) {
-  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
-  return {selectedIndices: result[0], selectedScores: result[1]};
-}
-const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-  const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const inputs = {boxes: $boxes, scores: $scores};
-  const attrs = {
-    maxOutputSize: $maxOutputSize,
-    iouThreshold: $iouThreshold,
-    scoreThreshold: $scoreThreshold,
-    padToMaxOutputSize
-  };
-  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
-  return {selectedIndices: result[0], validOutputs: result[1]};
-}
-const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
-  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
-  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
-  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
-  const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-  return res;
-}
-const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeBilinear_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeBilinear");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeBilinear, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeBilinear = op({resizeBilinear_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function resizeNearestNeighbor_(images, size, alignCorners = false) {
-  const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
-  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
-  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
-  assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
-  let batchImages = $images;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [newHeight, newWidth] = size;
-  const inputs = {images: batchImages};
-  const attrs = {alignCorners, size};
-  const forward = (backend2, save) => {
-    save([batchImages]);
-    return backend2.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
-  };
-  const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeNearestNeighbor, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const resizeNearestNeighbor = op({resizeNearestNeighbor_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function bandPart_(a, numLower, numUpper) {
-  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
-  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
-  const $a = convertToTensor(a, "a", "bandPart");
-  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
-  const shape = $a.shape;
-  const [M, N] = $a.shape.slice(-2);
-  if (!(numLower <= M)) {
-    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
-  }
-  if (!(numUpper <= N)) {
-    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
-  }
-  if (numLower < 0) {
-    numLower = M;
-  }
-  if (numUpper < 0) {
-    numUpper = N;
-  }
-  const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
-  const j = range(0, N, 1, "int32");
-  const ij = sub(i, j);
-  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
-  const zero = zeros([M, N], $a.dtype);
-  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
-}
-const bandPart = op({bandPart_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gramSchmidt_(xs) {
-  let inputIsTensor2D;
-  if (Array.isArray(xs)) {
-    inputIsTensor2D = false;
-    assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
-    const dim = xs[0].shape[0];
-    for (let i = 1; i < xs.length; ++i) {
-      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
-    }
-  } else {
-    inputIsTensor2D = true;
-    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
-  }
-  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
-  const ys = [];
-  const xs1d = xs;
-  for (let i = 0; i < xs.length; ++i) {
-    ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i];
-      if (i > 0) {
-        for (let j = 0; j < i; ++j) {
-          const proj = mul(sum2(mul(ys[j], x)), ys[j]);
-          x = sub(x, proj);
-        }
-      }
-      return div(x, norm(x, "euclidean"));
-    }));
-  }
-  if (inputIsTensor2D) {
-    return stack(ys, 0);
-  } else {
-    return ys;
-  }
-}
-const gramSchmidt = op({gramSchmidt_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function qr_(x, fullMatrices = false) {
-  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
-  if (x.rank === 2) {
-    return qr2d(x, fullMatrices);
-  } else {
-    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
-    const x2ds = unstack(reshape(x, [
-      outerDimsProd,
-      x.shape[x.shape.length - 2],
-      x.shape[x.shape.length - 1]
-    ]), 0);
-    const q2ds = [];
-    const r2ds = [];
-    x2ds.forEach((x2d) => {
-      const [q2d, r2d] = qr2d(x2d, fullMatrices);
-      q2ds.push(q2d);
-      r2ds.push(r2d);
-    });
-    const q = reshape(stack(q2ds, 0), x.shape);
-    const r = reshape(stack(r2ds, 0), x.shape);
-    return [q, r];
-  }
-}
-function qr2d(x, fullMatrices = false) {
-  return ENGINE.tidy(() => {
-    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
-    const m = x.shape[0];
-    const n = x.shape[1];
-    let q = eye(m);
-    let r = clone(x);
-    const one2D = tensor2d([[1]], [1, 1]);
-    let w = clone(one2D);
-    const iters = m >= n ? n : m;
-    for (let j = 0; j < iters; ++j) {
-      const rTemp = r;
-      const wTemp = w;
-      const qTemp = q;
-      [w, r, q] = ENGINE.tidy(() => {
-        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
-        const normX = norm(rjEnd1);
-        const rjj = slice(r, [j, j], [1, 1]);
-        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-        const u1 = sub(rjj, mul(s, normX));
-        const wPre = div(rjEnd1, u1);
-        if (wPre.shape[0] === 1) {
-          w = clone(one2D);
-        } else {
-          w = concat([
-            one2D,
-            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
-          ], 0);
-        }
-        const tau = neg(div(matMul(s, u1), normX));
-        const rjEndAll = slice(r, [j, 0], [m - j, n]);
-        const tauTimesW = mul(tau, w);
-        const wT = transpose(w);
-        if (j === 0) {
-          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-        } else {
-          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
-        }
-        const tawTimesWT = transpose(tauTimesW);
-        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
-        if (j === 0) {
-          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-        } else {
-          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
-        }
-        return [w, r, q];
-      });
-      dispose([rTemp, wTemp, qTemp]);
-    }
-    if (!fullMatrices && m > n) {
-      q = slice(q, [0, 0], [m, n]);
-      r = slice(r, [0, 0], [n, n]);
-    }
-    return [q, r];
-  });
-}
-const qr = op({qr_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-var Reduction;
-(function(Reduction2) {
-  Reduction2[Reduction2["NONE"] = 0] = "NONE";
-  Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
-  Reduction2[Reduction2["SUM"] = 2] = "SUM";
-  Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
-})(Reduction || (Reduction = {}));
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
-function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
-  }
-  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
-  if (reduction === Reduction.NONE) {
-    return weightedLoss;
-  }
-  if (reduction === Reduction.SUM) {
-    return sum2(weightedLoss);
-  }
-  if (reduction === Reduction.MEAN) {
-    if ($weights == null) {
-      return mean(weightedLoss);
-    } else {
-      const broadcastFactor = $losses.size / $weights.size;
-      const result = div(sum2(weightedLoss), sum2($weights));
-      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
-    }
-  }
-  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    if ($weights == null) {
-      return div(sum2(weightedLoss), scalar($losses.size));
-    } else {
-      const broadcastedWeights = mul($weights, ones2($losses.shape));
-      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
-      return div(sum2(weightedLoss), numNonZeros);
-    }
-  }
-  throw Error(`Unknown reduction: ${reduction}`);
-}
-const computeWeightedLoss = op({computeWeightedLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "absoluteDifference");
-  const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "absoluteDifference");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
-  const losses2 = abs(sub($labels, $predictions));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const absoluteDifference = op({absoluteDifference_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
-function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "cosineDistance");
-  const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "cosineDistance");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
-  const one = scalar(1);
-  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const cosineDistance = op({cosineDistance_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
-function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $labels = convertToTensor(labels, "labels", "hingeLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "hingeLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
-  const one = scalar(1);
-  $labels = sub(mul(scalar(2), $labels), one);
-  const losses2 = relu(sub(one, mul($labels, $predictions)));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const hingeLoss = op({hingeLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "huberLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "huberLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
-  const deltaScalar = scalar(delta);
-  const error = abs(sub($predictions, $labels));
-  const quadratic = minimum(error, deltaScalar);
-  const linear = sub(error, quadratic);
-  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const huberLoss = op({huberLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function logLoss_(labels, predictions, weights, epsilon2 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "logLoss");
-  const $predictions = convertToTensor(predictions, "predictions", "logLoss");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "logLoss");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
-  const one = scalar(1);
-  const epsilonScalar = scalar(epsilon2);
-  const l1 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
-  const l2 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
-  const losses2 = sub(l1, l2);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const logLoss = op({logLoss_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  const $labels = convertToTensor(labels, "labels", "meanSquaredError");
-  const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "meanSquaredError");
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
-  const losses2 = squaredDifference($labels, $predictions);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const meanSquaredError = op({meanSquaredError_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function sigmoidCrossEntropyWithLogits_(labels, logits) {
-  const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
-  assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  const maxOutput = relu($logits);
-  const outputXTarget = mul($logits, $labels);
-  const sigmoidOutput = log1p(exp(neg(abs($logits))));
-  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
-}
-function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
-  }
-  assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const half = scalar(0.5);
-    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
-  }
-  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
-  if (dim === -1) {
-    dim = logits.rank - 1;
-  }
-  if (dim !== logits.rank - 1) {
-    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
-  }
-  const customOp = customGrad((labels2, logits2, save) => {
-    const keepDims = true;
-    const lse = logSumExp(logits2, [dim], keepDims);
-    const logResult = sub(cast(logits2, "float32"), lse);
-    save([labels2, logResult]);
-    const costVector = neg(mul(logResult, labels2));
-    const value = sum2(costVector, [dim]);
-    const gradFunc = (dy, saved) => {
-      const [labels3, logResult2] = saved;
-      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
-      return [
-        mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
-        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
-      ];
-    };
-    return {value, gradFunc};
-  });
-  return customOp(labels, logits);
-}
-function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
-  let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
-  const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
-  let $weights = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
-  }
-  assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const numClasses = scalar($onehotLabels.shape[1]);
-    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
-  }
-  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
-  return computeWeightedLoss(losses2, $weights, reduction);
-}
-const softmaxCrossEntropy = op({softmaxCrossEntropy_});
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spectral = {
-  fft,
-  ifft,
-  rfft,
-  irfft
-};
-const signal = {
-  hammingWindow,
-  hannWindow,
-  frame,
-  stft
-};
-const image = {
-  flipLeftRight,
-  resizeNearestNeighbor,
-  resizeBilinear,
-  rotateWithOffset,
-  cropAndResize,
-  nonMaxSuppression,
-  nonMaxSuppressionAsync,
-  nonMaxSuppressionWithScore,
-  nonMaxSuppressionWithScoreAsync,
-  nonMaxSuppressionPadded,
-  nonMaxSuppressionPaddedAsync
-};
-const linalg = {
-  bandPart,
-  gramSchmidt,
-  qr
-};
-const losses = {
-  absoluteDifference,
-  computeWeightedLoss,
-  cosineDistance,
-  hingeLoss,
-  huberLoss,
-  logLoss,
-  meanSquaredError,
-  sigmoidCrossEntropy,
-  softmaxCrossEntropy
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class Optimizer extends Serializable {
-  minimize(f, returnCost = false, varList) {
-    const {value, grads: grads2} = this.computeGradients(f, varList);
-    if (varList != null) {
-      const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
-      this.applyGradients(gradArray);
-    } else {
-      this.applyGradients(grads2);
-    }
-    dispose(grads2);
-    if (returnCost) {
-      return value;
-    } else {
-      value.dispose();
-      return null;
-    }
-  }
-  get iterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return this.iterations_;
-  }
-  incrementIterations() {
-    this.iterations_ = this.iterations + 1;
-  }
-  computeGradients(f, varList) {
-    return variableGrads(f, varList);
-  }
-  dispose() {
-    if (this.iterations_ != null) {
-      dispose(this.iterations_);
-    }
-  }
-  async saveIterations() {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return {
-      name: "iter",
-      tensor: scalar(this.iterations_, "int32")
-    };
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for this optimizer yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
-  }
-  async extractIterations(weightValues) {
-    this.iterations_ = (await weightValues[0].tensor.data())[0];
-    return weightValues.slice(1);
-  }
-}
-Object.defineProperty(Optimizer, Symbol.hasInstance, {
-  value: (instance) => {
-    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
-  }
-});
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdadeltaOptimizer extends Optimizer {
-  constructor(learningRate, rho, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.rho = rho;
-    this.epsilon = epsilon2;
-    this.accumulatedGrads = [];
-    this.accumulatedUpdates = [];
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedGrads[i] == null) {
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accum_grad`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedUpdates[i] == null) {
-        this.accumulatedUpdates[i] = {
-          originalName: `${name}/accum_var`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
-        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
-        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        accumulatedUpdate.assign(newAccumulatedUpdate);
-        const newValue = add2(mul(updates, -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedUpdates != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-      dispose(this.accumulatedUpdates.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      rho: this.rho,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["rho"], config2["epsilon"]);
-  }
-}
-AdadeltaOptimizer.className = "Adadelta";
-registerClass(AdadeltaOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdagradOptimizer extends Optimizer {
-  constructor(learningRate, initialAccumulatorValue = 0.1) {
-    super();
-    this.learningRate = learningRate;
-    this.initialAccumulatorValue = initialAccumulatorValue;
-    this.accumulatedGrads = [];
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i] == null) {
-        const trainable = false;
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accumulator`,
-          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      tidy(() => {
-        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
-        accumulatedGrad.assign(newAccumulatedGrad);
-        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedGrads != null) {
-      dispose(this.accumulatedGrads.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      initialAccumulatorValue: this.initialAccumulatorValue
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["initialAccumulatorValue"]);
-  }
-}
-AdagradOptimizer.className = "Adagrad";
-registerClass(AdagradOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedSecondMoment = [];
-    tidy(() => {
-      this.accBeta1 = scalar(beta1).variable();
-      this.accBeta2 = scalar(beta2).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const oneMinusAccBeta2 = sub(1, this.accBeta2);
-      varNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedSecondMoment[i] == null) {
-          this.accumulatedSecondMoment[i] = {
-            originalName: `${name}/v`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const secondMoment = this.accumulatedSecondMoment[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
-        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
-        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
-        firstMoment.assign(newFirstMoment);
-        secondMoment.assign(newSecondMoment);
-        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
-        value.assign(newValue);
-      });
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.accBeta2.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedSecondMoment != null) {
-      dispose(this.accumulatedSecondMoment.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    tidy(() => {
-      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
-      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
-    });
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"]);
-  }
-}
-AdamOptimizer.className = "Adam";
-registerClass(AdamOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class AdamaxOptimizer extends Optimizer {
-  constructor(learningRate, beta1, beta2, epsilon2 = null, decay = 0) {
-    super();
-    this.learningRate = learningRate;
-    this.beta1 = beta1;
-    this.beta2 = beta2;
-    this.epsilon = epsilon2;
-    this.decay = decay;
-    this.accumulatedFirstMoment = [];
-    this.accumulatedWeightedInfNorm = [];
-    tidy(() => {
-      this.iteration = scalar(0).variable();
-      this.accBeta1 = scalar(beta1).variable();
-    });
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        if (this.accumulatedWeightedInfNorm[i] == null) {
-          this.accumulatedWeightedInfNorm[i] = {
-            originalName: `${name}/v`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
-        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const ut0 = mul(weightedInfNorm, this.beta2);
-        const ut1 = abs(gradient);
-        const newWeightedInfNorm = maximum(ut0, ut1);
-        firstMoment.assign(newFirstMoment);
-        weightedInfNorm.assign(newWeightedInfNorm);
-        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
-        value.assign(newValue);
-      });
-      this.iteration.assign(add2(this.iteration, 1));
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.accBeta1.dispose();
-    this.iteration.dispose();
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
-    }
-    if (this.accumulatedWeightedInfNorm != null) {
-      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    throw new Error("getWeights() is not implemented for Adamax yet.");
-  }
-  async setWeights(weightValues) {
-    throw new Error("setWeights() is not implemented for Adamax yet.");
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      beta1: this.beta1,
-      beta2: this.beta2,
-      epsilon: this.epsilon,
-      decay: this.decay
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"], config2["decay"]);
-  }
-}
-AdamaxOptimizer.className = "Adamax";
-registerClass(AdamaxOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class SGDOptimizer extends Optimizer {
-  constructor(learningRate) {
-    super();
-    this.learningRate = learningRate;
-    this.setLearningRate(learningRate);
-  }
-  applyGradients(variableGradients) {
-    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    varNames.forEach((name, i) => {
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const value = ENGINE.registeredVariables[name];
-      tidy(() => {
-        const newValue = add2(mul(this.c, gradient), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  setLearningRate(learningRate) {
-    this.learningRate = learningRate;
-    if (this.c != null) {
-      this.c.dispose();
-    }
-    this.c = keep(scalar(-learningRate));
-  }
-  dispose() {
-    this.c.dispose();
-  }
-  async getWeights() {
-    return [await this.saveIterations()];
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    if (weightValues.length !== 0) {
-      throw new Error("SGD optimizer does not have settable weights.");
-    }
-  }
-  getConfig() {
-    return {learningRate: this.learningRate};
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"]);
-  }
-}
-SGDOptimizer.className = "SGD";
-registerClass(SGDOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class MomentumOptimizer extends SGDOptimizer {
-  constructor(learningRate, momentum, useNesterov = false) {
-    super(learningRate);
-    this.learningRate = learningRate;
-    this.momentum = momentum;
-    this.useNesterov = useNesterov;
-    this.accumulations = [];
-    this.m = scalar(this.momentum);
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i] == null) {
-        const trainable = false;
-        this.accumulations[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const accumulation = this.accumulations[i].variable;
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      tidy(() => {
-        let newValue;
-        const newAccumulation = add2(mul(this.m, accumulation), gradient);
-        if (this.useNesterov) {
-          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
-        } else {
-          newValue = add2(mul(this.c, newAccumulation), value);
-        }
-        accumulation.assign(newAccumulation);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    this.m.dispose();
-    if (this.accumulations != null) {
-      dispose(this.accumulations.map((v) => v.variable));
-    }
-  }
-  setMomentum(momentum) {
-    this.momentum = momentum;
-  }
-  async getWeights() {
-    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      momentum: this.momentum,
-      useNesterov: this.useNesterov
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["momentum"], config2["useNesterov"]);
-  }
-}
-MomentumOptimizer.className = "Momentum";
-registerClass(MomentumOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class RMSPropOptimizer extends Optimizer {
-  constructor(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    super();
-    this.learningRate = learningRate;
-    this.decay = decay;
-    this.momentum = momentum;
-    this.epsilon = epsilon2;
-    this.accumulatedMeanSquares = [];
-    this.accumulatedMoments = [];
-    this.accumulatedMeanGrads = [];
-    this.centered = centered;
-    if (epsilon2 == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-    if (learningRate == null) {
-      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
-    }
-  }
-  applyGradients(variableGradients) {
-    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedMeanSquares[i] == null) {
-        this.accumulatedMeanSquares[i] = {
-          originalName: `${name}/rms`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMoments[i] == null) {
-        this.accumulatedMoments[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMeanGrads[i] == null && this.centered) {
-        this.accumulatedMeanGrads[i] = {
-          originalName: `${name}/mg`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
-      const accumulatedMoments = this.accumulatedMoments[i].variable;
-      tidy(() => {
-        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-        if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
-          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
-          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        } else {
-          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
-          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
-          accumulatedMoments.assign(newAccumulatedMoments);
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        }
-      });
-    });
-    this.incrementIterations();
-  }
-  dispose() {
-    if (this.accumulatedMeanSquares != null) {
-      dispose(this.accumulatedMeanSquares.map((v) => v.variable));
-    }
-    if (this.accumulatedMeanGrads != null && this.centered) {
-      dispose(this.accumulatedMeanGrads.map((v) => v.variable));
-    }
-    if (this.accumulatedMoments != null) {
-      dispose(this.accumulatedMoments.map((v) => v.variable));
-    }
-  }
-  async getWeights() {
-    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
-    if (this.centered) {
-      variables.push(...this.accumulatedMeanGrads);
-    }
-    return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
-  }
-  async setWeights(weightValues) {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
-      originalName: v.name,
-      variable: v.tensor.variable(trainable)
-    }));
-    if (this.centered) {
-      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
-        originalName: v.name,
-        variable: v.tensor.variable(trainable)
-      }));
-    }
-  }
-  getConfig() {
-    return {
-      learningRate: this.learningRate,
-      decay: this.decay,
-      momentum: this.momentum,
-      epsilon: this.epsilon,
-      centered: this.centered
-    };
-  }
-  static fromConfig(cls, config2) {
-    return new cls(config2["learningRate"], config2["decay"], config2["momentum"], config2["epsilon"], config2["centered"]);
-  }
-}
-RMSPropOptimizer.className = "RMSProp";
-registerClass(RMSPropOptimizer);
-
-// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-class OptimizerConstructors {
-  static sgd(learningRate) {
-    return new SGDOptimizer(learningRate);
-  }
-  static momentum(learningRate, momentum, useNesterov = false) {
-    return new MomentumOptimizer(learningRate, momentum, useNesterov);
-  }
-  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
-    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon2, centered);
-  }
-  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null) {
-    return new AdamOptimizer(learningRate, beta1, beta2, epsilon2);
-  }
-  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon2 = null) {
-    return new AdadeltaOptimizer(learningRate, rho, epsilon2);
-  }
-  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null, decay = 0) {
-    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon2, decay);
-  }
-  static adagrad(learningRate, initialAccumulatorValue = 0.1) {
-    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/train.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-[
-  MomentumOptimizer,
-  SGDOptimizer,
-  AdadeltaOptimizer,
-  AdagradOptimizer,
-  RMSPropOptimizer,
-  AdamaxOptimizer,
-  AdamOptimizer
-];
-const train = {
-  sgd: OptimizerConstructors.sgd,
-  momentum: OptimizerConstructors.momentum,
-  adadelta: OptimizerConstructors.adadelta,
-  adagrad: OptimizerConstructors.adagrad,
-  rmsprop: OptimizerConstructors.rmsprop,
-  adamax: OptimizerConstructors.adamax,
-  adam: OptimizerConstructors.adam
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/browser_util.js
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const delayCallback = (() => {
-  if (typeof requestAnimationFrame !== "undefined") {
-    return requestAnimationFrame;
-  } else if (typeof setImmediate !== "undefined") {
-    return setImmediate;
-  }
-  return (f) => f();
-})();
-function nextFrame() {
-  return new Promise((resolve) => delayCallback(() => resolve()));
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getImageCenter(center, imageHeight, imageWidth) {
-  const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
-  const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
-  return [centerX, centerY];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function getReshaped(inputShape, blockShape, prod3, batchToSpace = true) {
-  let reshaped = [];
-  if (batchToSpace) {
-    reshaped = reshaped.concat(blockShape.slice(0));
-    reshaped.push(inputShape[0] / prod3);
-    reshaped = reshaped.concat(inputShape.slice(1));
-  } else {
-    reshaped = reshaped.concat(inputShape[0]);
-    const spatialLength = blockShape.length;
-    for (let i = 0; i < spatialLength; ++i) {
-      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
-    }
-    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
-  }
-  return reshaped;
-}
-function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
-  const permuted = [];
-  if (batchToSpace) {
-    permuted.push(blockShapeRank);
-    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
-      if (i <= 2 * blockShapeRank) {
-        permuted.push(i);
-        permuted.push(i - (blockShapeRank + 1));
-      } else {
-        permuted.push(i);
-      }
-    }
-  } else {
-    const permutedBeforeBatch = [];
-    const permutedAfterBatch = [];
-    for (let i = 1; i < reshapedRank; ++i) {
-      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
-        permutedAfterBatch.push(i);
-      } else {
-        permutedBeforeBatch.push(i);
-      }
-    }
-    permuted.push(...permutedBeforeBatch);
-    permuted.push(0);
-    permuted.push(...permutedAfterBatch);
-  }
-  return permuted;
-}
-function getReshapedPermuted(inputShape, blockShape, prod3, batchToSpace = true) {
-  const reshapedPermuted = [];
-  if (batchToSpace) {
-    reshapedPermuted.push(inputShape[0] / prod3);
-  } else {
-    reshapedPermuted.push(inputShape[0] * prod3);
-  }
-  for (let i = 1; i < inputShape.length; ++i) {
-    if (i <= blockShape.length) {
-      if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
-      } else {
-        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
-      }
-    } else {
-      reshapedPermuted.push(inputShape[i]);
-    }
-  }
-  return reshapedPermuted;
-}
-function getSliceBeginCoords(crops, blockShape) {
-  const sliceBeginCoords = [0];
-  for (let i = 0; i < blockShape; ++i) {
-    sliceBeginCoords.push(crops[i][0]);
-  }
-  return sliceBeginCoords;
-}
-function getSliceSize(uncroppedShape, crops, blockShape) {
-  const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i = 0; i < blockShape; ++i) {
-    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
-  }
-  return sliceSize;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const SELU_SCALEALPHA = 1.7580993408473768;
-const SELU_SCALE = 1.0507009873554805;
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ERF_P = 0.3275911;
-const ERF_A1 = 0.254829592;
-const ERF_A2 = -0.284496736;
-const ERF_A3 = 1.421413741;
-const ERF_A4 = -1.453152027;
-const ERF_A5 = 1.061405429;
-
-// node_modules/@tensorflow/tfjs-core/dist/log.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function warn(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.warn(...msg);
-  }
-}
-function log6(...msg) {
-  if (!env().getBool("IS_TEST")) {
-    console.log(...msg);
-  }
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function mergeRealAndImagArrays(real6, imag6) {
-  if (real6.length !== imag6.length) {
-    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real6.length}, imag: ${imag6.length}.`);
-  }
-  const result = new Float32Array(real6.length * 2);
-  for (let i = 0; i < result.length; i += 2) {
-    result[i] = real6[i / 2];
-    result[i + 1] = imag6[i / 2];
-  }
-  return result;
-}
-function splitRealAndImagArrays(complex9) {
-  const real6 = new Float32Array(complex9.length / 2);
-  const imag6 = new Float32Array(complex9.length / 2);
-  for (let i = 0; i < complex9.length; i += 2) {
-    real6[i / 2] = complex9[i];
-    imag6[i / 2] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithEvenIndex(complex9) {
-  const len = Math.ceil(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 0; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function complexWithOddIndex(complex9) {
-  const len = Math.floor(complex9.length / 4);
-  const real6 = new Float32Array(len);
-  const imag6 = new Float32Array(len);
-  for (let i = 2; i < complex9.length; i += 4) {
-    real6[Math.floor(i / 4)] = complex9[i];
-    imag6[Math.floor(i / 4)] = complex9[i + 1];
-  }
-  return {real: real6, imag: imag6};
-}
-function getComplexWithIndex(complex9, index) {
-  const real6 = complex9[index * 2];
-  const imag6 = complex9[index * 2 + 1];
-  return {real: real6, imag: imag6};
-}
-function assignToTypedArray(data, real6, imag6, index) {
-  data[index * 2] = real6;
-  data[index * 2 + 1] = imag6;
-}
-function exponents(n, inverse) {
-  const real6 = new Float32Array(n / 2);
-  const imag6 = new Float32Array(n / 2);
-  for (let i = 0; i < Math.ceil(n / 2); i++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
-    real6[i] = Math.cos(x);
-    imag6[i] = Math.sin(x);
-  }
-  return {real: real6, imag: imag6};
-}
-function exponent(k, n, inverse) {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
-  const real6 = Math.cos(x);
-  const imag6 = Math.sin(x);
-  return {real: real6, imag: imag6};
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
-const backend_util_exports = {};
-__export(backend_util_exports, {
-  ERF_A1: () => ERF_A1,
-  ERF_A2: () => ERF_A2,
-  ERF_A3: () => ERF_A3,
-  ERF_A4: () => ERF_A4,
-  ERF_A5: () => ERF_A5,
-  ERF_P: () => ERF_P,
-  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
-  SELU_SCALE: () => SELU_SCALE,
-  SELU_SCALEALPHA: () => SELU_SCALEALPHA,
-  applyActivation: () => applyActivation,
-  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
-  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
-  assertParamsConsistent: () => assertParamsConsistent,
-  assignToTypedArray: () => assignToTypedArray,
-  axesAreInnerMostDims: () => axesAreInnerMostDims,
-  calculateShapes: () => calculateShapes,
-  castTensor: () => castTensor,
-  combineLocations: () => combineLocations,
-  complexWithEvenIndex: () => complexWithEvenIndex,
-  complexWithOddIndex: () => complexWithOddIndex,
-  computeConv2DInfo: () => computeConv2DInfo,
-  computeConv3DInfo: () => computeConv3DInfo,
-  computeDefaultPad: () => computeDefaultPad,
