diff --git a/README.md b/README.md
index 67a191a..0d76c48 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,6 @@
 ## Note
 
 This is updated **face-api.js** with latest available TensorFlow/JS as the original face-api.js is not compatible with **tfjs 2.0+**.  
-Currently based on **TFJS-Core 2.4.0**.  
 
 Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020  
 
@@ -17,12 +16,13 @@ Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020
 - Compatible with TensorFlow/JS 2.0+  
 - Updated type casting for TypeScript type checking
 - Removed unnecesary package dependencies (karma, jasmine, etc.)  
-- Typescript build process now targets ES2017 and instead of dual ES5/ES6  
+- Typescript build process now targets ES2018 and instead of dual ES5/ES6  
 - Browser bundle process uses ESBuild instead of Rollup
 - New TensorFlow/JS dependencies since backends were removed from @tensorflow/tfjs-core
 - Updated mobileNetv1 model due to batchNorm() dependency
 - Fully tree shakable when imported as an ESM module
-- Added `version` class that returns JSON objecgt with version of FaceAPI as well as linked TFJS
+- Added `version` class that returns JSON object with version of FaceAPI as well as linked TFJS
+- Added calls for `setPlatform` to automatically prepare TFJS in browser
 - Removed following models as they are either obsolete or non-functional with tfjs 2.0+
   - mtcnn: Mostly obsolete
   - tinyYolov2: Non-functional since weights are missing
@@ -32,48 +32,63 @@ Due to reduced code and changed build process, resulting bundle is about **>5x s
 
 ## Installation
 
-**Imporant!**: This version of **face-api** does not embedd full version of **TensorFlow/JS (tfjs)** to keep package as small as possible (322KB minified), enable dynamic loading of different tfjs backends as well as to enable reusability of tfjs for different purposes.  
+There are several ways to use Face-API: 
 
-*Load tfjs explicitly before loading face-api.*  
-*Note: package `@tensorflow/tfjs` is bundle, if you want to keep your project small, import `@tensorflow/tfjs-core` plus a specific backend such as `@tensorflow/tfjs-backend-cpu`, `@tensorflow/tfjs-backend-webgl` or `@tensorflow/tfjs-node`*
+### IIFE script
+  *Size: 936KB minified*
 
-For example as a script:
+  This is simplest way for usage within Browser as it includes full version of TensorFlow/JS prepackaged with no external dependencies.  
+  Simply include this in your `HTML` file and it's ready to use.
 
-```html
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.3.0/tf.es2017.js"></script>
-  <script src="/dist/face-api.js"></script> <!-- full version / 960KB -->
-  or
-  <script src="/dist/face-api.min.js"></script> <!-- minified version / 320KB -->
-```
+  ```html
+  <script src="dist/face-api.js"><script>
+  ``` 
 
-Or install a module:
+  IIFE script auto-registers global namespace `faceapi` within Window object.  
+  And if you want to access `TensorFlow/JS` classes directly, they are exported as `faceapi.tf`
 
-```bash
-  npm install @tensorflow/tfjs @vladmandic/face-api
-```
+  Pre-packaged version of `TFJS` is **2.6.0**
 
-Use module using `require` (recommended for VanillaJS):
+### ESM module
+  *Size: 164KB non-minified*
 
-```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api');
-```
+  If you're using bundler *(such as rollup, webpack, esbuild)* to package your client application, you can import ESM version of FaceAPI which supports full tree shaking  
+  Note that this version does NOT pre-package `TFJS`, so you'll need to include it before you import `FaceAPI`  
+  You can use any version of `TFJS` 2.0+  
 
-Use module in a JavaScript project using `import`:  
-(NodeJS requires `"type": "module"` inside `package.json` to support `import` statements)
+  ```js
+    import * as tf from 'https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.6.0/tf.min.js'; // load directly from CDN
+    import * as faceapi from 'dist/face-api.esm.js';
+  ```
+  *Experimental*:  
+  You could use same syntax within your main `JS` file if it's imported with `<script type="module">`  
 
-```js
-import tf from '@tensorflow/tfjs';
-import faceapi from '@vladmandic/face-api';
-```
+  ```html
+    <script src="tf.min.js">
+    <script src="./index.js" type="module">
+  ```
+  and then in `index.js`
 