-  computeDilation2DInfo: () => computeDilation2DInfo,
-  computeOptimalWindowSize: () => computeOptimalWindowSize,
-  computeOutAndReduceShapes: () => computeOutAndReduceShapes,
-  computeOutShape: () => computeOutShape2,
-  computePool2DInfo: () => computePool2DInfo,
-  computePool3DInfo: () => computePool3DInfo,
-  convertConv2DDataFormat: () => convertConv2DDataFormat,
-  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
-  expandShapeToKeepDim: () => expandShapeToKeepDim,
-  exponent: () => exponent,
-  exponents: () => exponents,
-  getAxesPermutation: () => getAxesPermutation,
-  getBroadcastDims: () => getBroadcastDims,
-  getComplexWithIndex: () => getComplexWithIndex,
-  getFusedBiasGradient: () => getFusedBiasGradient,
-  getFusedDyActivation: () => getFusedDyActivation,
-  getImageCenter: () => getImageCenter,
-  getInnerMostAxes: () => getInnerMostAxes,
-  getPermuted: () => getPermuted,
-  getReductionAxes: () => getReductionAxes,
-  getReshaped: () => getReshaped,
-  getReshapedPermuted: () => getReshapedPermuted,
-  getSliceBeginCoords: () => getSliceBeginCoords,
-  getSliceSize: () => getSliceSize,
-  getUndoAxesPermutation: () => getUndoAxesPermutation,
-  linspaceImpl: () => linspaceImpl,
-  log: () => log6,
-  mergeRealAndImagArrays: () => mergeRealAndImagArrays,
-  prepareAndValidate: () => prepareAndValidate,
-  prepareSplitSize: () => prepareSplitSize,
-  reshapeTensor: () => reshapeTensor,
-  segment_util: () => segment_util_exports,
-  shouldFuse: () => shouldFuse,
-  slice_util: () => slice_util_exports,
-  splitRealAndImagArrays: () => splitRealAndImagArrays,
-  tupleValuesAreOne: () => tupleValuesAreOne,
-  upcastType: () => upcastType,
-  validateInput: () => validateInput,
-  validateUpdateShape: () => validateUpdateShape,
-  warn: () => warn
-});
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function castTensor(x, dtype, backend2) {
-  if (dtype === "complex64") {
-    if (x.dtype === "complex64") {
-      return x.clone();
-    }
-    const zerosTensor = zeros(x.shape);
-    const floatX = cast(x, "float32");
-    const result = backend2.complex(floatX, zerosTensor);
-    zerosTensor.dispose();
-    floatX.dispose();
-    return result;
-  }
-  if (!hasEncodingLoss(x.dtype, dtype)) {
-    return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
-  }
-  if (x.dtype === "complex64") {
-    const real6 = backend2.real(x);
-    const result = cast(real6, dtype);
-    real6.dispose();
-    return result;
-  }
-  if (dtype === "int32") {
-    return backend2.int(x);
-  } else if (dtype === "bool") {
-    const zero = scalar(0, x.dtype);
-    const result = backend2.notEqual(x, zero);
-    zero.dispose();
-    return result;
-  } else {
-    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-  }
-}
-function reshapeTensor(x, shape) {
-  return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
-}
-function linspaceImpl(start, stop, num) {
-  const step7 = (stop - start) / (num - 1);
-  const values = makeZerosTypedArray(num, "float32");
-  values[0] = start;
-  for (let i = 1; i < values.length; i++) {
-    values[i] = values[i - 1] + step7;
-  }
-  return tensor1d(values, "float32");
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/split_shared.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function split5(x, sizeSplits, axis) {
-  const begin = new Array(x.rank).fill(0);
-  const size = x.shape.slice();
-  return sizeSplits.map((s) => {
-    const sliceSize = [...size];
-    sliceSize[axis] = s;
-    const sliceT = slice(x, begin, sliceSize);
-    begin[axis] += s;
-    return sliceT;
-  });
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/tile_impl.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function tile4(xBuf, reps) {
-  const newShape = new Array(xBuf.rank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = xBuf.shape[i] * reps[i];
-  }
-  const result = buffer(newShape, xBuf.dtype);
-  for (let i = 0; i < result.values.length; ++i) {
-    const newLoc = result.indexToLoc(i);
-    const originalLoc = new Array(xBuf.rank);
-    for (let j = 0; j < originalLoc.length; j++) {
-      originalLoc[j] = newLoc[j] % xBuf.shape[j];
-    }
-    const originalIndex = xBuf.locToIndex(originalLoc);
-    result.values[i] = xBuf.values[originalIndex];
-  }
-  return result.toTensor();
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/topk_impl.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function topkImpl(x, xShape, xDtype, k, sorted) {
-  const lastDim = xShape[xShape.length - 1];
-  const [batch, size] = [x.length / lastDim, lastDim];
-  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
-  const allTopKIndices = getTypedArrayFromDType("int32", batch * k);
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = x.subarray(offset, offset + size);
-    const valAndInd = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b2) => b2.value - a.value);
-    const outOffset = b * k;
-    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
-    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i = 0; i < k; i++) {
-      topKVals[i] = valAndInd[i].value;
-      topKIndices[i] = valAndInd[i].index;
-    }
-  }
-  const outputShape = xShape.slice();
-  outputShape[outputShape.length - 1] = k;
-  return [
-    tensor4(allTopKVals, outputShape, xDtype),
-    tensor4(allTopKIndices, outputShape, "int32")
-  ];
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
-const kernel_impls_exports = {};
-__export(kernel_impls_exports, {
-  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
-  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
-  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
-  split: () => split5,
-  tile: () => tile4,
-  topkImpl: () => topkImpl,
-  whereImpl: () => whereImpl
-});
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const EPSILON_FLOAT32 = 1e-7;
-const EPSILON_FLOAT16 = 1e-4;
-class DataStorage {
-  constructor(backend2, dataMover) {
-    this.backend = backend2;
-    this.dataMover = dataMover;
-    this.data = new WeakMap();
-    this.dataIdsCount = 0;
-  }
-  get(dataId) {
-    if (!this.data.has(dataId)) {
-      this.dataMover.moveData(this.backend, dataId);
-    }
-    return this.data.get(dataId);
-  }
-  set(dataId, value) {
-    this.dataIdsCount++;
-    this.data.set(dataId, value);
-  }
-  has(dataId) {
-    return this.data.has(dataId);
-  }
-  delete(dataId) {
-    this.dataIdsCount--;
-    return this.data.delete(dataId);
-  }
-  numDataIds() {
-    return this.dataIdsCount;
-  }
-}
-class KernelBackend {
-  time(f) {
-    return notYetImplemented("time");
-  }
-  read(dataId) {
-    return notYetImplemented("read");
-  }
-  readSync(dataId) {
-    return notYetImplemented("readSync");
-  }
-  numDataIds() {
-    return notYetImplemented("numDataIds");
-  }
-  disposeData(dataId) {
-    return notYetImplemented("disposeData");
-  }
-  write(values, shape, dtype) {
-    return notYetImplemented("write");
-  }
-  move(dataId, values, shape, dtype) {
-    return notYetImplemented("move");
-  }
-  memory() {
-    return notYetImplemented("memory");
-  }
-  floatPrecision() {
-    return notYetImplemented("floatPrecision");
-  }
-  epsilon() {
-    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-  }
-  batchMatMul(a, b, transposeA, transposeB) {
-    return notYetImplemented("batchMatMul");
-  }
-  fusedBatchMatMul({a, b, transposeA, transposeB, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedBatchMatMul");
-  }
-  slice(x, begin, size) {
-    return notYetImplemented("slice");
-  }
-  stridedSlice(x, begin, end, strides) {
-    return notYetImplemented("stridedSlice");
-  }
-  unstack(x, axis) {
-    return notYetImplemented("unstack");
-  }
-  reverse(a, axis) {
-    return notYetImplemented("reverse");
-  }
-  concat(tensors, axis) {
-    return notYetImplemented("concat");
-  }
-  neg(a) {
-    return notYetImplemented("neg");
-  }
-  add(a, b) {
-    return notYetImplemented("add");
-  }
-  addN(tensors) {
-    return notYetImplemented("addN");
-  }
-  subtract(a, b) {
-    return notYetImplemented("subtract");
-  }
-  multiply(a, b) {
-    return notYetImplemented("multiply");
-  }
-  realDivide(a, b) {
-    return notYetImplemented("realDivide");
-  }
-  floorDiv(a, b) {
-    return notYetImplemented("floorDiv");
-  }
-  sum(x, axes) {
-    return notYetImplemented("sum");
-  }
-  prod(x, axes) {
-    return notYetImplemented("prod");
-  }
-  unsortedSegmentSum(x, segmentIds, numSegments) {
-    return notYetImplemented("unsortedSegmentSum");
-  }
-  argMin(x, axis) {
-    return notYetImplemented("argMin");
-  }
-  argMax(x, axis) {
-    return notYetImplemented("argMax");
-  }
-  equal(a, b) {
-    return notYetImplemented("equal");
-  }
-  notEqual(a, b) {
-    return notYetImplemented("notEqual");
-  }
-  less(a, b) {
-    return notYetImplemented("less");
-  }
-  lessEqual(a, b) {
-    return notYetImplemented("lessEqual");
-  }
-  greater(a, b) {
-    return notYetImplemented("greater");
-  }
-  greaterEqual(a, b) {
-    return notYetImplemented("greaterEqual");
-  }
-  logicalNot(a) {
-    return notYetImplemented("logicalNot");
-  }
-  logicalAnd(a, b) {
-    return notYetImplemented("logicalAnd");
-  }
-  logicalOr(a, b) {
-    return notYetImplemented("logicalOr");
-  }
-  where(condition) {
-    return notYetImplemented("where");
-  }
-  select(condition, a, b) {
-    return notYetImplemented("select");
-  }
-  topk(x, k, sorted) {
-    return notYetImplemented("topk");
-  }
-  min(x, axes) {
-    return notYetImplemented("min");
-  }
-  minimum(a, b) {
-    return notYetImplemented("minimum");
-  }
-  mod(a, b) {
-    return notYetImplemented("mod");
-  }
-  max(x, axes) {
-    return notYetImplemented("max");
-  }
-  maximum(a, b) {
-    return notYetImplemented("maximum");
-  }
-  all(x, axes) {
-    return notYetImplemented("all");
-  }
-  any(x, axes) {
-    return notYetImplemented("any");
-  }
-  squaredDifference(a, b) {
-    return notYetImplemented("squaredDifference");
-  }
-  ceil(x) {
-    return notYetImplemented("ceil");
-  }
-  floor(x) {
-    return notYetImplemented("floor");
-  }
-  round(x) {
-    return notYetImplemented("round");
-  }
-  sign(x) {
-    return notYetImplemented("sign");
-  }
-  isNaN(x) {
-    return notYetImplemented("isNaN");
-  }
-  isInf(x) {
-    return notYetImplemented("isInf");
-  }
-  isFinite(x) {
-    return notYetImplemented("isFinite");
-  }
-  pow(a, b) {
-    return notYetImplemented("pow");
-  }
-  exp(x) {
-    return notYetImplemented("exp");
-  }
-  expm1(x) {
-    return notYetImplemented("expm1");
-  }
-  softmax(x, dim) {
-    return notYetImplemented("softmax");
-  }
-  log(x) {
-    return notYetImplemented("log");
-  }
-  log1p(x) {
-    return notYetImplemented("log1p");
-  }
-  sqrt(x) {
-    return notYetImplemented("sqrt");
-  }
-  rsqrt(x) {
-    return notYetImplemented("rsqrt");
-  }
-  square(x) {
-    return notYetImplemented("square");
-  }
-  reciprocal(x) {
-    return notYetImplemented("reciprocal");
-  }
-  relu(x) {
-    return notYetImplemented("relu");
-  }
-  relu6(x) {
-    return notYetImplemented("relu6");
-  }
-  prelu(x, a) {
-    return notYetImplemented("prelu");
-  }
-  elu(x) {
-    return notYetImplemented("elu");
-  }
-  eluDer(dy, y) {
-    return notYetImplemented("eluDer");
-  }
-  selu(x) {
-    return notYetImplemented("selu");
-  }
-  int(x) {
-    return notYetImplemented("int");
-  }
-  clip(x, min4, max6) {
-    return notYetImplemented("clip");
-  }
-  abs(x) {
-    return notYetImplemented("abs");
-  }
-  complexAbs(x) {
-    return notYetImplemented("complexAbs");
-  }
-  sigmoid(x) {
-    return notYetImplemented("sigmoid");
-  }
-  softplus(x) {
-    return notYetImplemented("softplus");
-  }
-  sin(x) {
-    return notYetImplemented("sin");
-  }
-  cos(x) {
-    return notYetImplemented("cos");
-  }
-  tan(x) {
-    return notYetImplemented("tan");
-  }
-  asin(x) {
-    return notYetImplemented("asin");
-  }
-  acos(x) {
-    return notYetImplemented("acos");
-  }
-  atan(x) {
-    return notYetImplemented("atan");
-  }
-  atan2(a, b) {
-    return notYetImplemented("atan2");
-  }
-  sinh(x) {
-    return notYetImplemented("sinh");
-  }
-  cosh(x) {
-    return notYetImplemented("cosh");
-  }
-  tanh(x) {
-    return notYetImplemented("tanh");
-  }
-  asinh(x) {
-    return notYetImplemented("asinh");
-  }
-  acosh(x) {
-    return notYetImplemented("acosh");
-  }
-  atanh(x) {
-    return notYetImplemented("atanh");
-  }
-  erf(x) {
-    return notYetImplemented("erf");
-  }
-  step(x, alpha) {
-    return notYetImplemented("step");
-  }
-  fusedConv2d({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedConv2d");
-  }
-  conv2d(x, filter, convInfo) {
-    return notYetImplemented("conv2d");
-  }
-  conv2dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv2dDerInput");
-  }
-  conv2dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv2dDerFilter");
-  }
-  fusedDepthwiseConv2D({input, filter, convInfo, bias, activation, preluActivationWeights}) {
-    return notYetImplemented("fusedDepthwiseConv2D");
-  }
-  depthwiseConv2D(input, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2D");
-  }
-  depthwiseConv2DDerInput(dy, filter, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerInput");
-  }
-  depthwiseConv2DDerFilter(x, dY, convInfo) {
-    return notYetImplemented("depthwiseConv2DDerFilter");
-  }
-  conv3d(x, filter, convInfo) {
-    return notYetImplemented("conv3d");
-  }
-  conv3dDerInput(dy, filter, convInfo) {
-    return notYetImplemented("conv3dDerInput");
-  }
-  conv3dDerFilter(x, dY, convInfo) {
-    return notYetImplemented("conv3dDerFilter");
-  }
-  maxPool(x, convInfo) {
-    return notYetImplemented("maxPool");
-  }
-  maxPoolBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPoolBackprop");
-  }
-  avgPool(x, convInfo) {
-    return notYetImplemented("avgPool");
-  }
-  avgPoolBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPoolBackprop");
-  }
-  avgPool3d(x, convInfo) {
-    return notYetImplemented("avgPool3d");
-  }
-  avgPool3dBackprop(dy, x, convInfo) {
-    return notYetImplemented("avgPool3dBackprop");
-  }
-  maxPool3d(x, convInfo) {
-    return notYetImplemented("maxPool3d");
-  }
-  maxPool3dBackprop(dy, x, y, convInfo) {
-    return notYetImplemented("maxPool3dBackprop");
-  }
-  reshape(x, shape) {
-    return notYetImplemented("reshape");
-  }
-  cast(x, dtype) {
-    return notYetImplemented("cast");
-  }
-  tile(x, reps) {
-    return notYetImplemented("tile");
-  }
-  pad(x, paddings, constantValue) {
-    return notYetImplemented("pad");
-  }
-  transpose(x, perm) {
-    return notYetImplemented("transpose");
-  }
-  gather(x, indices, axis) {
-    return notYetImplemented("gather");
-  }
-  gatherND(x, indices) {
-    return notYetImplemented("gatherND");
-  }
-  scatterND(indices, updates, shape) {
-    return notYetImplemented("scatterND");
-  }
-  batchToSpaceND(x, blockShape, crops) {
-    return notYetImplemented("batchToSpaceND");
-  }
-  spaceToBatchND(x, blockShape, paddings) {
-    return notYetImplemented("spaceToBatchND");
-  }
-  resizeBilinear(x, newHeight, newWidth, alignCorners) {
-    return notYetImplemented("resizeBilinear");
-  }
-  resizeBilinearBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeBilinearBackprop");
-  }
-  resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {
-    return notYetImplemented("resizeNearestNeighbor");
-  }
-  resizeNearestNeighborBackprop(dy, x, alignCorners) {
-    return notYetImplemented("resizeNearestNeighborBackprop");
-  }
-  batchNorm(x, mean5, variance, offset, scale2, varianceEpsilon) {
-    return notYetImplemented("batchNorm");
-  }
-  localResponseNormalization4D(x, radius, bias, alpha, beta) {
-    return notYetImplemented("localResponseNormalization4D");
-  }
-  LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {
-    return notYetImplemented("LRNGrad");
-  }
-  multinomial(logits, normalized, numSamples, seed) {
-    return notYetImplemented("multinomial");
-  }
-  oneHot(indices, depth, onValue, offValue) {
-    return notYetImplemented("oneHot");
-  }
-  cumsum(x, axis, exclusive, reverse9) {
-    return notYetImplemented("cumsum");
-  }
-  nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
-    return notYetImplemented("nonMaxSuppression");
-  }
-  fft(x) {
-    return notYetImplemented("fft");
-  }
-  ifft(x) {
-    return notYetImplemented("ifft");
-  }
-  complex(real6, imag6) {
-    return notYetImplemented("complex");
-  }
-  real(input) {
-    return notYetImplemented("real");
-  }
-  imag(input) {
-    return notYetImplemented("imag");
-  }
-  cropAndResize(image2, boxes, boxIndex, cropSize, method, extrapolationValue) {
-    return notYetImplemented("cropAndResize");
-  }
-  depthToSpace(x, blockSize, dataFormat) {
-    return notYetImplemented("depthToSpace");
-  }
-  split(value, sizeSplits, axis) {
-    return notYetImplemented("split");
-  }
-  sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {
-    return notYetImplemented("sparseToDense");
-  }
-  diag(x) {
-    return notYetImplemented("diag");
-  }
-  fill(shape, value, dtype) {
-    return notYetImplemented("fill");
-  }
-  onesLike(x) {
-    return notYetImplemented("onesLike");
-  }
-  zerosLike(x) {
-    return notYetImplemented("zerosLike");
-  }
-  linspace(start, stop, num) {
-    return notYetImplemented("linspace");
-  }
-  dispose() {
-    return notYetImplemented("dispose");
-  }
-}
-function notYetImplemented(kernelName) {
-  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. Did you forget to import the kernel?`);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const absGradConfig = {
-  kernelName: Abs,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, step(cast(x, "float32"), -1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acosGradConfig = {
-  kernelName: Acos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = square(cast(x, "float32"));
-        const b = sqrt(sub(scalar(1), a));
-        return neg(div(dy, b));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const acoshGradConfig = {
-  kernelName: Acosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(sub(square(cast(x, "float32")), 1));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addGradConfig = {
-  kernelName: Add,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const addNGradConfig = {
-  kernelName: AddN,
-  saveAllInputs: true,
-  gradFunc: (dy, saved) => {
-    const ders = {};
-    saved.forEach((_, i) => {
-      ders[i] = () => dy.clone();
-    });
-    return ders;
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMaxGradConfig = {
-  kernelName: ArgMax,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const argMinGradConfig = {
-  kernelName: ArgMin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinGradConfig = {
-  kernelName: Asin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32")))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const asinhGradConfig = {
-  kernelName: Asinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
-        return div(dy, a);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atan2GradConfig = {
-  kernelName: Atan2,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const d = add2(square(a), square(b));
-      let res = mul(dy, div(b, d));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      const d = add2(square(a), square(b));
-      let res = neg(mul(dy, div(a, d)));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanGradConfig = {
-  kernelName: Atan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(square(cast(x, "float32")), 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const atanhGradConfig = {
-  kernelName: Atanh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, sub(scalar(1), square(cast(x, "float32"))))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "avgPool3dBackprop");
-  const $input = convertToTensor(input, "input", "avgPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.avgPool3dBackprop(dy5D, input5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const avgPool3dBackprop = op({avgPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPool3DGradConfig = {
-  kernelName: AvgPool3D,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function avgPoolBackprop_(dy, input, filterSize, strides, pad8) {
-  const $dy = convertToTensor(dy, "dy", "avgPoolBackprop");
-  const $input = convertToTensor(input, "input", "avgPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  let input4D = $input;
-  let dy4D = $dy;
-  let reshapedTo4D = false;
-  if ($input.rank === 3) {
-    reshapedTo4D = true;
-    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
-    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
-  }
-  assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`);
-  assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo(input4D.shape, filterSize, strides, 1, pad8);
-    return backend2.avgPoolBackprop(dy4D, input4D, convInfo);
-  };
-  const inputs = {dy: dy4D, input: input4D};
-  const attrs = {filterSize, strides, pad: pad8};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
-  }
-  return res;
-}
-const avgPoolBackprop = op({avgPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const avgPoolGradConfig = {
-  kernelName: AvgPool,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => avgPoolBackprop(dy, x, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchMatMulGradConfig = {
-  kernelName: BatchMatMul,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved, attrs) => {
-    const [a, b] = saved;
-    const {transposeA, transposeB} = attrs;
-    if (!transposeA && !transposeB) {
-      return {
-        a: () => matMul(dy, b, false, true),
-        b: () => matMul(a, dy, true, false)
-      };
-    } else if (!transposeA && transposeB) {
-      return {
-        a: () => matMul(dy, b, false, false),
-        b: () => matMul(dy, a, true, false)
-      };
-    } else if (transposeA && !transposeB) {
-      return {
-        a: () => matMul(b, dy, false, true),
-        b: () => matMul(a, dy, false, false)
-      };
-    } else {
-      return {
-        a: () => matMul(b, dy, true, true),
-        b: () => matMul(dy, a, true, true)
-      };
-    }
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const batchToSpaceNDGradConfig = {
-  kernelName: BatchToSpaceND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, crops} = attrs;
-    return {x: () => spaceToBatchND(dy, blockShape, crops)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const broadcastToGradConfig = {
-  kernelName: BroadcastTo,
-  gradFunc: (dy, saved, attrs) => {
-    const broadCastToAttrs = attrs;
-    const inputShape = broadCastToAttrs.inputShape;
-    const outputShape = broadCastToAttrs.shape;
-    const reps = Array.from(outputShape);
-    for (let i = inputShape.length - 1; i >= 0; i--) {
-      if (inputShape[i] === outputShape[i]) {
-        reps[i] = 1;
-      } else if (inputShape[i] !== 1) {
-        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
-      }
-    }
-    const axes = [];
-    for (let i = 0; i < reps.length; i++) {
-      if (reps[i] > 1) {
-        axes.push(i);
-      }
-    }
-    return {x: () => sum2(dy, axes, true)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const castGradConfig = {
-  kernelName: Cast,
-  gradFunc: (dy) => {
-    return {x: () => dy.clone()};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const ceilGradConfig = {
-  kernelName: Ceil,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const clipByValueGradConfig = {
-  kernelName: ClipByValue,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {clipValueMin, clipValueMax} = attrs;
-    return {
-      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const concatGradConfig = {
-  kernelName: Concat,
-  saveAllInputs: true,
-  gradFunc: (dy, saved, attrs) => {
-    const shapes = saved.map((t) => t.shape);
-    const {axis} = attrs;
-    const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map((s) => s[$axis]);
-    const derTensors = split(dy, sizeSplits, $axis);
-    return derTensors.map((t) => () => t);
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DGradConfig = {
-  kernelName: Conv2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x4D, $filter] = saved;
-    const {dilations, strides, pad: pad8, dataFormat} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    return {
-      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad8, dataFormat),
-      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad8, dataFormat)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv2DBackpropInputGradConfig = {
-  kernelName: Conv2DBackpropInput,
-  inputsToSave: ["dy", "filter"],
-  gradFunc: (ddx, saved, attrs) => {
-    const [dy, filter] = saved;
-    const {strides, pad: pad8, dataFormat, dimRoundingMode} = attrs;
-    return {
-      dy: () => conv2d(ddx, filter, strides, pad8, dataFormat, 1, dimRoundingMode),
-      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad8, dataFormat, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function conv3DBackpropFilter_(x, dy, filterShape, strides, pad8) {
-  let x5D = x;
-  if (x.rank === 4) {
-    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
-  }
-  let dy5D = dy;
-  if (dy5D.rank === 4) {
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-  }
-  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
-  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
-  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
-  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
-  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
-  const forward = (backend2) => {
-    const dilations = 1;
-    const convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad8);
-    return backend2.conv3dDerFilter(x5D, dy5D, convInfo);
-  };
-  const inputs = {x: x5D, y: dy5D};
-  const attrs = {strides, pad: pad8};
-  return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);
-}
-const conv3DBackpropFilter = op({conv3DBackpropFilter_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const conv3DGradConfig = {
-  kernelName: Conv3D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8} = attrs;
-    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
-    const [x5D, $filter] = saved;
-    return {
-      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad8),
-      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cosGradConfig = {
-  kernelName: Cos,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(neg(sin(cast(x, "float32"))), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const coshGradConfig = {
-  kernelName: Cosh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(sinh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const cumsumGradConfig = {
-  kernelName: Cumsum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {axis, exclusive, reverse: reverse9} = attrs;
-    return {
-      x: () => {
-        const permutation = getAxesPermutation([axis], x.rank);
-        let out = cumsum(dy, axis, exclusive, !reverse9);
-        if (permutation != null) {
-          out = transpose(out, permutation);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const depthwiseConv2dNativeGradConfig = {
-  kernelName: DepthwiseConv2dNative,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const {dilations, strides, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1] : dilations;
-    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
-    const [x, filter] = saved;
-    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
-    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
-    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
-    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
-    if (dimRoundingMode != null) {
-      assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-    }
-    const convInfo = computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad8, dimRoundingMode, true);
-    return {
-      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),
-      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const dilation2dGradConfig = {
-  kernelName: Dilation2D,
-  inputsToSave: ["x", "filter"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, filter] = saved;
-    const inputInputs = {x, filter, dy};
-    const filterInputs = {x, filter, dy};
-    return {
-      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
-      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Div_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const divGradConfig = {
-  kernelName: Div,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const eluGradConfig = {
-  kernelName: Elu,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    const backPropKernelFunc = (backend2) => {
-      return backend2.eluDer(dy, y);
-    };
-    const inputs = {dy, y};
-    return {
-      x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, EluGrad)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const erfGradConfig = {
-  kernelName: Erf,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
-    return {x: () => mul(dy, a)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expGradConfig = {
-  kernelName: Exp,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, y)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const expm1GradConfig = {
-  kernelName: Expm1,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, exp(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorGradConfig = {
-  kernelName: Floor,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const floorDivGradConfig = {
-  kernelName: FloorDiv,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum2(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, "float32")));
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const fusedBatchNormGradConfig = {
-  kernelName: FusedBatchNorm,
-  inputsToSave: ["x", "mean", "variance", "scale"],
-  gradFunc: (dy, saved, attrs) => {
-    const {varianceEpsilon} = attrs;
-    const [x, mean5, variance, scale2] = saved;
-    const scaleValue = scale2 == null ? scalar(1) : scale2;
-    const reductionAxes = getReductionAxes(mean5.shape, x.shape);
-    const tileShape = [];
-    if (mean5.rank === 1) {
-      for (let i = 0; i < x.shape.length - 1; ++i) {
-        tileShape.push(x.shape[i]);
-      }
-      tileShape.push(1);
-    }
-    const xMinusMean = sub(x, mean5);
-    const dyTimesScaleValue = mul(dy, scaleValue);
-    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
-    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
-    const derX = () => {
-      if (mean5.rank === 1) {
-        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
-      } else {
-        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
-      }
-    };
-    const derMean = () => {
-      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        meanDer = sum2(meanDer, reductionAxes);
-      }
-      return reshape(meanDer, mean5.shape);
-    };
-    const derVariance = () => {
-      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
-      if (mean5.rank === 1) {
-        varianceDer = sum2(varianceDer, reductionAxes);
-      }
-      return reshape(varianceDer, mean5.shape);
-    };
-    const derScale = () => {
-      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
-      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
-      if (mean5.rank === 1) {
-        scaleDer = sum2(scaleDer, reductionAxes);
-      }
-      return reshape(scaleDer, mean5.shape);
-    };
-    const derOffset = () => {
-      let offsetDer = dy;
-      if (mean5.rank === 1) {
-        offsetDer = sum2(offsetDer, reductionAxes);
-      }
-      return reshape(offsetDer, mean5.shape);
-    };
-    return {
-      x: derX,
-      mean: derMean,
-      variance: derVariance,
-      scale: derScale,
-      offset: derOffset
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gatherGradConfig = {
-  kernelName: GatherV2,
-  inputsToSave: ["x", "indices"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, indices] = saved;
-    const {axis} = attrs;
-    const parsedAxis = parseAxisParam(axis, x.shape)[0];
-    const derX = () => {
-      const paramsShape = x.shape;
-      const indicesSize = indices.size;
-      const outerShape = paramsShape.slice(0, parsedAxis);
-      const outerDims = outerShape.length;
-      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
-      const innerDims = innerShape.length;
-      const outerAxesIndices = arrayRange(0, outerDims);
-      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
-      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
-      const values = reshape(dy, valuesShape);
-      const reshapedIndices = reshape(indices, [indicesSize]);
-      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
-      const valuesTranspose = transpose(values, transposeDims);
-      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
-      const invertTransposeDims = getUndoAxesPermutation(transposeDims);
-      paramsGrad = transpose(paramsGrad, invertTransposeDims);
-      return paramsGrad;
-    };
-    return {x: derX, indices: () => indices};
-  }
-};
-function arrayRange(start, stop) {
-  const result = [];
-  for (let i = start; i < stop; ++i) {
-    result.push(i);
-  }
-  return result;
-}
-function arrayConcat(arrays) {
-  const result = [];
-  for (let i = 0; i < arrays.length; ++i) {
-    for (let j = 0; j < arrays[i].length; ++j) {
-      result.push(arrays[i][j]);
-    }
-  }
-  return result;
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const greaterEqualGradConfig = {
-  kernelName: GreaterEqual,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    return {a: () => zerosLike(a), b: () => zerosLike(b)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const identityGradConfig = {
-  kernelName: Identity,
-  gradFunc: (dy) => {
-    return {x: () => cast(dy, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isFiniteGradConfig = {
-  kernelName: IsFinite,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isInfGradConfig = {
-  kernelName: IsInf,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const isNanGradConfig = {
-  kernelName: IsNan,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const log1pGradConfig = {
-  kernelName: Log1p,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, add2(x, 1))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logGradConfig = {
-  kernelName: Log,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, cast(x, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const logSoftmaxGradConfig = {
-  kernelName: LogSoftmax,
-  inputsToSave: [],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [value] = saved;
-    const {axis} = attrs;
-    return {
-      logits: () => {
-        const keepDims = true;
-        const softmax3 = exp(value);
-        return sub(dy, mul(sum2(dy, axis, keepDims), softmax3));
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
-  const forward = (backend2) => backend2.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);
-  const inputs = {x, y, dy};
-  const attrs = {depthRadius, bias, alpha, beta};
-  return ENGINE.runKernelFunc(forward, inputs, null, LRNBackprop, attrs);
-}
-const localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const lrnGradConfig = {
-  kernelName: LRN,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {depthRadius, bias, alpha, beta} = attrs;
-    return {
-      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
-  if (y.rank < xOrig.rank) {
-    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