-Use module in a TypeScript project:  
-(TSC will compile this to `require` statements)
+  ```js
+    import * as tf from 'https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.6.0/tf.min.js'; // load directly from CDN
+    import * as faceapi from 'dist/face-api.esm.js';
+  ```
 
-```js
-import * as tf from '@tensorflow/tfjs';
-import * as faceapi from '@vladmandic/face-api';
-```
+### NPM module
+  *Size: 45,104KB unpacked (including sources and pre-trained model weights)*
+
+  Simmilar to ESM module, but with full sources as it points to `build/src/index.js` instead  
+  Recommended for NodeJS projects
+
+  Install with:
+  ```shell
+    npm install @tensorflow/tfjs @vladmandic/face-api 
+  ```
+  And then use with:
+  ```js
+    import * as tf from '@tensorflow/tfjs';
+    import * as faceapi from '@vladmandic/face-api';
+  ```
 
 ## Weights
 
@@ -81,20 +96,25 @@ Pretrained models and their weights are includes in `./model`.
 
 ## Build
 
-Included in `./dist` are:
-
-- face-api.cjs: CJS format, used by NodeJS import/require (default for node and browser require/import)
-- face-api.ejs: ESM format, used by Browser (provided as an alternative)
-- face-api.js:  IIFE format, used by Browser (default for browser script)
-- face-api.min.js:  Minified IIFE format, used by Browser
-
-If you want to do a full rebuild use:
-
+If you want to do a full rebuild, either download npm module
 ```shell
+npm install @vladmandic/face-api
+cd node_modules/@vladmandic/face-api
+```
+
+or clone a git project
+```shell
+git clone https://github.com/vladmandic/face-api
+cd face-api
+```
+
+Then install all dependencies and run rebuild:
+```shell
+npm install
 npm run build
 ```
 
-Which will compile everything in `./src` into `./build` and create both standard and minified bundles as well as a sourcemap in `./dist`
+Which will compile everything in `./src` into `./build` and create both ESM (standard) and IIFE (minified) bundles as well as sourcemaps in `./dist`
 
 ## Documentation
 
diff --git a/build/package.json b/build/package.json
index 40a7962..e75fee2 100644
--- a/build/package.json
+++ b/build/package.json
@@ -3,21 +3,16 @@
     "version": "0.5.3",
     "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
     "main": "build/src/index.js",
-    "browser": "dist/face-api.esm.js",
-    "typings": "./build/src/index.d.ts",
+    "browser": "dist/face-api.js",
+    "typings": "build/src/index.d.ts",
     "engines": {
         "node": ">=12.0.0"
     },
     "type": "module",
     "scripts": {
-        "compile": "tsc",
-        "clean": "rimraf build/ dist/",
-        "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"
+        "build-esm": "esbuild --bundle --format=esm --target=esnext --platform=browser --sourcemap --outfile=./dist/face-api.esm.js --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "build-iife": "esbuild --bundle --format=iife --target=esnext --platform=browser --sourcemap --outfile=./dist/face-api.js --global-name=faceapi --minify --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+        "build": "rimraf build/ dist/ && tsc && npm run build-esm && npm run build-iife"
     },
     "keywords": [
         "tensorflow",
@@ -45,11 +40,11 @@
         "@tensorflow/tfjs": "^2.6.0"
     },
     "devDependencies": {
-        "@types/node": "^14.11.7",
+        "@types/node": "^14.11.8",
         "esbuild": "^0.6.34",
         "rimraf": "^3.0.2",
         "ts-node": "^9.0.0",
-        "tslib": "^2.0.2",
-        "typescript": "^4.1.0-dev.20201008"
+        "tslib": "^2.0.3",
+        "typescript": "^4.1.0-dev.20201011"
     }
 }
diff --git a/build/src/NeuralNetwork.d.ts.map b/build/src/NeuralNetwork.d.ts.map
index 58a5ae3..4c1621c 100644
--- a/build/src/NeuralNetwork.d.ts.map
+++ b/build/src/NeuralNetwork.d.ts.map
@@ -1 +1 @@
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diff --git a/build/src/NeuralNetwork.js.map b/build/src/NeuralNetwork.js.map
index 6cd6e84..90cc3ec 100644
--- a/build/src/NeuralNetwork.js.map
+++ b/build/src/NeuralNetwork.js.map
@@ -1 +1 @@
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+{"version":3,"file":"NeuralNetwork.js","sourceRoot":"","sources":["../../src/NeuralNetwork.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,kBAAkB,CAAC;AAGvC,OAAO,EAAE,YAAY,EAAE,MAAM,uBAAuB,CAAC;AACrD,OAAO,EAAE,aAAa,EAAE,MAAM,OAAO,CAAC;AACtC,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAE5B,MAAM,OAAgB,aAAa;IAKjC,YAAsB,KAAa;QAAb,UAAK,GAAL,KAAK,CAAQ;QAHzB,YAAO,GAA2B,SAAS,CAAA;QAC3C,mBAAc,GAAmB,EAAE,CAAA;QAG3C;;;;;;;UAOE;IACJ,CAAC;IAED,IAAW,MAAM,KAA6B,OAAO,IAAI,CAAC,OAAO,CAAA,CAAC,CAAC;IACnE,IAAW,aAAa,KAAqB,OAAO,IAAI,CAAC,cAAc,CAAA,CAAC,CAAC;IACzE,IAAW,QAAQ,KAAc,OAAO,CAAC,CAAC,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IAEhD,gBAAgB,CAAC,SAAiB;QACvC,MAAM,EAAE,GAAG,EAAE,OAAO,EAAE,GAAG,IAAI,CAAC,oBAAoB,CAAC,SAAS,CAAC,CAAA;QAC7D,OAAO,GAAG,CAAC,OAAO,CAAC,CAAA;IACrB,CAAC;IAEM,qBAAqB,CAAC,SAAiB,EAAE,MAAiB;QAC/D,MAAM,EAAE,GAAG,EAAE,OAAO,EAAE,GAAG,IAAI,CAAC,oBAAoB,CAAC,SAAS,CAAC,CAAA;QAC7D,GAAG,CAAC,OAAO,CAAC,CAAC,OAAO,EAAE,CAAA;QACtB,GAAG,CAAC,OAAO,CAAC,GAAG,MAAM,CAAA;IACvB,CAAC;IAEM,YAAY;QACjB,OAAO,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,CAAC,EAAE,SAAS,EAAE,EAAE,EAAE,CAAC,CAAC;YACjD,IAAI,EAAE,SAAS;YACf,MAAM,EAAE,IAAI,CAAC,gBAAgB,CAAC,SAAS,CAAC;SACzC,CAAC,CAAC,CAAA;IACL,CAAC;IAEM,kBAAkB;QACvB,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,EAAE,CAAC,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,CAAC,CAAA;IACjF,CAAC;IAEM,eAAe;QACpB,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAA;IACpF,CAAC;IAEM,QAAQ;QACb,IAAI,CAAC,eAAe,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,IAAI,EAAE,MAAM,EAAE,EAAE