-  }
-  if (dy.rank < xOrig.rank) {
-    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
-  }
-  return {
-    x: () => {
-      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
-      return permutedAxes == null ? dx : transpose(dx, permutedAxes);
-    }
-  };
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxGradConfig = {
-  kernelName: Max,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const maxAttrs = attrs;
-    const {reductionIndices} = maxAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(reductionIndices, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = maxGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maximumGradConfig = {
-  kernelName: Maximum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(less(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPool3dBackprop");
-  const $input = convertToTensor(input, "input", "maxPool3dBackprop");
-  const $output = convertToTensor(output, "output", "maxPool3dBackprop");
-  let dy5D = $dy;
-  let input5D = $input;
-  let output5D = $output;
-  let reshapedTo5D = false;
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1,
-      $input.shape[0],
-      $input.shape[1],
-      $input.shape[2],
-      $input.shape[3]
-    ]);
-    output5D = reshape($output, [
-      1,
-      $output.shape[0],
-      $output.shape[1],
-      $output.shape[2],
-      $output.shape[3]
-    ]);
-  }
-  assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`);
-  assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`);
-  assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`);
-  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode);
-    return backend2.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);
-  };
-  const inputs = {dy: dy5D, input: input5D, output: output5D};
-  const attrs = {filterSize, strides, dilations, pad: pad8, dimRoundingMode};
-  const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3DBackprop, attrs);
-  if (reshapedTo5D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
-  }
-  return res;
-}
-const maxPool3dBackprop = op({maxPool3dBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPool3DGradConfig = {
-  kernelName: MaxPool3D,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, dilations, pad: pad8, dimRoundingMode} = attrs;
-    const $dilations = dilations == null ? [1, 1, 1] : dilations;
-    return {
-      x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad8, dimRoundingMode)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_backprop.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function maxPoolBackprop_(dy, input, output, filterSize, strides, pad8, dimRoundingMode) {
-  const $dy = convertToTensor(dy, "dy", "maxPoolBackprop");
-  const $input = convertToTensor(input, "input", "maxPoolBackprop");
-  const $output = convertToTensor(output, "output", "maxPoolBackprop");
-  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
-  assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`);
-  assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`);
-  if (dimRoundingMode != null) {
-    assert(isInt(pad8), () => `Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
-  }
-  const forward = (backend2) => {
-    const convInfo = computePool2DInfo($input.shape, filterSize, strides, 1, pad8, dimRoundingMode);
-    return backend2.maxPoolBackprop($dy, $input, $output, convInfo);
-  };
-  const inputs = {dy: $dy, input: $input, output: $output};
-  const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
-  return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);
-}
-const maxPoolBackprop = op({maxPoolBackprop_});
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const maxPoolGradConfig = {
-  kernelName: MaxPool,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [x, y] = saved;
-    const {filterSize, strides, pad: pad8} = attrs;
-    return {
-      x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad8)
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minGradConfig = {
-  kernelName: Min,
-  inputsToSave: ["x"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const minAttrs = attrs;
-    const {axis} = minAttrs;
-    const [x, y] = saved;
-    const origAxes = parseAxisParam(axis, x.shape);
-    const permutedAxes = getAxesPermutation(origAxes, x.rank);
-    const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);
-    return {
-      x: () => {
-        let out = minGrad["x"]();
-        if (permutedAxes != null) {
-          out = transpose(out);
-        }
-        return out;
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const minimumGradConfig = {
-  kernelName: Minimum,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
-    const derB = () => mul(dy, cast(greater(a, b), "float32"));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const modGradConfig = {
-  kernelName: Mod,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(dy, reduceAxes), a.shape);
-      }
-      return dy;
-    };
-    const derB = () => {
-      const res = mul(dy, neg(floor(div(a, b))));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const multiplyGradConfig = {
-  kernelName: Multiply,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = mul(dy, cast(b, "float32"));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      const res = mul(dy, cast(a, "float32"));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum2(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Negate_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const negateGradConfig = {
-  kernelName: Negate,
-  gradFunc: (dy) => {
-    return {x: () => neg(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const oneHotGradConfig = {
-  kernelName: OneHot,
-  inputsToSave: ["indices"],
-  gradFunc: (dy, saved) => {
-    const indices = saved[0];
-    return {indices: () => zeros(indices.shape, "float32")};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const onesLikeGradConfig = {
-  kernelName: OnesLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const padV2GradConfig = {
-  kernelName: PadV2,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const x = saved[0];
-    const {paddings} = attrs;
-    const begin = paddings.map((p) => p[0]);
-    return {x: () => slice(dy, begin, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const powGradConfig = {
-  kernelName: Pow,
-  inputsToSave: ["a", "b"],
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [a, b, y] = saved;
-    const base = a;
-    const exp11 = b;
-    const outShape = assertAndGetBroadcastShape(base.shape, exp11.shape);
-    const derBase = () => {
-      const expFloat = cast(exp11, "float32");
-      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
-      const reduceAxes = getReductionAxes(base.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, base.shape);
-    };
-    const derExp = () => {
-      const condition = greater(base, 0);
-      const logBase = where(condition, log(base), zerosLike(base));
-      let res = mul(dy, mul(y, logBase));
-      const reduceAxes = getReductionAxes(exp11.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, exp11.shape);
-    };
-    return {a: derBase, b: derExp};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const preluGradConfig = {
-  kernelName: Prelu,
-  inputsToSave: ["x", "alpha"],
-  gradFunc: (dy, saved) => {
-    const [x, alpha] = saved;
-    const mask = greater(x, 0);
-    return {
-      x: () => where(mask, dy, mul(dy, alpha)),
-      alpha: () => {
-        let res = where(mask, zerosLike(dy), mul(dy, x));
-        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
-        if (reduceAxes.length > 0) {
-          res = sum2(res, reduceAxes);
-        }
-        return reshape(res, alpha.shape);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reciprocalGradConfig = {
-  kernelName: Reciprocal,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, neg(square(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const relu6GradConfig = {
-  kernelName: Relu6,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    const mask = mul(lessEqual(x, 6), step(x));
-    return {x: () => mul(dy, cast(mask, "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reluGradConfig = {
-  kernelName: Relu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, cast(step(x), "float32"))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reshapeGradConfig = {
-  kernelName: Reshape,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => reshape(dy, x.shape)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeBilinearGradConfig = {
-  kernelName: ResizeBilinear,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeBilinearBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeBilinearGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const resizeNearestNeighborGradConfig = {
-  kernelName: ResizeNearestNeighbor,
-  inputsToSave: ["images"],
-  gradFunc: (dy, saved, attrs) => {
-    const [images] = saved;
-    const backPropKernelFunc = (backend2) => {
-      const {alignCorners} = attrs;
-      return backend2.resizeNearestNeighborBackprop(dy, images, alignCorners);
-    };
-    const inputs = {images};
-    const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null, ResizeNearestNeighborGrad, attrs);
-    return {images: imagesDer};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const reverseGradConfig = {
-  kernelName: Reverse,
-  gradFunc: (dy, saved, attrs) => {
-    const {dims} = attrs;
-    const axes = parseAxisParam(dims, dy.shape);
-    return {x: () => reverse(dy, axes)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const roundGradConfig = {
-  kernelName: Round,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const rsqrtGradConfig = {
-  kernelName: Rsqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SelectV2_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const selectV2PoolGradConfig = {
-  kernelName: SelectV2,
-  inputsToSave: ["condition"],
-  gradFunc: (dy, saved) => {
-    const [condition] = saved;
-    return {
-      condition: () => cast(zerosLike(condition), "float32"),
-      t: () => mul(dy, cast(condition, dy.dtype)),
-      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const seluGradConfig = {
-  kernelName: Selu,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const mask = greater(x, scalar(0));
-        const scaleAlpha = scalar(SELU_SCALEALPHA);
-        const scale2 = scalar(SELU_SCALE);
-        const greaterThanZeroDer = mul(dy, scale2);
-        const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, "float32")));
-        return where(mask, greaterThanZeroDer, lessEqualZeroDer);
-      }
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sigmoidGradConfig = {
-  kernelName: Sigmoid,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const signGradConfig = {
-  kernelName: Sign,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinGradConfig = {
-  kernelName: Sin,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cos(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sinhGradConfig = {
-  kernelName: Sinh,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(cosh(cast(x, "float32")), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sliceGradConfig = {
-  kernelName: Slice,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {begin, size} = attrs;
-    const inputShape = x.shape;
-    const [begin_, size_] = parseSliceParams(x, begin, size);
-    const paddings = [];
-    for (let i = 0; i < dy.rank; i++) {
-      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
-    }
-    return {x: () => pad(dy, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softmaxGradConfig = {
-  kernelName: Softmax,
-  outputsToSave: [true],
-  gradFunc: (dy, saved, attrs) => {
-    const [y] = saved;
-    const {dim} = attrs;
-    const keepDims = true;
-    const dyTimesY = mul(dy, y);
-    return {
-      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
-    };
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const softplusGradConfig = {
-  kernelName: Softplus,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, sigmoid(x))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const spaceToBatchNDGradConfig = {
-  kernelName: SpaceToBatchND,
-  gradFunc: (dy, saved, attrs) => {
-    const {blockShape, paddings} = attrs;
-    return {x: () => batchToSpaceND(dy, blockShape, paddings)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const splitVGradConfig = {
-  kernelName: SplitV,
-  gradFunc: (dy, saved, attrs) => {
-    const {axis} = attrs;
-    return {x: () => concat(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sqrtGradConfig = {
-  kernelName: Sqrt,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, mul(sqrt(cast(x, "float32")), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squareGradConfig = {
-  kernelName: Square,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => mul(dy, mul(cast(x, "float32"), 2))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const squaredDifferenceGradConfig = {
-  kernelName: SquaredDifference,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const two = scalar(2);
-    const derA = () => mul(dy, mul(two, sub(a, b)));
-    const derB = () => mul(dy, mul(two, sub(b, a)));
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const stepGradConfig = {
-  kernelName: Step,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const subGradConfig = {
-  kernelName: Sub,
-  inputsToSave: ["a", "b"],
-  gradFunc: (dy, saved) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum2(res, reduceAxes);
-      }
-      return reshape(neg(res), b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const sumGradConfig = {
-  kernelName: Sum,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const expandedDyShape = x.shape.slice();
-    const {axis} = attrs;
-    const axes = parseAxisParam(axis, x.shape);
-    axes.forEach((axis2) => {
-      expandedDyShape[axis2] = 1;
-    });
-    const expandedDy = reshape(dy, expandedDyShape);
-    const derX = mul(expandedDy, ones2(x.shape, "float32"));
-    return {x: () => derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanGradConfig = {
-  kernelName: Tan,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved) => {
-    const [x] = saved;
-    return {x: () => div(dy, square(cos(x)))};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tanhGradConfig = {
-  kernelName: Tanh,
-  outputsToSave: [true],
-  gradFunc: (dy, saved) => {
-    const [y] = saved;
-    return {x: () => mul(sub(scalar(1), square(y)), dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const tileGradConfig = {
-  kernelName: Tile,
-  inputsToSave: ["x"],
-  gradFunc: (dy, saved, attrs) => {
-    const [x] = saved;
-    const {reps} = attrs;
-    const derX = () => {
-      let xGrad = zerosLike(x);
-      if (x.rank === 1) {
-        for (let i = 0; i < reps[0]; ++i) {
-          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
-        }
-      } else if (x.rank === 2) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
-              x.shape[0],
-              x.shape[1]
-            ]));
-          }
-        }
-      } else if (x.rank === 3) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
-            }
-          }
-        }
-      } else if (x.rank === 4) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              for (let l = 0; l < reps[3]; ++l) {
-                xGrad = add2(xGrad, slice(dy, [
-                  i * x.shape[0],
-                  j * x.shape[1],
-                  k * x.shape[2],
-                  l * x.shape[3]
-                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
-              }
-            }
-          }
-        }
-      } else {
-        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
-      }
-      return xGrad;
-    };
-    return {x: derX};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const transposeGradConfig = {
-  kernelName: Transpose,
-  gradFunc: (dy, saved, attrs) => {
-    const transposeAttrs = attrs;
-    const {perm} = transposeAttrs;
-    const undoPerm = getUndoAxesPermutation(perm);
-    return {x: () => transpose(dy, undoPerm)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unpackGradConfig = {
-  kernelName: Unpack,
-  gradFunc: (dy, saved, attrs) => {
-    const unpackAttrs = attrs;
-    const {axis} = unpackAttrs;
-    return {value: () => stack(dy, axis)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const unsortedSegmentSumGradConfig = {
-  kernelName: UnsortedSegmentSum,
-  inputsToSave: ["segmentIds"],
-  gradFunc: (dy, saved) => {
-    const [segmentIds] = saved;
-    const derX = () => {
-      return gatherDropNegatives(dy, segmentIds);
-    };
-    return {x: derX};
-  }
-};
-function gatherDropNegatives(x, indices) {
-  const zeroClippedIndices = maximum(indices, zerosLike(indices));
-  const gathered = gather(x, zeroClippedIndices);
-  let isPositive = greaterEqual(indices, scalar(0, "int32"));
-  const numIters = gathered.rank - isPositive.rank;
-  for (let i = 0; i < numIters; ++i) {
-    isPositive = expandDims(isPositive, i + 1);
-  }
-  isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
-  const zeroSlice = zerosLike(gathered);
-  return where(isPositive, gathered, zeroSlice);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const zerosLikeGradConfig = {
-  kernelName: ZerosLike,
-  gradFunc: (dy) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
-
-// node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const gradConfigs = [
-  absGradConfig,
-  acosGradConfig,
-  acoshGradConfig,
-  addGradConfig,
-  addNGradConfig,
-  argMaxGradConfig,
-  argMinGradConfig,
-  asinGradConfig,
-  asinhGradConfig,
-  atan2GradConfig,
-  atanGradConfig,
-  atanhGradConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulGradConfig,
-  batchToSpaceNDGradConfig,
-  broadcastToGradConfig,
-  castGradConfig,
-  ceilGradConfig,
-  clipByValueGradConfig,
-  concatGradConfig,
-  conv2DBackpropInputGradConfig,
-  conv2DGradConfig,
-  conv3DGradConfig,
-  cosGradConfig,
-  coshGradConfig,
-  cumsumGradConfig,
-  depthwiseConv2dNativeGradConfig,
-  dilation2dGradConfig,
-  divGradConfig,
-  eluGradConfig,
-  erfGradConfig,
-  expGradConfig,
-  expm1GradConfig,
-  floorDivGradConfig,
-  floorGradConfig,
-  fusedBatchNormGradConfig,
-  gatherGradConfig,
-  greaterEqualGradConfig,
-  identityGradConfig,
-  isFiniteGradConfig,
-  isInfGradConfig,
-  isNanGradConfig,
-  log1pGradConfig,
-  logGradConfig,
-  logSoftmaxGradConfig,
-  lrnGradConfig,
-  maxGradConfig,
-  maxGradConfig,
-  maximumGradConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  minGradConfig,
-  minimumGradConfig,
-  modGradConfig,
-  multiplyGradConfig,
-  negateGradConfig,
-  oneHotGradConfig,
-  onesLikeGradConfig,
-  padV2GradConfig,
-  padV2GradConfig,
-  powGradConfig,
-  preluGradConfig,
-  reciprocalGradConfig,
-  relu6GradConfig,
-  reluGradConfig,
-  reshapeGradConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborGradConfig,
-  reverseGradConfig,
-  roundGradConfig,
-  rsqrtGradConfig,
-  selectV2PoolGradConfig,
-  seluGradConfig,
-  sigmoidGradConfig,
-  signGradConfig,
-  sinGradConfig,
-  sinhGradConfig,
-  sliceGradConfig,
-  softmaxGradConfig,
-  softplusGradConfig,
-  spaceToBatchNDGradConfig,
-  spaceToBatchNDGradConfig,
-  splitVGradConfig,
-  splitVGradConfig,
-  sqrtGradConfig,
-  squaredDifferenceGradConfig,
-  squareGradConfig,
-  stepGradConfig,
-  subGradConfig,
-  sumGradConfig,
-  tanGradConfig,
-  tanhGradConfig,
-  tileGradConfig,
-  transposeGradConfig,
-  unpackGradConfig,
-  unsortedSegmentSumGradConfig,
-  zerosLikeGradConfig
-];
-for (const gradientConfig of gradConfigs) {
-  registerGradient(gradientConfig);
-}
-
-// node_modules/@tensorflow/tfjs-core/dist/index.js
-const dist_exports = {};
-__export(dist_exports, {
-  Abs: () => Abs,
-  Acos: () => Acos,
-  Acosh: () => Acosh,
-  AdadeltaOptimizer: () => AdadeltaOptimizer,
-  AdagradOptimizer: () => AdagradOptimizer,
-  AdamOptimizer: () => AdamOptimizer,
-  AdamaxOptimizer: () => AdamaxOptimizer,
-  Add: () => Add,
-  AddN: () => AddN,
-  All: () => All,
-  Any: () => Any,
-  ArgMax: () => ArgMax,
-  ArgMin: () => ArgMin,
-  Asin: () => Asin,
-  Asinh: () => Asinh,
-  Atan: () => Atan,
-  Atan2: () => Atan2,
-  Atanh: () => Atanh,
-  AvgPool: () => AvgPool,
-  AvgPool3D: () => AvgPool3D,
-  AvgPool3DBackprop: () => AvgPool3DBackprop,
-  AvgPoolBackprop: () => AvgPoolBackprop,
-  BatchMatMul: () => BatchMatMul,
-  BatchToSpaceND: () => BatchToSpaceND,
-  BroadcastTo: () => BroadcastTo,
-  Cast: () => Cast,
-  Ceil: () => Ceil,
-  ClipByValue: () => ClipByValue,
-  Complex: () => Complex,
-  Concat: () => Concat,
-  Conv2D: () => Conv2D,
-  Conv2DBackpropFilter: () => Conv2DBackpropFilter,
-  Conv2DBackpropInput: () => Conv2DBackpropInput,
-  Conv3D: () => Conv3D,
-  Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
-  Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
-  Cos: () => Cos,
-  Cosh: () => Cosh,
-  CropAndResize: () => CropAndResize,
-  Cumsum: () => Cumsum,
-  DataStorage: () => DataStorage,
-  DepthToSpace: () => DepthToSpace,
-  DepthwiseConv2dNative: () => DepthwiseConv2dNative,
-  DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
-  DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
-  Diag: () => Diag,
-  Dilation2D: () => Dilation2D,
-  Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
-  Dilation2DBackpropInput: () => Dilation2DBackpropInput,
-  Div: () => Div,
-  ENV: () => ENV,
-  Elu: () => Elu,
-  EluGrad: () => EluGrad,
-  Environment: () => Environment,
-  Equal: () => Equal,
-  Erf: () => Erf,
-  Exp: () => Exp,
-  Expm1: () => Expm1,
-  FFT: () => FFT,
-  Fill: () => Fill,
-  FlipLeftRight: () => FlipLeftRight,
-  Floor: () => Floor,
-  FloorDiv: () => FloorDiv,
-  FromPixels: () => FromPixels,
-  FusedBatchNorm: () => FusedBatchNorm,
-  FusedConv2D: () => FusedConv2D,
-  FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
-  GatherNd: () => GatherNd,
-  GatherV2: () => GatherV2,
-  Greater: () => Greater,
-  GreaterEqual: () => GreaterEqual,
-  IFFT: () => IFFT,
-  Identity: () => Identity,
-  Imag: () => Imag,
-  IsFinite: () => IsFinite,
-  IsInf: () => IsInf,
-  IsNan: () => IsNan,
-  KernelBackend: () => KernelBackend,
-  LRN: () => LRN,
-  LRNBackprop: () => LRNBackprop,
-  Less: () => Less,
-  LessEqual: () => LessEqual,
-  LinSpace: () => LinSpace,
-  Log: () => Log,
-  Log1p: () => Log1p,
-  LogSoftmax: () => LogSoftmax,
-  LogicalAnd: () => LogicalAnd,
-  LogicalNot: () => LogicalNot,
-  LogicalOr: () => LogicalOr,
-  Max: () => Max,
-  MaxPool: () => MaxPool,
-  MaxPool3D: () => MaxPool3D,
-  MaxPool3DBackprop: () => MaxPool3DBackprop,
-  MaxPoolBackprop: () => MaxPoolBackprop,
-  MaxPoolWithArgmax: () => MaxPoolWithArgmax,
-  Maximum: () => Maximum,
-  Mean: () => Mean,
-  Min: () => Min,
-  Minimum: () => Minimum,
-  Mod: () => Mod,
-  MomentumOptimizer: () => MomentumOptimizer,
-  Multiply: () => Multiply,
-  Negate: () => Negate,
-  NonMaxSuppressionV3: () => NonMaxSuppressionV3,
-  NonMaxSuppressionV4: () => NonMaxSuppressionV4,
-  NonMaxSuppressionV5: () => NonMaxSuppressionV5,
-  NotEqual: () => NotEqual,
-  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
-  OneHot: () => OneHot,
-  OnesLike: () => OnesLike,
-  Optimizer: () => Optimizer,
-  PadV2: () => PadV2,
-  Pool: () => Pool,
-  Pow: () => Pow,
-  Prelu: () => Prelu,
-  Prod: () => Prod,
-  RMSPropOptimizer: () => RMSPropOptimizer,
-  Range: () => Range,
-  Rank: () => Rank,
-  Real: () => Real,
-  Reciprocal: () => Reciprocal,
-  Reduction: () => Reduction,
-  Relu: () => Relu,
-  Relu6: () => Relu6,
-  Reshape: () => Reshape,
-  ResizeBilinear: () => ResizeBilinear,
-  ResizeBilinearGrad: () => ResizeBilinearGrad,
-  ResizeNearestNeighbor: () => ResizeNearestNeighbor,
-  ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
-  Reverse: () => Reverse,
-  RotateWithOffset: () => RotateWithOffset,
-  Round: () => Round,
-  Rsqrt: () => Rsqrt,
-  SGDOptimizer: () => SGDOptimizer,
-  ScatterNd: () => ScatterNd,
-  SelectV2: () => SelectV2,
-  Selu: () => Selu,
-  Sigmoid: () => Sigmoid,
-  Sign: () => Sign,
-  Sin: () => Sin,
-  Sinh: () => Sinh,
-  Slice: () => Slice,
-  Softmax: () => Softmax,
-  Softplus: () => Softplus,
-  SpaceToBatchND: () => SpaceToBatchND,
-  SparseToDense: () => SparseToDense,
-  SplitV: () => SplitV,
-  Sqrt: () => Sqrt,
-  Square: () => Square,
-  SquaredDifference: () => SquaredDifference,
-  Step: () => Step,
-  StridedSlice: () => StridedSlice,
-  Sub: () => Sub,
-  Sum: () => Sum,
-  Tan: () => Tan,
-  Tanh: () => Tanh,
-  Tensor: () => Tensor,
-  TensorBuffer: () => TensorBuffer,
-  Tile: () => Tile,
-  TopK: () => TopK,
-  Transpose: () => Transpose,
-  Unpack: () => Unpack,
-  UnsortedSegmentSum: () => UnsortedSegmentSum,
-  Variable: () => Variable,
-  ZerosLike: () => ZerosLike,
-  _FusedMatMul: () => _FusedMatMul,
-  abs: () => abs,
-  acos: () => acos,
-  acosh: () => acosh,
-  add: () => add2,
-  addN: () => addN,
-  addStrict: () => addStrict,
-  all: () => all,
-  any: () => any,
-  argMax: () => argMax,
-  argMin: () => argMin,
-  asin: () => asin,
-  asinh: () => asinh,
-  atan: () => atan,
-  atan2: () => atan2,
-  atanh: () => atanh,
-  avgPool: () => avgPool,
-  avgPool3d: () => avgPool3d,
-  backend: () => backend,
-  backend_util: () => backend_util_exports,
-  basicLSTMCell: () => basicLSTMCell,
-  batchNorm: () => batchNorm,
-  batchNorm2d: () => batchNorm2d,
-  batchNorm3d: () => batchNorm3d,
-  batchNorm4d: () => batchNorm4d,
-  batchToSpaceND: () => batchToSpaceND,
-  booleanMaskAsync: () => booleanMaskAsync,
-  broadcastTo: () => broadcastTo,
-  browser: () => browser_exports,
-  buffer: () => buffer,
-  cast: () => cast,
-  ceil: () => ceil,
-  clipByValue: () => clipByValue,
-  clone: () => clone,
-  complex: () => complex,
-  concat: () => concat,
-  concat1d: () => concat1d,
-  concat2d: () => concat2d,
-  concat3d: () => concat3d,
-  concat4d: () => concat4d,
-  conv1d: () => conv1d,
-  conv2d: () => conv2d,
-  conv2dTranspose: () => conv2dTranspose,
-  conv3d: () => conv3d,
-  conv3dTranspose: () => conv3dTranspose,
-  cos: () => cos,
-  cosh: () => cosh,
-  cosineWindow: () => cosineWindow,
-  cumsum: () => cumsum,
-  customGrad: () => customGrad,
-  deprecationWarn: () => deprecationWarn,
-  depthToSpace: () => depthToSpace,
-  depthwiseConv2d: () => depthwiseConv2d,
-  device_util: () => device_util_exports,
-  diag: () => diag,
-  dilation2d: () => dilation2d,
-  disableDeprecationWarnings: () => disableDeprecationWarnings,
-  dispose: () => dispose,
-  disposeVariables: () => disposeVariables,
-  div: () => div,
-  divNoNan: () => divNoNan,
-  divStrict: () => divStrict,
-  dot: () => dot,
-  dropout: () => dropout,
-  elu: () => elu,
-  enableDebugMode: () => enableDebugMode,
-  enableProdMode: () => enableProdMode,
-  enclosingPowerOfTwo: () => enclosingPowerOfTwo,
-  engine: () => engine15,
-  env: () => env,
-  equal: () => equal,
-  equalStrict: () => equalStrict,
-  erf: () => erf,
-  exp: () => exp,
-  expandDims: () => expandDims,
-  expm1: () => expm1,
-  eye: () => eye,
-  fft: () => fft,
-  fill: () => fill,
-  findBackend: () => findBackend,
-  findBackendFactory: () => findBackendFactory,
-  floor: () => floor,
-  floorDiv: () => floorDiv,
-  fused: () => fused_ops_exports,
-  gather: () => gather,
-  gatherND: () => gatherND,
-  gather_util: () => gather_nd_util_exports,
-  getBackend: () => getBackend,
-  getGradient: () => getGradient,
-  getKernel: () => getKernel,
-  getKernelsForBackend: () => getKernelsForBackend,
-  grad: () => grad,
-  grads: () => grads,
-  greater: () => greater,
-  greaterEqual: () => greaterEqual,
-  greaterEqualStrict: () => greaterEqualStrict,
-  greaterStrict: () => greaterStrict,
-  ifft: () => ifft,
-  imag: () => imag,
-  image: () => image,
-  inTopKAsync: () => inTopKAsync,
-  io: () => io_exports,
-  irfft: () => irfft,
-  isFinite: () => isFinite2,
-  isInf: () => isInf,
-  isNaN: () => isNaN2,
-  keep: () => keep,
-  kernel_impls: () => kernel_impls_exports,
-  leakyRelu: () => leakyRelu,
-  less: () => less,
-  lessEqual: () => lessEqual,
-  lessEqualStrict: () => lessEqualStrict,
-  lessStrict: () => lessStrict,
-  linalg: () => linalg,
-  linspace: () => linspace,
-  localResponseNormalization: () => localResponseNormalization,
-  log: () => log,
-  log1p: () => log1p,
-  logSigmoid: () => logSigmoid,
-  logSoftmax: () => logSoftmax,
-  logSumExp: () => logSumExp,
-  logicalAnd: () => logicalAnd,
-  logicalNot: () => logicalNot,
-  logicalOr: () => logicalOr,
-  logicalXor: () => logicalXor,
-  losses: () => losses,
-  matMul: () => matMul,
-  math: () => math_exports,
-  max: () => max,
-  maxPool: () => maxPool,
-  maxPool3d: () => maxPool3d,
-  maxPoolWithArgmax: () => maxPoolWithArgmax,
-  maximum: () => maximum,
-  maximumStrict: () => maximumStrict,
-  mean: () => mean,
-  memory: () => memory,
-  min: () => min,
-  minimum: () => minimum,
-  minimumStrict: () => minimumStrict,
-  mod: () => mod,
-  modStrict: () => modStrict,
-  moments: () => moments,
-  movingAverage: () => movingAverage,
-  mul: () => mul,
-  mulStrict: () => mulStrict,
-  multiRNNCell: () => multiRNNCell,
-  multinomial: () => multinomial,
-  neg: () => neg,
-  nextFrame: () => nextFrame,
-  norm: () => norm,
-  notEqual: () => notEqual,
-  notEqualStrict: () => notEqualStrict,
-  oneHot: () => oneHot,
-  ones: () => ones2,
-  onesLike: () => onesLike,
-  op: () => op,
-  outerProduct: () => outerProduct,
-  pad: () => pad,
-  pad1d: () => pad1d,
-  pad2d: () => pad2d,
-  pad3d: () => pad3d,
-  pad4d: () => pad4d,
-  pool: () => pool,
-  pow: () => pow,
-  powStrict: () => powStrict,
-  prelu: () => prelu,
-  print: () => print,
-  prod: () => prod,
-  profile: () => profile,
-  rand: () => rand,
-  randomGamma: () => randomGamma,
-  randomNormal: () => randomNormal,
-  randomUniform: () => randomUniform,
-  range: () => range,
-  ready: () => ready,
-  real: () => real,
-  reciprocal: () => reciprocal,
-  registerBackend: () => registerBackend,
-  registerGradient: () => registerGradient,
-  registerKernel: () => registerKernel,
-  relu: () => relu,
-  relu6: () => relu6,
-  removeBackend: () => removeBackend,
-  reshape: () => reshape,
-  reverse: () => reverse,
-  reverse1d: () => reverse1d,
-  reverse2d: () => reverse2d,
-  reverse3d: () => reverse3d,
-  reverse4d: () => reverse4d,
-  rfft: () => rfft,
-  round: () => round,
-  rsqrt: () => rsqrt,
-  scalar: () => scalar,
-  scatterND: () => scatterND,
-  scatter_util: () => scatter_nd_util_exports,
-  selu: () => selu,
-  separableConv2d: () => separableConv2d,
-  serialization: () => serialization_exports,
-  setBackend: () => setBackend,
-  setPlatform: () => setPlatform,
-  setdiff1dAsync: () => setdiff1dAsync,
-  sigmoid: () => sigmoid,
-  sign: () => sign,
-  signal: () => signal,
-  sin: () => sin,
-  sinh: () => sinh,
-  slice: () => slice,
-  slice1d: () => slice1d,
-  slice2d: () => slice2d,
-  slice3d: () => slice3d,
-  slice4d: () => slice4d,
-  slice_util: () => slice_util_exports,
-  softmax: () => softmax,
-  softplus: () => softplus,
-  spaceToBatchND: () => spaceToBatchND,
-  sparseToDense: () => sparseToDense,
-  spectral: () => spectral,
-  split: () => split,
-  sqrt: () => sqrt,
-  square: () => square,
-  squaredDifference: () => squaredDifference,
-  squaredDifferenceStrict: () => squaredDifferenceStrict,
-  squeeze: () => squeeze,
-  stack: () => stack,
-  step: () => step,
-  stridedSlice: () => stridedSlice,
-  sub: () => sub,
-  subStrict: () => subStrict,
-  sum: () => sum2,
-  sumOutType: () => sumOutType,
-  tan: () => tan,
-  tanh: () => tanh2,
-  tensor: () => tensor4,
-  tensor1d: () => tensor1d,
-  tensor2d: () => tensor2d,
-  tensor3d: () => tensor3d,
-  tensor4d: () => tensor4d,
-  tensor5d: () => tensor5d,
-  tensor6d: () => tensor6d,
-  tensor_util: () => tensor_util_exports,
-  test_util: () => test_util_exports,
-  tidy: () => tidy,
-  tile: () => tile,
-  time: () => time,
-  topk: () => topk,
-  train: () => train,
-  transpose: () => transpose,
-  truncatedNormal: () => truncatedNormal,
-  unregisterGradient: () => unregisterGradient,
-  unregisterKernel: () => unregisterKernel,
-  unsortedSegmentSum: () => unsortedSegmentSum,
-  unstack: () => unstack,
-  upcastType: () => upcastType,
-  util: () => util_exports,
-  valueAndGrad: () => valueAndGrad,
-  valueAndGrads: () => valueAndGrads,
-  variable: () => variable,
-  variableGrads: () => variableGrads,
-  version_core: () => version,
-  where: () => where,
-  whereAsync: () => whereAsync,
-  zeros: () => zeros,
-  zerosLike: () => zerosLike
-});
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
 // build/src/draw/drawContour.js
 function drawContour(ctx, points, isClosed = false) {
   ctx.beginPath();
@@ -19983,23 +95,26 @@ __export(utils_exports, {
   isTensor4D: () => isTensor4D,
   isValidNumber: () => isValidNumber,
   isValidProbablitiy: () => isValidProbablitiy,
-  range: () => range4,
-  round: () => round3
+  range: () => range,
+  round: () => round
 });
-function isTensor(tensor17, dim) {
-  return tensor17 instanceof Tensor && tensor17.shape.length === dim;
+import {
+  Tensor
+} from "@tensorflow/tfjs";
+function isTensor(tensor2, dim) {
+  return tensor2 instanceof Tensor && tensor2.shape.length === dim;
 }
-function isTensor1D(tensor17) {
-  return isTensor(tensor17, 1);
+function isTensor1D(tensor2) {
+  return isTensor(tensor2, 1);
 }
-function isTensor2D(tensor17) {
-  return isTensor(tensor17, 2);
+function isTensor2D(tensor2) {
+  return isTensor(tensor2, 2);
 }
-function isTensor3D(tensor17) {
-  return isTensor(tensor17, 3);
+function isTensor3D(tensor2) {
+  return isTensor(tensor2, 3);
 }
-function isTensor4D(tensor17) {
-  return isTensor(tensor17, 4);
+function isTensor4D(tensor2) {
+  return isTensor(tensor2, 4);
 }
 function isFloat(num) {
   return num % 1 !== 0;
@@ -20007,7 +122,7 @@ function isFloat(num) {
 function isEven(num) {
   return num % 2 === 0;
 }
-function round3(num, prec = 2) {
+function round(num, prec = 2) {
   const f = Math.pow(10, prec);
   return Math.floor(num * f) / f;
 }
@@ -20019,10 +134,10 @@ function computeReshapedDimensions({width, height}, inputSize) {
   return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
 }
 function getCenterPoint(pts) {
-  return pts.reduce((sum26, pt) => sum26.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
+  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
 }
-function range4(num, start, step7) {
-  return Array(num).fill(0).map((_, i) => start + i * step7);
+function range(num, start, step) {
+  return Array(num).fill(0).map((_, i) => start + i * step);
 }
 function isValidNumber(num) {
   return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
@@ -20295,15 +410,15 @@ function iou(box1, box2, isIOU = true) {
 function minBbox(pts) {
   const xs = pts.map((pt) => pt.x);
   const ys = pts.map((pt) => pt.y);
-  const minX = xs.reduce((min4, x) => x < min4 ? x : min4, Infinity);
-  const minY = ys.reduce((min4, y) => y < min4 ? y : min4, Infinity);
-  const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0);
-  const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0);
+  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity);
+  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity);
+  const maxX = xs.reduce((max, x) => max < x ? x : max, 0);
+  const maxY = ys.reduce((max, y) => max < y ? y : max, 0);
   return new BoundingBox(minX, minY, maxX, maxY);
 }
 