,EAAE;YAClD,IAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,MAAM,CAAC,QAAQ,EAAE,CAAC,CAAA;QACrD,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,MAAM;QACX,IAAI,CAAC,kBAAkB,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,IAAI,EAAE,MAAM,EAAE,QAAQ,EAAE,EAAE,EAAE;YAC/D,MAAM,MAAM,GAAG,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,QAAQ,EAAE,CAAC,CAAA;YAC7C,QAAQ,CAAC,OAAO,EAAE,CAAA;YAClB,IAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,MAAM,CAAC,CAAA;QAC1C,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,OAAO,CAAC,mBAA4B,IAAI;QAC7C,IAAI,CAAC,YAAY,EAAE,CAAC,OAAO,CAAC,KAAK,CAAC,EAAE;YAClC,IAAI,gBAAgB,IAAI,KAAK,CAAC,MAAM,CAAC,UAAU,EAAE;gBAC/C,MAAM,IAAI,KAAK,CAAC,mDAAmD,KAAK,CAAC,IAAI,EAAE,CAAC,CAAA;aACjF;YACD,KAAK,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;QACxB,CAAC,CAAC,CAAA;QACF,IAAI,CAAC,OAAO,GAAG,SAAS,CAAA;IAC1B,CAAC;IAEM,eAAe;QACpB,OAAO,IAAI,YAAY,CACrB,IAAI,CAAC,YAAY,EAAE;aAChB,GAAG,CAAC,CAAC,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC,QAAQ,EAAE,CAAa,CAAC;aAC9D,MAAM,CAAC,CAAC,IAAI,EAAE,GAAG,EAAE,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,CAC3C,CAAA;IACH,CAAC;IAEM,KAAK,CAAC,IAAI,CAAC,YAA+C;QAC/D,IAAI,YAAY,YAAY,YAAY,EAAE;YACxC,IAAI,CAAC,cAAc,CAAC,YAAY,CAAC,CAAA;YACjC,OAAM;SACP;QAED,MAAM,IAAI,CAAC,WAAW,CAAC,YAAY,CAAC,CAAA;IACtC,CAAC;IAEM,KAAK,CAAC,WAAW,CAAC,GAAuB;QAC9C,IAAI,GAAG,IAAI,OAAO,GAAG,KAAK,QAAQ,EAAE;YAClC,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,mCAAmC,CAAC,CAAA;SAClE;QAED,MAAM,SAAS,GAAG,MAAM,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,mBAAmB,EAAE,CAAC,CAAA;QACtE,IAAI,CAAC,iBAAiB,CAAC,SAAS,CAAC,CAAA;IACnC,CAAC;IAEM,KAAK,CAAC,YAAY,CAAC,QAA4B;QACpD,IAAI,QAAQ,IAAI,OAAO,QAAQ,KAAK,QAAQ,EAAE;YAC5C,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,0CAA0C,CAAC,CAAA;SACzE;QAED,MAAM,EAAE,QAAQ,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;QAEjC,MAAM,EAAE,WAAW,EAAE,YAAY,EAAE,GAAG,YAAY,CAAC,QAAQ,EAAE,IAAI,CAAC,mBAAmB,EAAE,CAAC,CAAA;QAExF,MAAM,oBAAoB,GAAG,CAAC,SAAmB,EAAE,EAAE,CAAC,OAAO,CAAC,GAAG,CAC/D,SAAS,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CACtE,CAAA;QACD,MAAM,WAAW,GAAG,EAAE,CAAC,EAAE,CAAC,oBAAoB,CAAC,oBAAoB,CAAC,CAAA;QACpE,MAAM,QAAQ,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,MAAM,QAAQ,CAAC,WAAW,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAA;QACrE,MAAM,SAAS,GAAG,MAAM,WAAW,CAAC,QAAQ,EAAE,YAAY,CAAC,CAAA;QAE3D,IAAI,CAAC,iBAAiB,CAAC,SAAS,CAAC,CAAA;IACnC,CAAC;IAEM,iBAAiB,CAAC,SAA4B;QACnD,MAAM,EACJ,aAAa,EACb,MAAM,EACP,GAAG,IAAI,CAAC,0BAA0B,CAAC,SAAS,CAAC,CAAA;QAE9C,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;QACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;IACvB,CAAC;IAEM,cAAc,CAAC,OAAqB;QACzC,MAAM,EACJ,aAAa,EACb,MAAM,EACP,GAAG,IAAI,CAAC,aAAa,CAAC,OAAO,CAAC,CAAA;QAE/B,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;QACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;IACvB,CAAC;IAEO,oBAAoB,CAAC,SAAiB;QAC5C,IAAI,CAAC,IAAI,CAAC,MAAM,EAAE;YAChB,MAAM,IAAI,KAAK,CAAC,mDAAmD,CAAC,CAAA;SACrE;QAED,MAAM,MAAM,GAAG,SAAS,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,CAAC,GAAkD,EAAE,OAAO,EAAE,EAAE;YACzG,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,cAAc,CAAC,OAAO,CAAC,EAAE;gBACxC,MAAM,IAAI,KAAK,CAAC,wDAAwD,OAAO,cAAc,SAAS,EAAE,CAAC,CAAA;aAC1G;YAED,OAAO,EAAE,GAAG,EAAE,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,EAAE,GAAG,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE,CAAA;QACrE,CAAC,EAAE,EAAE,OAAO,EAAE,IAAI,CAAC,MAAM,EAAE,CAAC,CAAA;QAE5B,MAAM,EAAE,GAAG,EAAE,OAAO,EAAE,GAAG,MAAM,CAAA;QAC/B,IAAI,CAAC,GAAG,IAAI,CAAC,OAAO,IAAI,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,EAAE;YAC5D,MAAM,IAAI,KAAK,CAAC,8DAA8D,SAAS,EAAE,CAAC,CAAA;SAC3F;QAED,OAAO,EAAE,GAAG,EAAE,OAAO,EAAE,CAAA;IACzB,CAAC;CAKF"}
\ No newline at end of file
diff --git a/build/src/Platform.d.ts b/build/src/Platform.d.ts
new file mode 100644
index 0000000..b230a31
--- /dev/null
+++ b/build/src/Platform.d.ts
@@ -0,0 +1,8 @@
+export declare class PlatformBrowser {
+    private textEncoder;
+    fetch(path: string, init?: RequestInit): Promise<Response>;
+    now(): number;
+    encode(text: string, encoding: string): Uint8Array;
+    decode(bytes: Uint8Array, encoding: string): string;
+}
+//# sourceMappingURL=Platform.d.ts.map
\ No newline at end of file
diff --git a/build/src/Platform.d.ts.map b/build/src/Platform.d.ts.map
new file mode 100644
index 0000000..e31d822
--- /dev/null
+++ b/build/src/Platform.d.ts.map
@@ -0,0 +1 @@
+{"version":3,"file":"Platform.d.ts","sourceRoot":"","sources":["../../src/Platform.ts"],"names":[],"mappings":"AAAA,qBAAa,eAAe;IAC1B,OAAO,CAAC,WAAW,CAAc;IAEjC,KAAK,CAAC,IAAI,EAAE,MAAM,EAAE,IAAI,CAAC,EAAE,WAAW,GAAG,OAAO,CAAC,QAAQ,CAAC;IAI1D,GAAG,IAAI,MAAM;IAIb,MAAM,CAAC,IAAI,EAAE,MAAM,EAAE,QAAQ,EAAE,MAAM,GAAG,UAAU;IAUlD,MAAM,CAAC,KAAK,EAAE,UAAU,EAAE,QAAQ,EAAE,MAAM,GAAG,MAAM;CAGpD"}
\ No newline at end of file
diff --git a/build/src/Platform.js b/build/src/Platform.js
new file mode 100644
index 0000000..6256f3e
--- /dev/null
+++ b/build/src/Platform.js
@@ -0,0 +1,21 @@
+export class PlatformBrowser {
+    fetch(path, init) {
+        return fetch(path, init);
+    }
+    now() {
+        return performance.now();
+    }
+    encode(text, encoding) {
+        if (encoding !== 'utf-8' && encoding !== 'utf8') {
+            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
+        }
+        if (this.textEncoder == null) {
+            this.textEncoder = new TextEncoder();
+        }
+        return this.textEncoder.encode(text);
+    }
+    decode(bytes, encoding) {
+        return new TextDecoder(encoding).decode(bytes);
+    }
+}
+//# sourceMappingURL=Platform.js.map
\ No newline at end of file
diff --git a/build/src/Platform.js.map b/build/src/Platform.js.map
new file mode 100644
index 0000000..c9ff9aa
--- /dev/null
+++ b/build/src/Platform.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/build/src/index.d.ts b/build/src/index.d.ts
index 488aaf3..fa371d5 100644
--- a/build/src/index.d.ts
+++ b/build/src/index.d.ts
@@ -20,7 +20,7 @@ export * from './NeuralNetwork';
 export * from './resizeResults';
 export declare const version: {
     faceapi: string;
-    node: string | boolean;
-    browser: string | boolean;
+    node: boolean;
+    browser: boolean;
 };
 //# sourceMappingURL=index.d.ts.map
\ No newline at end of file
diff --git a/build/src/index.js b/build/src/index.js
index 086e893..9e2e5f1 100644
--- a/build/src/index.js
+++ b/build/src/index.js
@@ -21,7 +21,10 @@ export * from './euclideanDistance';
 export * from './NeuralNetwork';
 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;
+const node = (typeof process !== 'undefined');
+const browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');
 export const version = { faceapi: pkg.version, node, browser };
+import { PlatformBrowser } from './Platform';
+if (!tf.env().platform && tf.env().get('IS_BROWSER'))
+    tf.env().setPlatform('browser', new PlatformBrowser);
 //# 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 67d1bd1..bf1867b 100644
--- a/build/src/index.js.map
+++ b/build/src/index.js.map
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/dist/face-api.cjs.js b/dist/face-api.cjs.js
deleted file mode 100644
index ed48944..0000000
--- a/dist/face-api.cjs.js
+++ /dev/null
@@ -1,4345 +0,0 @@
-var __defineProperty = Object.defineProperty;
-var __hasOwnProperty = Object.prototype.hasOwnProperty;