 // build/src/ops/nonMaxSuppression.js
-function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
+function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) {
   let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex);
   const pick = [];
   while (indicesSortedByScore.length > 0) {
@@ -20323,6 +438,12 @@ function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
 }
 
 // build/src/ops/normalize.js
+import {
+  concat,
+  fill,
+  sub,
+  tidy
+} from "@tensorflow/tfjs";
 function normalize(x, meanRgb) {
   return tidy(() => {
     const [r, g, b] = meanRgb;
@@ -20335,8 +456,14 @@ function normalize(x, meanRgb) {
 }
 
 // build/src/ops/padToSquare.js
+import {
+  cast,
+  concat as concat2,
+  fill as fill2,
+  tidy as tidy2
+} from "@tensorflow/tfjs";
 function padToSquare(imgTensor, isCenterImage = false) {
-  return tidy(() => {
+  return tidy2(() => {
     const [height, width] = imgTensor.shape.slice(1);
     if (height === width) {
       return imgTensor;
@@ -20347,7 +474,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
     const createPaddingTensor = (paddingAmount2) => {
       const paddingTensorShape = imgTensor.shape.slice();
       paddingTensorShape[paddingAxis] = paddingAmount2;
-      return fill(paddingTensorShape, 0);
+      return fill2(paddingTensorShape, 0);
     };
     const paddingTensorAppend = createPaddingTensor(paddingAmount);
     const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -20357,7 +484,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
       imgTensor,
       paddingTensorAppend
     ].filter((t) => !!t).map((t) => cast(t, "float32"));
-    return concat(tensorsToStack, paddingAxis);
+    return concat2(tensorsToStack, paddingAxis);
   });
 }
 
@@ -20374,7 +501,7 @@ function shuffleArray(inputArray) {
 }
 
 // build/src/ops/index.js
-function sigmoid6(x) {
+function sigmoid(x) {
   return 1 / (1 + Math.exp(-x));
 }
 function inverseSigmoid(x) {
@@ -20511,7 +638,7 @@ class FaceMatch {
     return this._distance;
   }
   toString(withDistance = true) {
-    return `${this.label}${withDistance ? ` (${round3(this.distance)})` : ""}`;
+    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
   }
 }
 
@@ -20598,7 +725,7 @@ function extendWithFaceDetection(sourceObj, detection) {
 
 // build/src/env/createBrowserEnv.js
 function createBrowserEnv() {
-  const fetch3 = window["fetch"] || function() {
+  const fetch = window["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for browser environment");
   };
   const readFile = function() {
@@ -20612,7 +739,7 @@ function createBrowserEnv() {
     Video: HTMLVideoElement,
     createCanvasElement: () => document.createElement("canvas"),
     createImageElement: () => document.createElement("img"),
-    fetch: fetch3,
+    fetch,
     readFile
   };
 }
@@ -20629,8 +756,8 @@ function createFileSystem(fs) {
   }
   const readFile = fs ? function(filePath) {
     return new Promise((res, rej) => {
-      fs.readFile(filePath, function(err, buffer11) {
-        return err ? rej(err) : res(buffer11);
+      fs.readFile(filePath, function(err, buffer) {
+        return err ? rej(err) : res(buffer);
       });
     });
   } : function() {
@@ -20657,11 +784,11 @@ function createNodejsEnv() {
     }
     throw new Error("createImageElement - missing Image implementation for nodejs environment");
   };
-  const fetch3 = global["fetch"] || function() {
+  const fetch = global["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for nodejs environment");
   };
   const fileSystem = createFileSystem();
-  return __assign({
+  return {
     Canvas: Canvas || class {
     },
     CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
@@ -20674,12 +801,13 @@ function createNodejsEnv() {
     },
     createCanvasElement,
     createImageElement,
-    fetch: fetch3
-  }, fileSystem);
+    fetch,
+    ...fileSystem
+  };
 }
 
 // build/src/env/isBrowser.js
-function isBrowser2() {
+function isBrowser() {
   return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
 }
 
@@ -20687,42 +815,42 @@ function isBrowser2() {
 
 // build/src/env/index.js
 const isNodejs = __toModule(require_isNodejs());
-let environment11;
+let environment;
 function getEnv() {
-  if (!environment11) {
+  if (!environment) {
     throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
   }
-  return environment11;
+  return environment;
 }
-function setEnv(env17) {
-  environment11 = env17;
+function setEnv(env16) {
+  environment = env16;
 }
 function initialize() {
-  if (isBrowser2()) {
+  if (isBrowser()) {
     return setEnv(createBrowserEnv());
   }
   if (isNodejs.isNodejs()) {
     return setEnv(createNodejsEnv());
   }
 }
-function monkeyPatch(env17) {
-  if (!environment11) {
+function monkeyPatch(env16) {
+  if (!environment) {
     initialize();
   }
-  if (!environment11) {
+  if (!environment) {
     throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
   }
-  const {Canvas = environment11.Canvas, Image = environment11.Image} = env17;
-  environment11.Canvas = Canvas;
-  environment11.Image = Image;
-  environment11.createCanvasElement = env17.createCanvasElement || (() => new Canvas());
-  environment11.createImageElement = env17.createImageElement || (() => new Image());
-  environment11.ImageData = env17.ImageData || environment11.ImageData;
-  environment11.Video = env17.Video || environment11.Video;
-  environment11.fetch = env17.fetch || environment11.fetch;
-  environment11.readFile = env17.readFile || environment11.readFile;
+  const {Canvas = environment.Canvas, Image = environment.Image} = env16;
+  environment.Canvas = Canvas;
+  environment.Image = Image;
+  environment.createCanvasElement = env16.createCanvasElement || (() => new Canvas());
+  environment.createImageElement = env16.createImageElement || (() => new Image());
+  environment.ImageData = env16.ImageData || environment.ImageData;
+  environment.Video = env16.Video || environment.Video;
+  environment.fetch = env16.fetch || environment.fetch;
+  environment.readFile = env16.readFile || environment.readFile;
 }
-const env2 = {
+const env = {
   getEnv,
   setEnv,
   initialize,
@@ -20730,14 +858,14 @@ const env2 = {
   createFileSystem,
   createNodejsEnv,
   monkeyPatch,
-  isBrowser: isBrowser2,
+  isBrowser,
   isNodejs: isNodejs.isNodejs
 };
 initialize();
 
 // build/src/dom/resolveInput.js
 function resolveInput(arg) {
-  if (!env2.isNodejs() && typeof arg === "string") {
+  if (!env.isNodejs() && typeof arg === "string") {
     return document.getElementById(arg);
   }
   return arg;
@@ -20745,7 +873,7 @@ function resolveInput(arg) {
 
 // build/src/dom/getContext2dOrThrow.js
 function getContext2dOrThrow(canvasArg) {
-  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env2.getEnv();
+  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
   if (canvasArg instanceof CanvasRenderingContext2D2) {
     return canvasArg;
   }
@@ -20867,21 +995,21 @@ function drawDetections(canvasArg, detections) {
   detectionsArray.forEach((det) => {
     const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0;
     const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det);
-    const label = score ? `${round3(score)}` : void 0;
+    const label = score ? `${round(score)}` : void 0;
     new DrawBox(box, {label}).draw(canvasArg);
   });
 }
 
 // build/src/dom/isMediaLoaded.js
 function isMediaLoaded(media) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
 }
 
 // build/src/dom/awaitMediaLoaded.js
 function awaitMediaLoaded(media) {
   return new Promise((resolve, reject) => {
-    if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media)) {
+    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
       return resolve(null);
     }
     function onLoad(e) {
@@ -20914,7 +1042,7 @@ function bufferToImage(buf) {
       if (typeof reader.result !== "string") {
         return reject("bufferToImage - expected reader.result to be a string, in onload");
       }
-      const img = env2.getEnv().createImageElement();
+      const img = env.getEnv().createImageElement();
       img.onload = () => resolve(img);
       img.onerror = reject;
       img.src = reader.result;
@@ -20926,7 +1054,7 @@ function bufferToImage(buf) {
 
 // build/src/dom/getMediaDimensions.js
 function getMediaDimensions(input) {
-  const {Image, Video} = env2.getEnv();
+  const {Image, Video} = env.getEnv();
   if (input instanceof Image) {
     return new Dimensions(input.naturalWidth, input.naturalHeight);
   }
@@ -20938,14 +1066,14 @@ function getMediaDimensions(input) {
 
 // build/src/dom/createCanvas.js
 function createCanvas({width, height}) {
-  const {createCanvasElement} = env2.getEnv();
+  const {createCanvasElement} = env.getEnv();
   const canvas = createCanvasElement();
   canvas.width = width;
   canvas.height = height;
   return canvas;
 }
 function createCanvasFromMedia(media, dims) {
-  const {ImageData: ImageData2} = env2.getEnv();
+  const {ImageData: ImageData2} = env.getEnv();
   if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
     throw new Error("createCanvasFromMedia - media has not finished loading yet");
   }
@@ -20960,24 +1088,28 @@ function createCanvasFromMedia(media, dims) {
 }
 
 // build/src/dom/imageTensorToCanvas.js
+import {
+  browser,
+  tidy as tidy3
+} from "@tensorflow/tfjs";
 async function imageTensorToCanvas(imgTensor, canvas) {
-  const targetCanvas = canvas || env2.getEnv().createCanvasElement();
+  const targetCanvas = canvas || env.getEnv().createCanvasElement();
   const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-  const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-  await browser_exports.toPixels(imgTensor3D, targetCanvas);
+  const imgTensor3D = tidy3(() => imgTensor.as3D(height, width, numChannels).toInt());
+  await browser.toPixels(imgTensor3D, targetCanvas);
   imgTensor3D.dispose();
   return targetCanvas;
 }
 
 // build/src/dom/isMediaElement.js
 function isMediaElement(input) {
-  const {Image, Canvas, Video} = env2.getEnv();
+  const {Image, Canvas, Video} = env.getEnv();
   return input instanceof Image || input instanceof Canvas || input instanceof Video;
 }
 
 // build/src/dom/imageToSquare.js
 function imageToSquare(input, inputSize, centerImage = false) {
-  const {Image, Canvas} = env2.getEnv();
+  const {Image, Canvas} = env.getEnv();
   if (!(input instanceof Image || input instanceof Canvas)) {
     throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
   }
@@ -20995,6 +1127,14 @@ function imageToSquare(input, inputSize, centerImage = false) {
 }
 
 // build/src/dom/NetInput.js
+import {
+  Tensor as Tensor2,
+  browser as browser2,
+  cast as cast2,
+  image,
+  stack,
+  tidy as tidy4
+} from "@tensorflow/tfjs";
 class NetInput {
   constructor(inputs, treatAsBatchInput = false) {
     this._imageTensors = [];
@@ -21021,7 +1161,7 @@ class NetInput {
         this._inputDimensions[idx] = input.shape.slice(1);
         return;
       }
-      const canvas = input instanceof env2.getEnv().Canvas ? input : createCanvasFromMedia(input);
+      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
       this._canvases[idx] = canvas;
       this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
     });
@@ -21045,7 +1185,7 @@ class NetInput {
     return this._inputSize;
   }
   get reshapedInputDimensions() {
-    return range4(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
+    return range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
   }
   getInput(batchIdx) {
     return this.canvases[batchIdx] || this.imageTensors[batchIdx];
@@ -21069,10 +1209,10 @@ class NetInput {
   }
   toBatchTensor(inputSize, isCenterInputs = true) {
     this._inputSize = inputSize;
-    return tidy(() => {
-      const inputTensors = range4(this.batchSize, 0, 1).map((batchIdx) => {
+    return tidy4(() => {
+      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
         const input = this.getInput(batchIdx);
-        if (input instanceof Tensor) {
+        if (input instanceof Tensor2) {
           let imgTensor = isTensor4D(input) ? input : input.expandDims();
           imgTensor = padToSquare(imgTensor, isCenterInputs);
           if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
@@ -21080,12 +1220,12 @@ class NetInput {
           }
           return imgTensor.as3D(inputSize, inputSize, 3);
         }
-        if (input instanceof env2.getEnv().Canvas) {
-          return browser_exports.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+        if (input instanceof env.getEnv().Canvas) {
+          return browser2.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
         }
         throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
       });
-      const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
+      const batchTensor = stack(inputTensors.map((t) => cast2(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
       return batchTensor;
     });
   }
@@ -21122,7 +1262,7 @@ async function toNetInput(inputs) {
 
 // build/src/dom/extractFaces.js
 async function extractFaces(input, detections) {
-  const {Canvas} = env2.getEnv();
+  const {Canvas} = env.getEnv();
   let canvas = input;
   if (!(input instanceof Canvas)) {
     const netInput = await toNetInput(input);
@@ -21142,6 +1282,10 @@ async function extractFaces(input, detections) {
 }
 
 // build/src/dom/extractFaceTensors.js
+import {
+  slice3d,
+  tidy as tidy5
+} from "@tensorflow/tfjs";
 async function extractFaceTensors(imageTensor, detections) {
   if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
     throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");
@@ -21149,7 +1293,7 @@ async function extractFaceTensors(imageTensor, detections) {
   if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
     throw new Error("extractFaceTensors - batchSize > 1 not supported");
   }
-  return tidy(() => {
+  return tidy5(() => {
     const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);
     const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight));
     const faceTensors = boxes.map(({x, y, width, height}) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
@@ -21159,8 +1303,8 @@ async function extractFaceTensors(imageTensor, detections) {
 
 // build/src/dom/fetchOrThrow.js
 async function fetchOrThrow(url, init) {
-  const fetch3 = env2.getEnv().fetch;
-  const res = await fetch3(url, init);
+  const fetch = env.getEnv().fetch;
+  const res = await fetch(url, init);
   if (!(res.status < 400)) {
     throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
   }
@@ -21215,10 +1359,13 @@ function getModelUris(uri, defaultModelName) {
 }
 
 // build/src/dom/loadWeightMap.js
+import {
+  io
+} from "@tensorflow/tfjs";
 async function loadWeightMap(uri, defaultModelName) {
   const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
   let manifest = await fetchJson(manifestUri);
-  return io_exports.loadWeights(manifest, modelBaseUri);
+  return io.loadWeights(manifest, modelBaseUri);
 }
 
 // build/src/dom/matchDimensions.js
@@ -21234,6 +1381,12 @@ function matchDimensions(input, reference, useMediaDimensions = false) {
 // build/src/dom/index.js
 
 // build/src/NeuralNetwork.js
+import {
+  Tensor as Tensor3,
+  Variable,
+  io as io2,
+  tensor
+} from "@tensorflow/tfjs";
 class NeuralNetwork {
   constructor(_name) {
     this._name = _name;
@@ -21253,10 +1406,10 @@ class NeuralNetwork {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     return obj[objProp];
   }
-  reassignParamFromPath(paramPath, tensor17) {
+  reassignParamFromPath(paramPath, tensor2) {
     const {obj, objProp} = this.traversePropertyPath(paramPath);
     obj[objProp].dispose();
-    obj[objProp] = tensor17;
+    obj[objProp] = tensor2;
   }
   getParamList() {
     return this._paramMappings.map(({paramPath}) => ({
@@ -21271,15 +1424,15 @@ class NeuralNetwork {
     return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
   }
   variable() {
-    this.getFrozenParams().forEach(({path, tensor: tensor17}) => {
-      this.reassignParamFromPath(path, tensor17.variable());
+    this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
+      this.reassignParamFromPath(path, tensor2.variable());
     });
   }
   freeze() {
-    this.getTrainableParams().forEach(({path, tensor: variable3}) => {
-      const tensor17 = tensor4(variable3.dataSync());
-      variable3.dispose();
-      this.reassignParamFromPath(path, tensor17);
+    this.getTrainableParams().forEach(({path, tensor: variable}) => {
+      const tensor2 = tensor(variable.dataSync());
+      variable.dispose();
+      this.reassignParamFromPath(path, tensor2);
     });
   }
   dispose(throwOnRedispose = true) {
@@ -21292,7 +1445,7 @@ class NeuralNetwork {
     this._params = void 0;
   }
   serializeParams() {
-    return new Float32Array(this.getParamList().map(({tensor: tensor17}) => Array.from(tensor17.dataSync())).reduce((flat, arr) => flat.concat(arr)));
+    return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
   }
   async load(weightsOrUrl) {
     if (weightsOrUrl instanceof Float32Array) {
@@ -21312,12 +1465,12 @@ class NeuralNetwork {
     if (filePath && typeof filePath !== "string") {
       throw new Error(`${this._name}.loadFromDisk - expected model file path`);
     }
-    const {readFile} = env2.getEnv();
+    const {readFile} = env.getEnv();
     const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName());
     const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((filePath2) => readFile(filePath2).then((buf) => buf.buffer)));
-    const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk);
+    const loadWeights = io2.weightsLoaderFactory(fetchWeightsFromDisk);
     const manifest = JSON.parse((await readFile(manifestUri)).toString());
-    const weightMap = await loadWeights2(manifest, modelBaseUri);
+    const weightMap = await loadWeights(manifest, modelBaseUri);
     this.loadFromWeightMap(weightMap);
   }
   loadFromWeightMap(weightMap) {
@@ -21341,7 +1494,7 @@ class NeuralNetwork {
       return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
     }, {nextObj: this.params});
     const {obj, objProp} = result;
-    if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+    if (!obj || !objProp || !(obj[objProp] instanceof Tensor3)) {
       throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
     }
     return {obj, objProp};
@@ -21349,17 +1502,28 @@ class NeuralNetwork {
 }
 
 // build/src/common/depthwiseSeparableConv.js
+import {
+  add,
+  separableConv2d,
+  tidy as tidy6
+} from "@tensorflow/tfjs";
 function depthwiseSeparableConv(x, params, stride) {
-  return tidy(() => {
+  return tidy6(() => {
     let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
-    out = add2(out, params.bias);
+    out = add(out, params.bias);
     return out;
   });
 }
 
 // build/src/faceFeatureExtractor/denseBlock.js
+import {
+  add as add2,
+  conv2d,
+  relu,
+  tidy as tidy7
+} from "@tensorflow/tfjs";
 function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
-  return tidy(() => {
+  return tidy7(() => {
     const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
     const in3 = relu(add2(out1, out2));
@@ -21368,7 +1532,7 @@ function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
   });
 }
 function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-  return tidy(() => {
+  return tidy7(() => {
     const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
     const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
     const in3 = relu(add2(out1, out2));
@@ -21380,10 +1544,16 @@ function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = tr
 }
 
 // build/src/common/convLayer.js
+import {
+  add as add3,
+  conv2d as conv2d2,
+  relu as relu2,
+  tidy as tidy8
+} from "@tensorflow/tfjs";
 function convLayer(x, params, padding = "same", withRelu = false) {
-  return tidy(() => {
-    const out = add2(conv2d(x, params.filters, [1, 1], padding), params.bias);
-    return withRelu ? relu(out) : out;
+  return tidy8(() => {
+    const out = add3(conv2d2(x, params.filters, [1, 1], padding), params.bias);
+    return withRelu ? relu2(out) : out;
   });
 }
 
@@ -21397,6 +1567,10 @@ function disposeUnusedWeightTensors(weightMap, paramMappings) {
 }
 
 // build/src/common/extractConvParamsFactory.js
+import {
+  tensor1d,
+  tensor4d
+} from "@tensorflow/tfjs";
 function extractConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
     const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
@@ -21407,10 +1581,14 @@ function extractConvParamsFactory(extractWeights, paramMappings) {
 }
 
 // build/src/common/extractFCParamsFactory.js
+import {
+  tensor1d as tensor1d2,
+  tensor2d
+} from "@tensorflow/tfjs";
 function extractFCParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
     const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tensor1d(extractWeights(channelsOut));
+    const fc_bias = tensor1d2(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
     return {
       weights: fc_weights,
@@ -21429,11 +1607,15 @@ class SeparableConvParams {
 }
 
 // build/src/common/extractSeparableConvParamsFactory.js
+import {
+  tensor1d as tensor1d3,
+  tensor4d as tensor4d2
+} from "@tensorflow/tfjs";
 function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
   return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const depthwise_filter = tensor4d2(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+    const pointwise_filter = tensor4d2(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+    const bias = tensor1d3(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
     return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
   };
@@ -21450,12 +1632,12 @@ function loadSeparableConvParamsFactory(extractWeightEntry) {
 // build/src/common/extractWeightEntryFactory.js
 function extractWeightEntryFactory(weightMap, paramMappings) {
   return function(originalPath, paramRank, mappedPath) {
-    const tensor17 = weightMap[originalPath];
-    if (!isTensor(tensor17, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor17}`);
+    const tensor2 = weightMap[originalPath];
+    if (!isTensor(tensor2, paramRank)) {
+      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
     }
     paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor17;
+    return tensor2;
   };
 }
 
@@ -21565,6 +1747,11 @@ function extractParamsFromWeigthMap(weightMap) {
 }
 
 // build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+import {
+  avgPool,
+  scalar,
+  tidy as tidy9
+} from "@tensorflow/tfjs";
 class FaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("FaceFeatureExtractor");
@@ -21574,7 +1761,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tidy9(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
@@ -21601,8 +1788,13 @@ class FaceFeatureExtractor extends NeuralNetwork {
 }
 
 // build/src/common/fullyConnectedLayer.js
+import {
+  add as add4,
+  matMul,
+  tidy as tidy10
+} from "@tensorflow/tfjs";
 function fullyConnectedLayer(x, params) {
-  return tidy(() => add2(matMul(x, params.weights), params.bias));
+  return tidy10(() => add4(matMul(x, params.weights), params.bias));
 }
 
 // build/src/faceProcessor/extractParams.js
@@ -21648,6 +1840,9 @@ function seperateWeightMaps(weightMap) {
 }
 
 // build/src/faceProcessor/FaceProcessor.js
+import {
+  tidy as tidy11
+} from "@tensorflow/tfjs";
 class FaceProcessor extends NeuralNetwork {
   constructor(_name, faceFeatureExtractor) {
     super(_name);
@@ -21661,7 +1856,7 @@ class FaceProcessor extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tidy11(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
       return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
     });
@@ -21711,12 +1906,17 @@ class FaceExpressions {
 }
 
 // build/src/faceExpressionNet/FaceExpressionNet.js
+import {
+  softmax,
+  tidy as tidy12,
+  unstack
+} from "@tensorflow/tfjs";
 class FaceExpressionNet extends FaceProcessor {
   constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
     super("FaceExpressionNet", faceFeatureExtractor);
   }
   forwardInput(input) {
-    return tidy(() => softmax(this.runNet(input)));
+    return tidy12(() => softmax(this.runNet(input)));
   }
   async forward(input) {
     return this.forwardInput(await toNetInput(input));
@@ -21766,7 +1966,7 @@ function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, te
     const sorted = expr.asSortedArray();
     const resultsToDisplay = sorted.filter((expr2) => expr2.probability > minConfidence);
     const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0);
-    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round3(expr2.probability)})`), anchor);
+    const drawTextField = new DrawTextField(resultsToDisplay.map((expr2) => `${expr2.expression} (${round(expr2.probability)})`), anchor);
     drawTextField.draw(canvasArg);
   });
 }
@@ -21895,7 +2095,7 @@ function extractParams5(weights, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
@@ -21949,7 +2149,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
     reduction_block_1: entry_flow_reduction_block_1
   };
   const middle_flow = {};
-  range4(numMainBlocks, 0, 1).forEach((idx) => {
+  range(numMainBlocks, 0, 1).forEach((idx) => {
     middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
   });
   const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
@@ -21963,22 +2163,30 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
 }
 
 // build/src/xception/TinyXception.js
+import {
+  add as add5,
+  conv2d as conv2d3,
+  maxPool,
+  relu as relu3,
+  scalar as scalar2,
+  tidy as tidy13
+} from "@tensorflow/tfjs";
 function conv(x, params, stride) {
-  return add2(conv2d(x, params.filters, stride, "same"), params.bias);
+  return add5(conv2d3(x, params.filters, stride, "same"), params.bias);
 }
 function reductionBlock(x, params, isActivateInput = true) {
-  let out = isActivateInput ? relu(x) : x;
+  let out = isActivateInput ? relu3(x) : x;
   out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv1, [1, 1]);
   out = maxPool(out, [3, 3], [2, 2], "same");
-  out = add2(out, conv(x, params.expansion_conv, [2, 2]));
+  out = add5(out, conv(x, params.expansion_conv, [2, 2]));
   return out;
 }
 function mainBlock(x, params) {
-  let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
-  out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]);
-  out = add2(out, x);
+  let out = depthwiseSeparableConv(relu3(x), params.separable_conv0, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv1, [1, 1]);
+  out = depthwiseSeparableConv(relu3(out), params.separable_conv2, [1, 1]);
+  out = add5(out, x);
   return out;
 }
 class TinyXception extends NeuralNetwork {
@@ -21991,18 +2199,18 @@ class TinyXception extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyXception - load model before inference");
     }
-    return tidy(() => {
+    return tidy13(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
-      let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar2(256));
+      let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2]));
       out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
       out = reductionBlock(out, params.entry_flow.reduction_block_1);
-      range4(this._numMainBlocks, 0, 1).forEach((idx) => {
+      range(this._numMainBlocks, 0, 1).forEach((idx) => {
         out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
       });
       out = reductionBlock(out, params.exit_flow.reduction_block);
-      out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
+      out = relu3(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
       return out;
     });
   }
@@ -22063,6 +2271,12 @@ var Gender;
 })(Gender || (Gender = {}));
 
 // build/src/ageGenderNet/AgeGenderNet.js
+import {
+  avgPool as avgPool2,
+  softmax as softmax2,
+  tidy as tidy14,
+  unstack as unstack2
+} from "@tensorflow/tfjs";
 class AgeGenderNet extends NeuralNetwork {
   constructor(faceFeatureExtractor = new TinyXception(2)) {
     super("AgeGenderNet");
@@ -22076,18 +2290,18 @@ class AgeGenderNet extends NeuralNetwork {
     if (!params) {
       throw new Error(`${this._name} - load model before inference`);
     }
-    return tidy(() => {
+    return tidy14(() => {
       const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
+      const pooled = avgPool2(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
       const age = fullyConnectedLayer(pooled, params.fc.age).as1D();
       const gender = fullyConnectedLayer(pooled, params.fc.gender);
       return {age, gender};
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tidy14(() => {
       const {age, gender} = this.runNet(input);
-      return {age, gender: softmax(gender)};
+      return {age, gender: softmax2(gender)};
     });
   }
   async forward(input) {
@@ -22096,8 +2310,8 @@ class AgeGenderNet extends NeuralNetwork {
   async predictAgeAndGender(input) {
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput);
-    const ages = unstack(out.age);
-    const genders = unstack(out.gender);
+    const ages = unstack2(out.age);
+    const genders = unstack2(out.gender);
     const ageAndGenderTensors = ages.map((ageTensor, i) => ({
       ageTensor,
       genderTensor: genders[i]
@@ -22148,6 +2362,12 @@ class AgeGenderNet extends NeuralNetwork {
 // build/src/ageGenderNet/index.js
 
 // build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+import {
+  fill as fill3,
+  stack as stack2,
+  tidy as tidy15,
+  unstack as unstack3
+} from "@tensorflow/tfjs";
 class FaceLandmark68NetBase extends FaceProcessor {
   postProcess(output, inputSize, originalDimensions) {
     const inputDimensions = originalDimensions.map(({width, height}) => {
@@ -22158,10 +2378,10 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
     });
     const batchSize = inputDimensions.length;
-    return tidy(() => {
-      const createInterleavedTensor = (fillX, fillY) => stack([
-        fill([68], fillX),
-        fill([68], fillY)
+    return tidy15(() => {
+      const createInterleavedTensor = (fillX, fillY) => stack2([
+        fill3([68], fillX),
+        fill3([68], fillY)
       ], 1).as2D(1, 136).as1D();
       const getPadding = (batchIdx, cond) => {
         const {width, height} = inputDimensions[batchIdx];
@@ -22169,12 +2389,12 @@ class FaceLandmark68NetBase extends FaceProcessor {
       };
       const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
       const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      const landmarkTensors = output.mul(fill([batchSize, 136], inputSize)).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+      const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize)).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
       return landmarkTensors;
     });
   }
   forwardInput(input) {
-    return tidy(() => {
+    return tidy15(() => {
       const out = this.runNet(input);
       return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
     });
@@ -22184,7 +2404,7 @@ class FaceLandmark68NetBase extends FaceProcessor {
   }
   async detectLandmarks(input) {
     const netInput = await toNetInput(input);
-    const landmarkTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const landmarkTensors = tidy15(() => unstack3(this.forwardInput(netInput)));
     const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => {
       const landmarksArray = Array.from(await landmarkTensor.data());
       const xCoords = landmarksArray.filter((_, i) => isEven(i));
@@ -22246,6 +2466,11 @@ function extractParamsTiny(weights) {
 }
 
 // build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+import {
+  avgPool as avgPool3,
+  scalar as scalar3,
+  tidy as tidy16
+} from "@tensorflow/tfjs";
 class TinyFaceFeatureExtractor extends NeuralNetwork {
   constructor() {
     super("TinyFaceFeatureExtractor");
@@ -22255,14 +2480,14 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyFaceFeatureExtractor - load model before inference");
     }
-    return tidy(() => {
+    return tidy16(() => {
       const batchTensor = input.toBatchTensor(112, true);
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar3(255));
       let out = denseBlock3(normalized, params.dense0, true);
       out = denseBlock3(out, params.dense1);
       out = denseBlock3(out, params.dense2);
-      out = avgPool(out, [14, 14], [2, 2], "valid");
+      out = avgPool3(out, [14, 14], [2, 2], "valid");
       return out;
     });
   }
@@ -22298,17 +2523,26 @@ class FaceLandmarkNet extends FaceLandmark68Net {
 }
 
 // build/src/faceRecognitionNet/scaleLayer.js
+import {
+  add as add6,
+  mul
+} from "@tensorflow/tfjs";
 function scale(x, params) {
-  return add2(mul(x, params.weights), params.biases);
+  return add6(mul(x, params.weights), params.biases);
 }
 
 // build/src/faceRecognitionNet/convLayer.js
+import {
+  add as add7,
+  conv2d as conv2d4,
+  relu as relu4
+} from "@tensorflow/tfjs";
 function convLayer2(x, params, strides, withRelu, padding = "same") {
   const {filters, bias} = params.conv;
-  let out = conv2d(x, filters, strides, padding);
-  out = add2(out, bias);
+  let out = conv2d4(x, filters, strides, padding);
+  out = add7(out, bias);
   out = scale(out, params.scale);
-  return withRelu ? relu(out) : out;
+  return withRelu ? relu4(out) : out;
 }
 function conv2(x, params) {
   return convLayer2(x, params, [1, 1], true);
@@ -22321,6 +2555,13 @@ function convDown(x, params) {
 }
 
 // build/src/faceRecognitionNet/extractParams.js
+import {
+  tensor1d as tensor1d4,
+  tensor2d as tensor2d2,
+  tensor4d as tensor4d3,
+  tidy as tidy17,
+  transpose
+} from "@tensorflow/tfjs";
 function extractorsFactory5(extractWeights, paramMappings) {
   function extractFilterValues(numFilterValues, numFilters, filterSize) {
     const weights = extractWeights(numFilterValues);
@@ -22328,17 +2569,17 @@ function extractorsFactory5(extractWeights, paramMappings) {
     if (isFloat(depth)) {
       throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
     }
-    return tidy(() => transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
+    return tidy17(() => transpose(tensor4d3(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
   }
   function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
     const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-    const bias = tensor1d(extractWeights(numFilters));
+    const bias = tensor1d4(extractWeights(numFilters));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
     return {filters, bias};
   }
   function extractScaleLayerParams(numWeights, mappedPrefix) {
-    const weights = tensor1d(extractWeights(numWeights));
-    const biases = tensor1d(extractWeights(numWeights));
+    const weights = tensor1d4(extractWeights(numWeights));
+    const biases = tensor1d4(extractWeights(numWeights));
     paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
     return {
       weights,
@@ -22379,7 +2620,7 @@ function extractParams9(weights) {
   const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1");
   const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2");
   const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out");
-  const fc = tidy(() => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
+  const fc = tidy17(() => transpose(tensor2d2(extractWeights(256 * 128), [128, 256]), [1, 0]));
   paramMappings.push({paramPath: `fc`});
   if (getRemainingWeights().length !== 0) {
     throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
@@ -22476,37 +2717,52 @@ function extractParamsFromWeigthMap9(weightMap) {
 }
 
 // build/src/faceRecognitionNet/residualLayer.js
+import {
+  add as add8,
+  avgPool as avgPool4,
+  concat as concat3,
+  relu as relu5,
+  zeros
+} from "@tensorflow/tfjs";
 function residual(x, params) {
   let out = conv2(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  out = add2(out, x);
-  out = relu(out);
+  out = add8(out, x);
+  out = relu5(out);
   return out;
 }
 function residualDown(x, params) {
   let out = convDown(x, params.conv1);
   out = convNoRelu(out, params.conv2);
-  let pooled = avgPool(x, 2, 2, "valid");
-  const zeros9 = zeros(pooled.shape);
+  let pooled = avgPool4(x, 2, 2, "valid");
+  const zeros2 = zeros(pooled.shape);
   const isPad = pooled.shape[3] !== out.shape[3];
   const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
   if (isAdjustShape) {
     const padShapeX = [...out.shape];
     padShapeX[1] = 1;
     const zerosW = zeros(padShapeX);
-    out = concat([out, zerosW], 1);
+    out = concat3([out, zerosW], 1);
     const padShapeY = [...out.shape];
     padShapeY[2] = 1;
     const zerosH = zeros(padShapeY);
-    out = concat([out, zerosH], 2);
+    out = concat3([out, zerosH], 2);
   }
-  pooled = isPad ? concat([pooled, zeros9], 3) : pooled;
-  out = add2(pooled, out);
-  out = relu(out);
+  pooled = isPad ? concat3([pooled, zeros2], 3) : pooled;
+  out = add8(pooled, out);
+  out = relu5(out);
   return out;
 }
 
 // build/src/faceRecognitionNet/FaceRecognitionNet.js
+import {
+  cast as cast3,
+  matMul as matMul2,
+  maxPool as maxPool2,
+  scalar as scalar4,
+  tidy as tidy18,
+  unstack as unstack4
+} from "@tensorflow/tfjs";
 class FaceRecognitionNet extends NeuralNetwork {
   constructor() {
     super("FaceRecognitionNet");
@@ -22516,12 +2772,12 @@ class FaceRecognitionNet extends NeuralNetwork {
     if (!params) {
       throw new Error("FaceRecognitionNet - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(150, true), "float32");
+    return tidy18(() => {
+      const batchTensor = cast3(input.toBatchTensor(150, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar(256));
+      const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
       let out = convDown(normalized, params.conv32_down);
-      out = maxPool(out, 3, 2, "valid");
+      out = maxPool2(out, 3, 2, "valid");
       out = residual(out, params.conv32_1);
       out = residual(out, params.conv32_2);
       out = residual(out, params.conv32_3);
@@ -22537,7 +2793,7 @@ class FaceRecognitionNet extends NeuralNetwork {
       out = residual(out, params.conv256_2);
       out = residualDown(out, params.conv256_down_out);
       const globalAvg = out.mean([1, 2]);
-      const fullyConnected = matMul(globalAvg, params.fc);
+      const fullyConnected = matMul2(globalAvg, params.fc);
       return fullyConnected;
     });
   }
@@ -22546,7 +2802,7 @@ class FaceRecognitionNet extends NeuralNetwork {
   }
   async computeFaceDescriptor(input) {
     const netInput = await toNetInput(input);
-    const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput)));
+    const faceDescriptorTensors = tidy18(() => unstack4(this.forwardInput(netInput)));
     const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data()));
     faceDescriptorTensors.forEach((t) => t.dispose());
     return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];
@@ -22596,13 +2852,18 @@ function extendWithGender(sourceObj, gender, genderProbability) {
 // build/src/factories/index.js
 
 // build/src/ssdMobilenetv1/extractParams.js
+import {
+  tensor1d as tensor1d5,
+  tensor3d,
+  tensor4d as tensor4d4
+} from "@tensorflow/tfjs";
 function extractorsFactory7(extractWeights, paramMappings) {
   function extractDepthwiseConvParams(numChannels, mappedPrefix) {
-    const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
-    const batch_norm_scale = tensor1d(extractWeights(numChannels));
-    const batch_norm_offset = tensor1d(extractWeights(numChannels));
-    const batch_norm_mean = tensor1d(extractWeights(numChannels));
-    const batch_norm_variance = tensor1d(extractWeights(numChannels));
+    const filters = tensor4d4(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+    const batch_norm_scale = tensor1d5(extractWeights(numChannels));
+    const batch_norm_offset = tensor1d5(extractWeights(numChannels));
+    const batch_norm_mean = tensor1d5(extractWeights(numChannels));
+    const batch_norm_variance = tensor1d5(extractWeights(numChannels));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`});
     return {
       filters,
@@ -22613,8 +2874,8 @@ function extractorsFactory7(extractWeights, paramMappings) {
     };
   }
   function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-    const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-    const bias = tensor1d(extractWeights(channelsOut));
+    const filters = tensor4d4(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+    const bias = tensor1d5(extractWeights(channelsOut));
     paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
     return {filters, bias};
   }
@@ -22853,28 +3114,40 @@ function extractParamsFromWeigthMap11(weightMap) {
 }
 
 // build/src/ssdMobilenetv1/pointwiseConvLayer.js
+import {
+  add as add9,
+  clipByValue,
+  conv2d as conv2d5,
+  tidy as tidy19
+} from "@tensorflow/tfjs";
 function pointwiseConvLayer(x, params, strides) {
-  return tidy(() => {
-    let out = conv2d(x, params.filters, strides, "same");
-    out = add2(out, params.batch_norm_offset);
+  return tidy19(() => {
+    let out = conv2d5(x, params.filters, strides, "same");
+    out = add9(out, params.batch_norm_offset);
     return clipByValue(out, 0, 6);
   });
 }
 
 // build/src/ssdMobilenetv1/mobileNetV1.js
+import {
+  batchNorm,
+  clipByValue as clipByValue2,
+  depthwiseConv2d,
+  tidy as tidy20
+} from "@tensorflow/tfjs";
 const epsilon = 0.0010000000474974513;
 function depthwiseConvLayer(x, params, strides) {
-  return tidy(() => {
+  return tidy20(() => {
     let out = depthwiseConv2d(x, params.filters, strides, "same");
     out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
-    return clipByValue(out, 0, 6);
+    return clipByValue2(out, 0, 6);
   });
 }
 function getStridesForLayerIdx(layerIdx) {
   return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];
 }
 function mobileNetV1(x, params) {
-  return tidy(() => {
+  return tidy20(() => {
     let conv11;
     let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
     const convPairParams = [
@@ -22912,7 +3185,7 @@ function mobileNetV1(x, params) {
 }
 
 // build/src/ssdMobilenetv1/nonMaxSuppression.js
-function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
   const numBoxes = boxes.shape[0];
   const outputSize = Math.min(maxOutputSize, numBoxes);
   const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score);
@@ -22963,15 +3236,31 @@ function IOU(boxes, i, j) {
 }
 
 // build/src/ssdMobilenetv1/outputLayer.js
+import {
+  add as add10,
+  div,
+  exp,
+  mul as mul2,
+  reshape,
+  scalar as scalar5,
+  sigmoid as sigmoid2,
+  slice,
+  stack as stack3,
+  sub as sub2,
+  tidy as tidy21,
+  tile,
+  transpose as transpose2,
+  unstack as unstack5
+} from "@tensorflow/tfjs";
 function getCenterCoordinatesAndSizesLayer(x) {
-  const vec = unstack(transpose(x, [1, 0]));
+  const vec = unstack5(transpose2(x, [1, 0]));
   const sizes = [
-    sub(vec[2], vec[0]),
-    sub(vec[3], vec[1])
+    sub2(vec[2], vec[0]),
+    sub2(vec[3], vec[1])
   ];
   const centers = [
-    add2(vec[0], div(sizes[0], scalar(2))),
-    add2(vec[1], div(sizes[1], scalar(2)))
+    add10(vec[0], div(sizes[0], scalar5(2))),
+    add10(vec[1], div(sizes[1], scalar5(2)))
   ];
   return {
     sizes,
@@ -22980,28 +3269,28 @@ function getCenterCoordinatesAndSizesLayer(x) {
 }
 function decodeBoxesLayer(x0, x1) {
   const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-  const vec = unstack(transpose(x1, [1, 0]));
-  const div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
-  const add0_out = add2(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
-  const div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
-  const add1_out = add2(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
-  return transpose(stack([
-    sub(add0_out, div0_out),
-    sub(add1_out, div1_out),
-    add2(add0_out, div0_out),
-    add2(add1_out, div1_out)
+  const vec = unstack5(transpose2(x1, [1, 0]));
+  const div0_out = div(mul2(exp(div(vec[2], scalar5(5))), sizes[0]), scalar5(2));
+  const add0_out = add10(mul2(div(vec[0], scalar5(10)), sizes[0]), centers[0]);
+  const div1_out = div(mul2(exp(div(vec[3], scalar5(5))), sizes[1]), scalar5(2));
+  const add1_out = add10(mul2(div(vec[1], scalar5(10)), sizes[1]), centers[1]);
+  return transpose2(stack3([
+    sub2(add0_out, div0_out),
+    sub2(add1_out, div1_out),
+    add10(add0_out, div0_out),
+    add10(add1_out, div1_out)
   ]), [1, 0]);
 }
 function outputLayer(boxPredictions, classPredictions, params) {
-  return tidy(() => {
+  return tidy21(() => {
     const batchSize = boxPredictions.shape[0];
     let boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
     boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
-    const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+    const scoresAndClasses = sigmoid2(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
     let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
     scores = reshape(scores, [batchSize, scores.shape[1]]);
-    const boxesByBatch = unstack(boxes);
-    const scoresByBatch = unstack(scores);
+    const boxesByBatch = unstack5(boxes);
+    const scoresByBatch = unstack5(scores);
     return {
       boxes: boxesByBatch,
       scores: scoresByBatch
@@ -23010,11 +3299,15 @@ function outputLayer(boxPredictions, classPredictions, params) {
 }
 
 // build/src/ssdMobilenetv1/boxPredictionLayer.js
+import {
+  reshape as reshape2,
+  tidy as tidy22
+} from "@tensorflow/tfjs";
 function boxPredictionLayer(x, params) {
-  return tidy(() => {
+  return tidy22(() => {
     const batchSize = x.shape[0];
-    const boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-    const classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+    const boxPredictionEncoding = reshape2(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+    const classPrediction = reshape2(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
     return {
       boxPredictionEncoding,
       classPrediction
@@ -23023,8 +3316,12 @@ function boxPredictionLayer(x, params) {
 }
 
 // build/src/ssdMobilenetv1/predictionLayer.js
+import {
+  concat as concat4,
+  tidy as tidy23
+} from "@tensorflow/tfjs";
 function predictionLayer(x, conv11, params) {
-  return tidy(() => {
+  return tidy23(() => {
     const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
     const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
     const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
@@ -23039,7 +3336,7 @@ function predictionLayer(x, conv11, params) {
     const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
     const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
     const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
-    const boxPredictions = concat([
+    const boxPredictions = concat4([
       boxPrediction0.boxPredictionEncoding,
       boxPrediction1.boxPredictionEncoding,
       boxPrediction2.boxPredictionEncoding,
@@ -23047,7 +3344,7 @@ function predictionLayer(x, conv11, params) {
       boxPrediction4.boxPredictionEncoding,
       boxPrediction5.boxPredictionEncoding
     ], 1);
-    const classPredictions = concat([
+    const classPredictions = concat4([
       boxPrediction0.classPrediction,
       boxPrediction1.classPrediction,
       boxPrediction2.classPrediction,
@@ -23084,6 +3381,13 @@ class SsdMobilenetv1Options {
 }
 
 // build/src/ssdMobilenetv1/SsdMobilenetv1.js
+import {
+  cast as cast4,
+  mul as mul3,
+  scalar as scalar6,
+  sub as sub3,
+  tidy as tidy24
+} from "@tensorflow/tfjs";
 class SsdMobilenetv1 extends NeuralNetwork {
   constructor() {
     super("SsdMobilenetv1");
@@ -23093,9 +3397,9 @@ class SsdMobilenetv1 extends NeuralNetwork {
     if (!params) {
       throw new Error("SsdMobilenetv1 - load model before inference");
     }
-    return tidy(() => {
-      const batchTensor = cast(input.toBatchTensor(512, false), "float32");
-      const x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
+    return tidy24(() => {
+      const batchTensor = cast4(input.toBatchTensor(512, false), "float32");
+      const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1));
       const features = mobileNetV1(x, params.mobilenetv1);
       const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer);
       return outputLayer(boxPredictions, classPredictions, params.output_layer);
@@ -23116,7 +3420,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
     }
     const scoresData = Array.from(await scores.data());
     const iouThreshold = 0.5;
-    const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+    const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
     const reshapedDims = netInput.getReshapedInputDimensions(0);
     const inputSize = netInput.inputSize;
     const padX = inputSize / reshapedDims.width;
@@ -23184,7 +3488,7 @@ const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
 const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
 
 // build/src/tinyYolov2/config.js
-const isNumber2 = (arg) => typeof arg === "number";
+const isNumber = (arg) => typeof arg === "number";
 function validateConfig(config2) {
   if (!config2) {
     throw new Error(`invalid config: ${config2}`);
@@ -23192,58 +3496,83 @@ function validateConfig(config2) {
   if (typeof config2.withSeparableConvs !== "boolean") {
     throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
   }
-  if (!isNumber2(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
+  if (!isNumber(config2.iouThreshold) || config2.iouThreshold < 0 || config2.iouThreshold > 1) {
     throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config2.iouThreshold}`);
   }
   if (!Array.isArray(config2.classes) || !config2.classes.length || !config2.classes.every((c) => typeof c === "string")) {
     throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config2.classes)}`);
   }
-  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) {
+  if (!Array.isArray(config2.anchors) || !config2.anchors.length || !config2.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) {
     throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config2.anchors)}`);
   }
-  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber2))) {
+  if (config2.meanRgb && (!Array.isArray(config2.meanRgb) || config2.meanRgb.length !== 3 || !config2.meanRgb.every(isNumber))) {
     throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
   }
 }
 
 // build/src/tinyYolov2/leaky.js
+import {
+  add as add11,
+  mul as mul4,
+  relu as relu6,
+  scalar as scalar7,
+  sub as sub4,
+  tidy as tidy25
+} from "@tensorflow/tfjs";
 function leaky(x) {
-  return tidy(() => {
-    const min4 = mul(x, scalar(0.10000000149011612));
-    return add2(relu(sub(x, min4)), min4);
+  return tidy25(() => {
+    const min = mul4(x, scalar7(0.10000000149011612));
+    return add11(relu6(sub4(x, min)), min);
   });
 }
 
 // build/src/tinyYolov2/convWithBatchNorm.js
+import {
+  add as add12,
+  conv2d as conv2d6,
+  mul as mul5,
+  pad,
+  sub as sub5,
+  tidy as tidy26
+} from "@tensorflow/tfjs";
 function convWithBatchNorm(x, params) {
-  return tidy(() => {
+  return tidy26(() => {
     let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = conv2d(out, params.conv.filters, [1, 1], "valid");
-    out = sub(out, params.bn.sub);
-    out = mul(out, params.bn.truediv);
-    out = add2(out, params.conv.bias);
+    out = conv2d6(out, params.conv.filters, [1, 1], "valid");
+    out = sub5(out, params.bn.sub);
+    out = mul5(out, params.bn.truediv);
+    out = add12(out, params.conv.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/depthwiseSeparableConv.js
+import {
+  add as add13,
+  pad as pad2,
+  separableConv2d as separableConv2d2,
+  tidy as tidy27
+} from "@tensorflow/tfjs";
 function depthwiseSeparableConv3(x, params) {
-  return tidy(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = add2(out, params.bias);
+  return tidy27(() => {
+    let out = pad2(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+    out = separableConv2d2(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
+    out = add13(out, params.bias);
     return leaky(out);
   });
 }
 
 // build/src/tinyYolov2/extractParams.js
+import {
+  tensor1d as tensor1d6
+} from "@tensorflow/tfjs";
 function extractorsFactory9(extractWeights, paramMappings) {
   const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
   function extractBatchNormParams(size, mappedPrefix) {
-    const sub32 = tensor1d(extractWeights(size));
-    const truediv = tensor1d(extractWeights(size));
+    const sub6 = tensor1d6(extractWeights(size));
+    const truediv = tensor1d6(extractWeights(size));
     paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
     const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
@@ -23297,9 +3626,9 @@ function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
 function extractorsFactory10(weightMap, paramMappings) {
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
   function extractBatchNormParams(prefix) {
-    const sub32 = extractWeightEntry(`${prefix}/sub`, 1);
+    const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
     const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-    return {sub: sub32, truediv};
+    return {sub: sub6, truediv};
   }
   function extractConvParams(prefix) {
     const filters = extractWeightEntry(`${prefix}/filters`, 4);
@@ -23381,6 +3710,14 @@ class TinyYolov2Options {
 }
 
 // build/src/tinyYolov2/TinyYolov2Base.js
+import {
+  cast as cast5,
+  maxPool as maxPool3,
+  scalar as scalar8,
+  softmax as softmax3,
+  tidy as tidy28,
+  unstack as unstack6
+} from "@tensorflow/tfjs";
 class TinyYolov2Base extends NeuralNetwork {
   constructor(config2) {
     super("TinyYolov2");
@@ -23398,34 +3735,34 @@ class TinyYolov2Base extends NeuralNetwork {
   }
   runTinyYolov2(x, params) {
     let out = convWithBatchNorm(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = convWithBatchNorm(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = maxPool3(out, [2, 2], [1, 1], "same");
     out = convWithBatchNorm(out, params.conv6);
     out = convWithBatchNorm(out, params.conv7);
     return convLayer(out, params.conv8, "valid", false);
   }
   runMobilenet(x, params) {
     let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv3(x, params.conv0);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv1);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv2);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv3);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv4);
-    out = maxPool(out, [2, 2], [2, 2], "same");
+    out = maxPool3(out, [2, 2], [2, 2], "same");
     out = depthwiseSeparableConv3(out, params.conv5);
-    out = maxPool(out, [2, 2], [1, 1], "same");
+    out = maxPool3(out, [2, 2], [1, 1], "same");
     out = params.conv6 ? depthwiseSeparableConv3(out, params.conv6) : out;
     out = params.conv7 ? depthwiseSeparableConv3(out, params.conv7) : out;
     return convLayer(out, params.conv8, "valid", false);
@@ -23435,10 +3772,10 @@ class TinyYolov2Base extends NeuralNetwork {
     if (!params) {
       throw new Error("TinyYolov2 - load model before inference");
     }
-    return tidy(() => {
-      let batchTensor = cast(input.toBatchTensor(inputSize, false), "float32");
+    return tidy28(() => {
+      let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32");
       batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
-      batchTensor = batchTensor.div(scalar(256));
+      batchTensor = batchTensor.div(scalar8(256));
       return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
     });
   }
@@ -23449,7 +3786,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
     const netInput = await toNetInput(input);
     const out = await this.forwardInput(netInput, inputSize);
-    const out0 = tidy(() => unstack(out)[0].expandDims());
+    const out0 = tidy28(() => unstack6(out)[0].expandDims());
     const inputDimensions = {
       width: netInput.getInputWidth(0),
       height: netInput.getInputHeight(0)
@@ -23461,7 +3798,7 @@ class TinyYolov2Base extends NeuralNetwork {
     const scores = results.map((res) => res.score);
     const classScores = results.map((res) => res.classScore);
     const classNames = results.map((res) => this.config.classes[res.label]);
-    const indices = nonMaxSuppression2(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
+    const indices = nonMaxSuppression(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
     const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions));
     return detections;
   }
@@ -23486,11 +3823,11 @@ class TinyYolov2Base extends NeuralNetwork {
     const correctionFactorY = inputSize / height;
     const numCells = outputTensor.shape[1];
     const numBoxes = this.config.anchors.length;
-    const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => {
+    const [boxesTensor, scoresTensor, classScoresTensor] = tidy28(() => {
       const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);
       const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
       const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
-      const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0);
+      const classScores = this.withClassScores ? softmax3(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar8(0);
       return [boxes, scores, classScores];
     });
     const results = [];
@@ -23499,22 +3836,23 @@ class TinyYolov2Base extends NeuralNetwork {
     for (let row = 0; row < numCells; row++) {
       for (let col = 0; col < numCells; col++) {
         for (let anchor = 0; anchor < numBoxes; anchor++) {
-          const score = sigmoid6(scoresData[row][col][anchor][0]);
+          const score = sigmoid(scoresData[row][col][anchor][0]);
           if (!scoreThreshold || score > scoreThreshold) {
-            const ctX = (col + sigmoid6(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
-            const ctY = (row + sigmoid6(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
+            const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
+            const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
             const width2 = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX;
             const height2 = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY;
             const x = ctX - width2 / 2;
             const y = ctY - height2 / 2;
             const pos = {row, col, anchor};
             const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0};
-            results.push(__assign({
+            results.push({
               box: new BoundingBox(x, y, x + width2, y + height2),
               score,
               classScore: score * classScore,
-              label
-            }, pos));
+              label,
+              ...pos
+            });
           }
         }
       }
@@ -23530,7 +3868,7 @@ class TinyYolov2Base extends NeuralNetwork {
     return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
       classScore,
       label
-    })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr);
+    })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
   }
 }
 TinyYolov2Base.DEFAULT_FILTER_SIZES = [
@@ -23607,11 +3945,14 @@ class ComposableTask {
 }
 
 // build/src/globalApi/extractFacesAndComputeResults.js
+import {
+  Tensor as Tensor4
+} from "@tensorflow/tfjs";
 async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) {
   const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection);
-  const faces = extractedFaces || (input instanceof Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
+  const faces = extractedFaces || (input instanceof Tensor4 ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
   const results = await computeResults(faces);
-  faces.forEach((f) => f instanceof Tensor && f.dispose());
+  faces.forEach((f) => f instanceof Tensor4 && f.dispose());
   return results;
 }
 async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
@@ -23829,6 +4170,9 @@ class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
 }
 
 // build/src/globalApi/DetectFaceLandmarksTasks.js
+import {
+  Tensor as Tensor5
+} from "@tensorflow/tfjs";
 class DetectFaceLandmarksTaskBase extends ComposableTask {
   constructor(parentTask, input, useTinyLandmarkNet) {
     super();
@@ -23844,9 +4188,9 @@ class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
   async run() {
     const parentResults = await this.parentTask;
     const detections = parentResults.map((res) => res.detection);
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
+    const faces = this.input instanceof Tensor5 ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
     const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face)));
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof Tensor5 && f.dispose());
     return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
   }
   withFaceExpressions() {
@@ -23866,9 +4210,9 @@ class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
       return;
     }
     const {detection} = parentResult;
-    const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
+    const faces = this.input instanceof Tensor5 ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
     const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
-    faces.forEach((f) => f instanceof Tensor && f.dispose());
+    faces.forEach((f) => f instanceof Tensor5 && f.dispose());
     return extendWithFaceLandmarks(parentResult, landmarks);
   }
   withFaceExpressions() {
@@ -24057,12 +4401,13 @@ function resizeResults(results, dimensions) {
 }
 
 // build/package.json
-var version2 = "0.5.2";
+var version = "0.5.3";
 
 // build/src/index.js
+import * as tf42 from "@tensorflow/tfjs";
 const node = typeof process !== "undefined" ? process.version : false;
-const browser = typeof navigator !== "undefined" ? navigator.userAgent : false;
-const version3 = {faceapi: version2, node, browser, tfjs: version, backend: getBackend()};
+const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
+const version2 = {faceapi: version, node, browser: browser3};
 export {
   AgeGenderNet,
   BoundingBox,
@@ -24127,7 +4472,7 @@ export {
   detectLandmarks,
   detectSingleFace,
   draw_exports as draw,
-  env2 as env,
+  env,
   euclideanDistance,
   extendWithAge,
   extendWithFaceDescriptor,
@@ -24168,7 +4513,7 @@ export {
   matchDimensions,
   minBbox,
   nets,
-  nonMaxSuppression2 as nonMaxSuppression,
+  nonMaxSuppression,
   normalize,
   padToSquare,
   predictAgeAndGender,
@@ -24176,14 +4521,14 @@ export {
   resizeResults,
   resolveInput,
   shuffleArray,
-  sigmoid6 as sigmoid,
+  sigmoid,
   ssdMobilenetv1,
-  dist_exports as tf,
+  tf42 as tf,
   tinyFaceDetector,
   tinyYolov23 as tinyYolov2,
   toNetInput,
   utils_exports as utils,
   validateConfig,
-  version3 as version
+  version2 as version
 };
 //# sourceMappingURL=face-api.node.js.map
diff --git a/dist/face-api.node.js.map b/dist/face-api.node.js.map
index fad95c5..ce37833 100644
--- a/dist/face-api.node.js.map
+++ b/dist/face-api.node.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["node_modules/seedrandom/lib/alea.js", "node_modules/seedrandom/lib/xor128.js", "node_modules/seedrandom/lib/xorwow.js", "node_modules/seedrandom/lib/xorshift7.js", "node_modules/seedrandom/lib/xor4096.js", "node_modules/seedrandom/lib/tychei.js", "node_modules/seedrandom/seedrandom.js", "node_modules/seedrandom/index.js", "build/src/env/../../../src/env/isNodejs.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/environment.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/global_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_names.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/kernel_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/profiler.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tape.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_format.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/types.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/engine.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/device_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/flags.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/tensor_util_env.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/operation.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/complex.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor_ops_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/types.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io_utils.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/router_registry.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/model_management.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/buffer.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cast.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clone.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/print.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/base_side_effects.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/browser_files.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/progress.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/weights_loader.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/http.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/passthrough.ts", "node_modules/@tensorflow/tfjs-core/dist/io/../../src/io/io.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reshape.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mat_mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/one_hot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/confusion_matrix.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/math.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/browser.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/serialization.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/test_util.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/version.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/globals.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floorDiv.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mul.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/abs.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/acosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/add_n.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/axis_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/all.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/any.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/arg_min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/asinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atan2.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/atanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/avg_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tanh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/basic_lstm_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batch_to_space_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/batchnorm4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_to.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ceil.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/clip_by_value.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/concat_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv2d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_backprop_input.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/conv3d_transpose.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cos.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cosh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/cumsum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depth_to_space.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/depthwise_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/diag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dilation2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/broadcast_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/div_no_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/dot.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/elu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/erf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expand_dims.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/expm1.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tile.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/eye.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/fill.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/floor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reduce_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/segment_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/gather.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/greater_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/imag.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_finite.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_inf.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/is_nan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/maximum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scalar.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/leaky_relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/less_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/linspace.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/local_response_normalization.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log1p.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/gradients.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/neg.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softplus.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sigmoid.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sub.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/log_sum_exp.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_and.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_not.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_or.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/logical_xor.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_with_argmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/zeros.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mean.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/min.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/minimum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/mod.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/square.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/moments.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multi_rnn_cell.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/multinomial.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/not_equal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/real.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/ones_like.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/outer_product.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pad4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/space_to_batch_nd.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pool.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/pow.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/prelu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/prod.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rand.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rand_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_gamma.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_normal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/random_uniform.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/range.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reciprocal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/relu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/relu6.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/reverse_4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/round.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/rsqrt.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/selu.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/separable_conv2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/setdiff1d_async.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sign.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sin.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sinh.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice1d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice3d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/slice4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/softmax.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/fft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/ifft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/irfft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/split_util.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/split.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/spectral/../../../src/ops/spectral/rfft.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/sqrt.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/squared_difference.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/squeeze.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/stack.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/step.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/strided_slice.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tan.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor2d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor4d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor5d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/tensor6d.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/topk.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/truncated_normal.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/unsorted_segment_sum.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/unstack.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/variable.ts", "node_modules/@tensorflow/tfjs-core/dist/backends/../../src/backends/where_impl.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/where_async.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/boolean_mask.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/compare.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/binary_ops.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/norm.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/moving_average.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/scatter_nd.ts", 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"build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/mobileNetV1.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/nonMaxSuppression.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/outputLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/boxPredictionLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/predictionLayer.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/SsdMobilenetv1.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/index.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/const.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/config.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/leaky.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/convWithBatchNorm.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/depthwiseSeparableConv.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParams.ts", 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"build/src/globalApi/../../../src/globalApi/DetectFaceLandmarksTasks.ts", "build/src/globalApi/../../../src/globalApi/DetectFacesTasks.ts", "build/src/globalApi/../../../src/globalApi/detectFaces.ts", "build/src/globalApi/../../../src/globalApi/allFaces.ts", "build/src/../../src/euclideanDistance.ts", "build/src/globalApi/../../../src/globalApi/FaceMatcher.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/index.ts", "build/src/../../src/resizeResults.ts", "build/src/../../src/index.ts"],
-  "sourcesContent": ["// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],
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diff --git a/package-lock.json b/package-lock.json
index c1663b3..36399ca 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -4,24 +4,91 @@
   "lockfileVersion": 1,
   "requires": true,
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+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-2.6.0.tgz",
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+        "@tensorflow/tfjs-converter": "2.6.0",
+        "@tensorflow/tfjs-core": "2.6.0",
+        "@tensorflow/tfjs-data": "2.6.0",
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+        "chalk": "^4.1.0",
+        "core-js": "3",
+        "regenerator-runtime": "^0.13.5",
+        "yargs": "^16.0.3"
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+        "seedrandom": "2.4.3"
+      }
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+        "@types/seedrandom": "2.4.27",
+        "@types/webgl-ext": "0.0.30",
+        "@types/webgl2": "0.0.4",
+        "seedrandom": "2.4.3"
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+    "@tensorflow/tfjs-converter": {
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+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-2.6.0.tgz",
+      "integrity": "sha512-TzL4ULidZ26iVqfLmv5G6dfnJyJt5HttU1VZoBYCbxUcWQYk1Z4D9wqLVwfdcJz01XEKpmsECh8HBF0hwYlrkA=="
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+      "version": "2.6.0",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-2.6.0.tgz",
+      "integrity": "sha512-akUB1iz663UCUdOfEUu91XeHzGpdYtdtMPxjsGEdF0CwENzSAcvHzQrEVoPBRD+RKpxrVXvQBoOd7GYBxMIIKQ==",
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         "@types/webgl-ext": "0.0.30",
         "@types/webgl2": "0.0.4",
-        "node-fetch": "~2.1.2",
+        "node-fetch": "~2.6.1",
         "seedrandom": "2.4.3"
       }
     },
+    "@tensorflow/tfjs-data": {
+      "version": "2.6.0",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-data/-/tfjs-data-2.6.0.tgz",
+      "integrity": "sha512-/x/j/A4Quiyc21xEYyBC82mqyssbFHRuHez7pYVJA/28TOesAfWPWo2I+wkeOTt91UerUeZMSq2FV3HOnPInhQ==",
+      "requires": {
+        "@types/node-fetch": "^2.1.2",
+        "node-fetch": "~2.6.1"
+      }
+    },
+    "@tensorflow/tfjs-layers": {
+      "version": "2.6.0",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-layers/-/tfjs-layers-2.6.0.tgz",
+      "integrity": "sha512-nU9WNSGpEU6GzKo5bvJBMa/OZRe1bR5Z2W6T0XiEY8CBiPNS+oJFJNm0NY8kQj/WnDS0Hfue38P46q7gV/9XMA=="
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-      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.11.1.tgz",
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-      "dev": true
+      "version": "14.11.7",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.11.7.tgz",
+      "integrity": "sha512-hSEXknS4KiayUdZ7401J/T6eykXHJkDEipnyQMJ4/GstK4kWjbHnwXlcpvIWfPKiEH1JU96DkbzJ1nHRmpmKLw=="
+    },
+    "@types/node-fetch": {
+      "version": "2.5.7",
+      "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.5.7.tgz",
+      "integrity": "sha512-o2WVNf5UhWRkxlf6eq+jMZDu7kjgpgJfl4xVNlvryc95O/6F2ld8ztKX+qu+Rjyet93WAWm5LjeX9H5FGkODvw==",
+      "requires": {
+        "@types/node": "*",
+        "form-data": "^3.0.0"
+      }
     },
     "@types/offscreencanvas": {
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@@ -43,12 +110,38 @@
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     },
+    "ansi-regex": {
+      "version": "5.0.0",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.0.tgz",
+      "integrity": "sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg=="
+    },
+    "ansi-styles": {
+      "version": "4.3.0",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
+      "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==",
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+      }
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       "resolved": "https://registry.npmjs.org/arg/-/arg-4.1.3.tgz",
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       "dev": true
     },
+    "argparse": {
+      "version": "1.0.10",
+      "resolved": "https://registry.npmjs.org/argparse/-/argparse-1.0.10.tgz",
+      "integrity": "sha512-o5Roy6tNG4SL/FOkCAN6RzjiakZS25RLYFrcMttJqbdd8BWrnA+fGz57iN5Pb06pvBGvl5gQ0B48dJlslXvoTg==",
+      "requires": {
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+      }
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+        "combined-stream": "^1.0.8",
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@@ -109,6 +277,11 @@
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       }
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@@ -125,12 +298,30 @@
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+    },
+    "mime-types": {
+      "version": "2.1.27",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.27.tgz",
+      "integrity": "sha512-JIhqnCasI9yD+SsmkquHBxTSEuZdQX5BuQnS2Vc7puQQQ+8yiP5AY5uWhpdv4YL4VM5c6iliiYWPgJ/nJQLp7w==",
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@@ -141,9 +332,9 @@
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-      "integrity": "sha1-q4hOjn5X44qUR1POxwb3iNF2i7U="
+      "version": "2.6.1",
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+      "integrity": "sha512-V4aYg89jEoVRxRb2fJdAg8FHvI7cEyYdVAh94HH0UIK8oJxUfkjlDQN9RbMx+bEjP7+ggMiFRprSti032Oipxw=="
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@@ -160,6 +351,16 @@
       "integrity": "sha1-F0uSaHNVNP+8es5r9TpanhtcX18=",
       "dev": true
     },
+    "regenerator-runtime": {
+      "version": "0.13.7",
+      "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.7.tgz",
+      "integrity": "sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew=="
+    },
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+      "version": "2.1.1",
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+      "integrity": "sha1-jGStX9MNqxyXbiNE/+f3kqam30I="
+    },
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@@ -190,6 +391,37 @@
         "source-map": "^0.6.0"
       }
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+    "sprintf-js": {
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+      "resolved": "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.0.3.tgz",
+      "integrity": "sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw="
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+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.0"
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+    },
+    "strip-ansi": {
+      "version": "6.0.0",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.0.tgz",
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+        "ansi-regex": "^5.0.0"
+      }
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+    "supports-color": {
+      "version": "7.2.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
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+      }
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       "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-9.0.0.tgz",
@@ -204,23 +436,57 @@
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+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.0.2.tgz",
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       "dev": true
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-      "version": "4.1.0-dev.20200918",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.0-dev.20200918.tgz",
-      "integrity": "sha512-cEcXJvz55OH3k3cmpMYoVfqdAQ2YvgeccUmccmleUnQ8VqR8T/7GI761Qky/vGZO/VhiU3Y8xJF3oLkAkNrG1g==",
+      "version": "4.1.0-dev.20201008",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.0-dev.20201008.tgz",
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+    "wrap-ansi": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
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+        "string-width": "^4.1.0",
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+    "y18n": {
+      "version": "5.0.2",
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+      "requires": {
+        "cliui": "^7.0.0",
+        "escalade": "^3.0.2",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.0",
+        "y18n": "^5.0.1",
+        "yargs-parser": "^20.0.0"
+      }
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+    "yargs-parser": {
+      "version": "20.2.1",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.1.tgz",
+      "integrity": "sha512-yYsjuSkjbLMBp16eaOt7/siKTjNVjMm3SoJnIg3sEh/JsvqVVDyjRKmaJV4cl+lNIgq6QEco2i3gDebJl7/vLA=="
+    },
     "yn": {
       "version": "3.1.1",
       "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz",
diff --git a/package.json b/package.json
index 83daada..98216cb 100644
--- a/package.json
+++ b/package.json
@@ -2,7 +2,7 @@
   "name": "@vladmandic/face-api",
   "version": "0.5.3",
   "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
-  "main": "dist/face-api.node.js",
+  "main": "build/src/index.js",
   "browser": "dist/face-api.esm.js",
   "typings": "./build/src/index.d.ts",
   "engines": {
@@ -12,11 +12,11 @@
   "scripts": {
     "compile": "tsc",
     "clean": "rimraf build/ dist/",
-    "cjs": "esbuild --bundle --outfile=./dist/face-api.cjs.js --target=es2017 --platform=browser --format=cjs --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-    "node": "esbuild --bundle --outfile=./dist/face-api.node.js --target=es2017 --platform=node --format=esm --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-    "esm": "esbuild --bundle --outfile=./dist/face-api.esm.js --target=es2017 --platform=browser --format=esm --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-    "iife": "esbuild --bundle --outfile=./dist/face-api.iife.js --target=es2017 --platform=browser --format=iife --global-name=faceapi --sourcemap --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
-    "minify": "esbuild --bundle --outfile=./dist/face-api.min.js --target=es2017 --platform=browser --format=iife --sourcemap --global-name=faceapi --log-level=error --minify --tsconfig=./tsconfig.json build/src/index.js",
+    "cjs": "esbuild --bundle --outfile=./dist/face-api.cjs.js --target=esnext --platform=browser --format=cjs --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "node": "esbuild --bundle --outfile=./dist/face-api.node.js --target=esnext --platform=node --format=esm --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "esm": "esbuild --bundle --outfile=./dist/face-api.esm.js --target=esnext --platform=browser --format=esm --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "iife": "esbuild --bundle --outfile=./dist/face-api.iife.js --target=esnext --platform=browser --format=iife --global-name=faceapi --sourcemap --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "minify": "esbuild --bundle --outfile=./dist/face-api.min.js --target=esnext --platform=browser --format=iife --sourcemap --global-name=faceapi --external:@tensorflow/tfjs --log-level=error --minify --tsconfig=./tsconfig.json build/src/index.js",
     "build": "npm run clean && npm run compile && npm run cjs && npm run esm && npm run iife && npm run node && npm run minify"
   },
   "keywords": [
@@ -42,14 +42,14 @@
   },
   "homepage": "https://github.com/vladmandic/face-api#readme",
   "dependencies": {
-    "@tensorflow/tfjs-core": "^2.4.0"
+    "@tensorflow/tfjs": "^2.6.0"
   },
   "devDependencies": {
-    "@types/node": "^14.10.2",
+    "@types/node": "^14.11.7",
     "esbuild": "^0.6.34",
     "rimraf": "^3.0.2",
     "ts-node": "^9.0.0",
-    "tslib": "^2.0.1",
-    "typescript": "^4.1.0-dev.20200916"
+    "tslib": "^2.0.2",
+    "typescript": "^4.1.0-dev.20201008"
   }
 }
diff --git a/src/NeuralNetwork.ts b/src/NeuralNetwork.ts
index 699bea5..b6cfd3a 100644
--- a/src/NeuralNetwork.ts
+++ b/src/NeuralNetwork.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ParamMapping } from './common';
 import { getModelUris } from './common/getModelUris';
diff --git a/src/ageGenderNet/AgeGenderNet.ts b/src/ageGenderNet/AgeGenderNet.ts
index 4e1f288..3bd62f0 100644
--- a/src/ageGenderNet/AgeGenderNet.ts
+++ b/src/ageGenderNet/AgeGenderNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { seperateWeightMaps } from '../faceProcessor/util';
diff --git a/src/ageGenderNet/extractParamsFromWeigthMap.ts b/src/ageGenderNet/extractParamsFromWeigthMap.ts
index 15e91dc..5d2a77a 100644
--- a/src/ageGenderNet/extractParamsFromWeigthMap.ts
+++ b/src/ageGenderNet/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';
 import { NetParams } from './types';
diff --git a/src/ageGenderNet/types.ts b/src/ageGenderNet/types.ts
index 3ec1b95..92c6079 100644
--- a/src/ageGenderNet/types.ts
+++ b/src/ageGenderNet/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FCParams } from '../common';
 
diff --git a/src/common/convLayer.ts b/src/common/convLayer.ts
index 764b166..8b17e5a 100644
--- a/src/common/convLayer.ts
+++ b/src/common/convLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from './types';
 
diff --git a/src/common/depthwiseSeparableConv.ts b/src/common/depthwiseSeparableConv.ts
index b6c6bfe..c8a4c27 100644
--- a/src/common/depthwiseSeparableConv.ts
+++ b/src/common/depthwiseSeparableConv.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { SeparableConvParams } from './types';
 
diff --git a/src/common/extractConvParamsFactory.ts b/src/common/extractConvParamsFactory.ts
index e55fafa..0f13b1e 100644
--- a/src/common/extractConvParamsFactory.ts
+++ b/src/common/extractConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
 
diff --git a/src/common/extractFCParamsFactory.ts b/src/common/extractFCParamsFactory.ts
index 68a9c91..7335116 100644
--- a/src/common/extractFCParamsFactory.ts
+++ b/src/common/extractFCParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ExtractWeightsFunction, FCParams, ParamMapping } from './types';
 
diff --git a/src/common/extractSeparableConvParamsFactory.ts b/src/common/extractSeparableConvParamsFactory.ts
index d5d23ce..bbab8ca 100644
--- a/src/common/extractSeparableConvParamsFactory.ts
+++ b/src/common/extractSeparableConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';
 
diff --git a/src/common/fullyConnectedLayer.ts b/src/common/fullyConnectedLayer.ts
index 806f764..4e67cae 100644
--- a/src/common/fullyConnectedLayer.ts
+++ b/src/common/fullyConnectedLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FCParams } from './types';
 
diff --git a/src/common/loadConvParamsFactory.ts b/src/common/loadConvParamsFactory.ts
index 286aa21..5546df9 100644
--- a/src/common/loadConvParamsFactory.ts
+++ b/src/common/loadConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from './types';
 
diff --git a/src/common/types.ts b/src/common/types.ts
index e3c50bb..0179990 100644
--- a/src/common/types.ts
+++ b/src/common/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 export type ExtractWeightsFunction = (numWeights: number) => Float32Array
 
diff --git a/src/dom/NetInput.ts b/src/dom/NetInput.ts
index c6560b3..ad8c54a 100644
--- a/src/dom/NetInput.ts
+++ b/src/dom/NetInput.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { Dimensions } from '../classes/Dimensions';
 import { env } from '../env';
diff --git a/src/dom/extractFaceTensors.ts b/src/dom/extractFaceTensors.ts
index da39944..52c8bcb 100644
--- a/src/dom/extractFaceTensors.ts
+++ b/src/dom/extractFaceTensors.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
diff --git a/src/dom/imageTensorToCanvas.ts b/src/dom/imageTensorToCanvas.ts
index d77f0b2..89eb344 100644
--- a/src/dom/imageTensorToCanvas.ts
+++ b/src/dom/imageTensorToCanvas.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { env } from '../env';
 import { isTensor4D } from '../utils';
diff --git a/src/dom/loadWeightMap.ts b/src/dom/loadWeightMap.ts
index 32b1c27..bdb1d92 100644
--- a/src/dom/loadWeightMap.ts
+++ b/src/dom/loadWeightMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { getModelUris } from '../common/getModelUris';
 import { fetchJson } from './fetchJson';
diff --git a/src/dom/types.ts b/src/dom/types.ts
index 44cc15f..6641c9f 100644
--- a/src/dom/types.ts
+++ b/src/dom/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput } from './NetInput';
 
diff --git a/src/faceExpressionNet/FaceExpressionNet.ts b/src/faceExpressionNet/FaceExpressionNet.ts
index f7d5da4..6c0a3c8 100644
--- a/src/faceExpressionNet/FaceExpressionNet.ts
+++ b/src/faceExpressionNet/FaceExpressionNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
diff --git a/src/faceFeatureExtractor/FaceFeatureExtractor.ts b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
index 199da4c..159356a 100644
--- a/src/faceFeatureExtractor/FaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
index a411935..9d6ecca 100644
--- a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceFeatureExtractor/denseBlock.ts b/src/faceFeatureExtractor/denseBlock.ts
index 92728ca..0b9288b 100644
--- a/src/faceFeatureExtractor/denseBlock.ts
+++ b/src/faceFeatureExtractor/denseBlock.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams, SeparableConvParams } from '../common';
 import { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';
diff --git a/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts b/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
index c20b682..ab8e93e 100644
--- a/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
+++ b/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { disposeUnusedWeightTensors, ParamMapping } from '../common';
 import { loadParamsFactory } from './loadParamsFactory';
diff --git a/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts b/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
index 2c61f58..0a3f216 100644
--- a/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
+++ b/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { disposeUnusedWeightTensors, ParamMapping } from '../common';
 import { loadParamsFactory } from './loadParamsFactory';
diff --git a/src/faceFeatureExtractor/types.ts b/src/faceFeatureExtractor/types.ts
index 338b3da..2cb136a 100644
--- a/src/faceFeatureExtractor/types.ts
+++ b/src/faceFeatureExtractor/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput, TNetInput } from '..';
 import { ConvParams, SeparableConvParams } from '../common';
diff --git a/src/faceLandmarkNet/FaceLandmark68NetBase.ts b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
index 10c5356..92de6ff 100644
--- a/src/faceLandmarkNet/FaceLandmark68NetBase.ts
+++ b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { IDimensions, Point } from '../classes';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
diff --git a/src/faceProcessor/FaceProcessor.ts b/src/faceProcessor/FaceProcessor.ts
index 2c674b2..76ffbc2 100644
--- a/src/faceProcessor/FaceProcessor.ts
+++ b/src/faceProcessor/FaceProcessor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { NetInput } from '../dom';
diff --git a/src/faceProcessor/extractParamsFromWeigthMap.ts b/src/faceProcessor/extractParamsFromWeigthMap.ts
index 30c923f..02a2b64 100644
--- a/src/faceProcessor/extractParamsFromWeigthMap.ts
+++ b/src/faceProcessor/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';
 import { NetParams } from './types';
diff --git a/src/faceProcessor/util.ts b/src/faceProcessor/util.ts
index 4fc839b..6f44e2b 100644
--- a/src/faceProcessor/util.ts
+++ b/src/faceProcessor/util.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 export function seperateWeightMaps(weightMap: tf.NamedTensorMap) {
 
diff --git a/src/faceRecognitionNet/FaceRecognitionNet.ts b/src/faceRecognitionNet/FaceRecognitionNet.ts
index 9e03c62..2681261 100644
--- a/src/faceRecognitionNet/FaceRecognitionNet.ts
+++ b/src/faceRecognitionNet/FaceRecognitionNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceRecognitionNet/convLayer.ts b/src/faceRecognitionNet/convLayer.ts
index fff3f53..8f0bc94 100644
--- a/src/faceRecognitionNet/convLayer.ts
+++ b/src/faceRecognitionNet/convLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { scale } from './scaleLayer';
 import { ConvLayerParams } from './types';
diff --git a/src/faceRecognitionNet/extractParams.ts b/src/faceRecognitionNet/extractParams.ts
index 24600cc..9291256 100644
--- a/src/faceRecognitionNet/extractParams.ts
+++ b/src/faceRecognitionNet/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';
 import { isFloat } from '../utils';
diff --git a/src/faceRecognitionNet/extractParamsFromWeigthMap.ts b/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
index 04c5bff..f935178 100644
--- a/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
+++ b/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';
 import { isTensor2D } from '../utils';
diff --git a/src/faceRecognitionNet/residualLayer.ts b/src/faceRecognitionNet/residualLayer.ts
index edc7d8a..e1404bb 100644
--- a/src/faceRecognitionNet/residualLayer.ts
+++ b/src/faceRecognitionNet/residualLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { conv, convDown, convNoRelu } from './convLayer';
 import { ResidualLayerParams } from './types';
diff --git a/src/faceRecognitionNet/scaleLayer.ts b/src/faceRecognitionNet/scaleLayer.ts
index d85f3de..3f9c0c6 100644
--- a/src/faceRecognitionNet/scaleLayer.ts
+++ b/src/faceRecognitionNet/scaleLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ScaleLayerParams } from './types';
 
diff --git a/src/faceRecognitionNet/types.ts b/src/faceRecognitionNet/types.ts
index e6fe12b..fffc5c0 100644
--- a/src/faceRecognitionNet/types.ts
+++ b/src/faceRecognitionNet/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from '../common';
 
diff --git a/src/globalApi/DetectFaceLandmarksTasks.ts b/src/globalApi/DetectFaceLandmarksTasks.ts
index 827f516..bb6b72c 100644
--- a/src/globalApi/DetectFaceLandmarksTasks.ts
+++ b/src/globalApi/DetectFaceLandmarksTasks.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { extractFaces, extractFaceTensors, TNetInput } from '../dom';
diff --git a/src/globalApi/PredictAgeAndGenderTask.ts b/src/globalApi/PredictAgeAndGenderTask.ts
index def31c1..2702abd 100644
--- a/src/globalApi/PredictAgeAndGenderTask.ts
+++ b/src/globalApi/PredictAgeAndGenderTask.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { AgeAndGenderPrediction } from '../ageGenderNet/types';
 import { TNetInput } from '../dom';
diff --git a/src/globalApi/PredictFaceExpressionsTask.ts b/src/globalApi/PredictFaceExpressionsTask.ts
index 5eb7732..5cfe58a 100644
--- a/src/globalApi/PredictFaceExpressionsTask.ts
+++ b/src/globalApi/PredictFaceExpressionsTask.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { TNetInput } from '../dom';
 import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
diff --git a/src/globalApi/extractFacesAndComputeResults.ts b/src/globalApi/extractFacesAndComputeResults.ts
index 468b8c1..e51f8e6 100644
--- a/src/globalApi/extractFacesAndComputeResults.ts
+++ b/src/globalApi/extractFacesAndComputeResults.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FaceDetection } from '../classes/FaceDetection';
 import { extractFaces, extractFaceTensors, TNetInput } from '../dom';
diff --git a/src/index.ts b/src/index.ts
index 6b81dcf..b540da3 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -1,7 +1,7 @@
 // const os = require('os');
 // const process = require('process');
 
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 import * as draw from './draw';
 import * as utils from './utils';
 export { draw, utils, tf };
@@ -26,4 +26,4 @@ export * from './resizeResults';
 import * as pkg from '../package.json';
 const node = typeof process !== 'undefined' ? process.version : false
 const browser = typeof navigator !== 'undefined' ? navigator.userAgent : false
-export const version = { faceapi: pkg.version, node, browser, tfjs: tf.version_core, backend: tf.getBackend() };
+export const version = { faceapi: pkg.version, node, browser };
diff --git a/src/ops/normalize.ts b/src/ops/normalize.ts
index 5360143..189f42a 100644
--- a/src/ops/normalize.ts
+++ b/src/ops/normalize.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
   return tf.tidy(() => {
diff --git a/src/ops/padToSquare.ts b/src/ops/padToSquare.ts
index 9731298..fffe323 100644
--- a/src/ops/padToSquare.ts
+++ b/src/ops/padToSquare.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 /**
  * Pads the smaller dimension of an image tensor with zeros, such that width === height.
diff --git a/src/ssdMobilenetv1/SsdMobilenetv1.ts b/src/ssdMobilenetv1/SsdMobilenetv1.ts
index 6457a91..b148353 100644
--- a/src/ssdMobilenetv1/SsdMobilenetv1.ts
+++ b/src/ssdMobilenetv1/SsdMobilenetv1.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
diff --git a/src/ssdMobilenetv1/boxPredictionLayer.ts b/src/ssdMobilenetv1/boxPredictionLayer.ts
index ff30649..b1c5faa 100644
--- a/src/ssdMobilenetv1/boxPredictionLayer.ts
+++ b/src/ssdMobilenetv1/boxPredictionLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { convLayer } from '../common';
 import { BoxPredictionParams } from './types';
diff --git a/src/ssdMobilenetv1/extractParams.ts b/src/ssdMobilenetv1/extractParams.ts
index d30c731..d3b1533 100644
--- a/src/ssdMobilenetv1/extractParams.ts
+++ b/src/ssdMobilenetv1/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';
 import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
diff --git a/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts b/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
index daa3f4b..ba6b3c5 100644
--- a/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
+++ b/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';
 import { isTensor3D } from '../utils';
diff --git a/src/ssdMobilenetv1/mobileNetV1.ts b/src/ssdMobilenetv1/mobileNetV1.ts
index 254324c..b82c6d2 100644
--- a/src/ssdMobilenetv1/mobileNetV1.ts
+++ b/src/ssdMobilenetv1/mobileNetV1.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { pointwiseConvLayer } from './pointwiseConvLayer';
 import { MobileNetV1 } from './types';
diff --git a/src/ssdMobilenetv1/outputLayer.ts b/src/ssdMobilenetv1/outputLayer.ts
index 80863f6..3508d1e 100644
--- a/src/ssdMobilenetv1/outputLayer.ts
+++ b/src/ssdMobilenetv1/outputLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { OutputLayerParams } from './types';
 
diff --git a/src/ssdMobilenetv1/pointwiseConvLayer.ts b/src/ssdMobilenetv1/pointwiseConvLayer.ts
index f193136..b5d39dd 100644
--- a/src/ssdMobilenetv1/pointwiseConvLayer.ts
+++ b/src/ssdMobilenetv1/pointwiseConvLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { PointwiseConvParams } from './types';
 
diff --git a/src/ssdMobilenetv1/predictionLayer.ts b/src/ssdMobilenetv1/predictionLayer.ts
index 43aafac..12a5bc1 100644
--- a/src/ssdMobilenetv1/predictionLayer.ts
+++ b/src/ssdMobilenetv1/predictionLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { boxPredictionLayer } from './boxPredictionLayer';
 import { pointwiseConvLayer } from './pointwiseConvLayer';
diff --git a/src/ssdMobilenetv1/types.ts b/src/ssdMobilenetv1/types.ts
index c5c0afe..957ad44 100644
--- a/src/ssdMobilenetv1/types.ts
+++ b/src/ssdMobilenetv1/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from '../common';
 
diff --git a/src/tinyFaceDetector/TinyFaceDetector.ts b/src/tinyFaceDetector/TinyFaceDetector.ts
index 09cb6a1..c453eb4 100644
--- a/src/tinyFaceDetector/TinyFaceDetector.ts
+++ b/src/tinyFaceDetector/TinyFaceDetector.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common';
diff --git a/src/tinyYolov2/TinyYolov2.ts b/src/tinyYolov2/TinyYolov2.ts
index 725c55b..b739b27 100644
--- a/src/tinyYolov2/TinyYolov2.ts
+++ b/src/tinyYolov2/TinyYolov2.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common/types';
diff --git a/src/tinyYolov2/TinyYolov2Base.ts b/src/tinyYolov2/TinyYolov2Base.ts
index 2058f6e..9dc38c6 100644
--- a/src/tinyYolov2/TinyYolov2Base.ts
+++ b/src/tinyYolov2/TinyYolov2Base.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { BoundingBox } from '../classes/BoundingBox';
 import { Dimensions } from '../classes/Dimensions';
diff --git a/src/tinyYolov2/convWithBatchNorm.ts b/src/tinyYolov2/convWithBatchNorm.ts
index 3e3d6f8..3d24894 100644
--- a/src/tinyYolov2/convWithBatchNorm.ts
+++ b/src/tinyYolov2/convWithBatchNorm.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { leaky } from './leaky';
 import { ConvWithBatchNorm } from './types';
diff --git a/src/tinyYolov2/depthwiseSeparableConv.ts b/src/tinyYolov2/depthwiseSeparableConv.ts
index a39a32a..5e0f6ba 100644
--- a/src/tinyYolov2/depthwiseSeparableConv.ts
+++ b/src/tinyYolov2/depthwiseSeparableConv.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { SeparableConvParams } from '../common/types';
 import { leaky } from './leaky';
diff --git a/src/tinyYolov2/extractParams.ts b/src/tinyYolov2/extractParams.ts
index cf31da1..1afc5cb 100644
--- a/src/tinyYolov2/extractParams.ts
+++ b/src/tinyYolov2/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { extractConvParamsFactory } from '../common';
 import { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
diff --git a/src/tinyYolov2/extractParamsFromWeigthMap.ts b/src/tinyYolov2/extractParamsFromWeigthMap.ts
index a18458e..b4e1a24 100644
--- a/src/tinyYolov2/extractParamsFromWeigthMap.ts
+++ b/src/tinyYolov2/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from '../common';
 import { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';
diff --git a/src/tinyYolov2/leaky.ts b/src/tinyYolov2/leaky.ts
index 6f84960..b7ac6da 100644
--- a/src/tinyYolov2/leaky.ts
+++ b/src/tinyYolov2/leaky.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 export function leaky(x: tf.Tensor4D): tf.Tensor4D {
   return tf.tidy(() => {
diff --git a/src/tinyYolov2/types.ts b/src/tinyYolov2/types.ts
index abf6425..d17a1d0 100644
--- a/src/tinyYolov2/types.ts
+++ b/src/tinyYolov2/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams } from '../common';
 import { SeparableConvParams } from '../common/types';
diff --git a/src/utils/index.ts b/src/utils/index.ts
index a49a603..1e48d78 100644
--- a/src/utils/index.ts
+++ b/src/utils/index.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { Point } from '../classes';
 import { Dimensions, IDimensions } from '../classes/Dimensions';
diff --git a/src/xception/TinyXception.ts b/src/xception/TinyXception.ts
index b2fd717..07cbc46 100644
--- a/src/xception/TinyXception.ts
+++ b/src/xception/TinyXception.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import { ConvParams, depthwiseSeparableConv } from '../common';
 import { NetInput, TNetInput, toNetInput } from '../dom';
diff --git a/src/xception/extractParamsFromWeigthMap.ts b/src/xception/extractParamsFromWeigthMap.ts
index bd8dc45..622071c 100644
--- a/src/xception/extractParamsFromWeigthMap.ts
+++ b/src/xception/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs-core';
+import * as tf from '@tensorflow/tfjs';
 
 import {
   disposeUnusedWeightTensors,