-var __commonJS = (callback, module2) => () => {
-  if (!module2) {
-    module2 = {exports: {}};
-    callback(module2.exports, module2);
-  }
-  return module2.exports;
-};
-var __markAsModule = (target) => {
-  return __defineProperty(target, "__esModule", {value: true});
-};
-var __export = (target, all) => {
-  __markAsModule(target);
-  for (var name in all)
-    __defineProperty(target, name, {get: all[name], enumerable: true});
-};
-var __exportStar = (target, module2) => {
-  __markAsModule(target);
-  if (typeof module2 === "object" || typeof module2 === "function") {
-    for (let key in module2)
-      if (!__hasOwnProperty.call(target, key) && key !== "default")
-        __defineProperty(target, key, {get: () => module2[key], enumerable: true});
-  }
-  return target;
-};
-var __toModule = (module2) => {
-  if (module2 && module2.__esModule)
-    return module2;
-  return __exportStar(__defineProperty({}, "default", {value: module2, enumerable: true}), module2);
-};
-
-// build/src/env/isNodejs.js
-var require_isNodejs = __commonJS((exports2, module2) => {
-  __export(exports2, {
-    isNodejs: () => isNodejs3
-  });
-  function isNodejs3() {
-    return typeof global === "object" && true && typeof module2 !== "undefined" && typeof process !== "undefined" && !!process.version;
-  }
-});
-
-// build/src/draw/drawContour.js
-function drawContour(ctx, points, isClosed = false) {
-  ctx.beginPath();
-  points.slice(1).forEach(({x, y}, prevIdx) => {
-    const from = points[prevIdx];
-    ctx.moveTo(from.x, from.y);
-    ctx.lineTo(x, y);
-  });
-  if (isClosed) {
-    const from = points[points.length - 1];
-    const to = points[0];
-    if (!from || !to) {
-      return;
-    }
-    ctx.moveTo(from.x, from.y);
-    ctx.lineTo(to.x, to.y);
-  }
-  ctx.stroke();
-}
-
-// build/src/classes/Dimensions.js
-class Dimensions {
-  constructor(width, height) {
-    if (!isValidNumber(width) || !isValidNumber(height)) {
-      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width, height})}`);
-    }
-    this._width = width;
-    this._height = height;
-  }
-  get width() {
-    return this._width;
-  }
-  get height() {
-    return this._height;
-  }
-  reverse() {
-    return new Dimensions(1 / this.width, 1 / this.height);
-  }
-}
-
-// build/src/utils/index.js
-const utils_exports = {};
-__export(utils_exports, {
-  computeReshapedDimensions: () => computeReshapedDimensions,
-  getCenterPoint: () => getCenterPoint,
-  isDimensions: () => isDimensions,
-  isEven: () => isEven,
-  isFloat: () => isFloat,
-  isTensor: () => isTensor,
-  isTensor1D: () => isTensor1D,
-  isTensor2D: () => isTensor2D,
-  isTensor3D: () => isTensor3D,
-  isTensor4D: () => isTensor4D,
-  isValidNumber: () => isValidNumber,
-  isValidProbablitiy: () => isValidProbablitiy,
-  range: () => range,
-  round: () => round
-});
-const tf = __toModule(require("@tensorflow/tfjs"));
-function isTensor(tensor2, dim) {
-  return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim;
-}
-function isTensor1D(tensor2) {
-  return isTensor(tensor2, 1);
-}
-function isTensor2D(tensor2) {
-  return isTensor(tensor2, 2);
-}
-function isTensor3D(tensor2) {
-  return isTensor(tensor2, 3);
-}
-function isTensor4D(tensor2) {
-  return isTensor(tensor2, 4);
-}
-function isFloat(num) {
-  return num % 1 !== 0;
-}
-function isEven(num) {
-  return num % 2 === 0;
-}
-function round(num, prec = 2) {
-  const f = Math.pow(10, prec);
-  return Math.floor(num * f) / f;
-}
-function isDimensions(obj) {
-  return obj && obj.width && obj.height;
-}
-function computeReshapedDimensions({width, height}, inputSize) {
-  const scale2 = inputSize / Math.max(height, width);
-  return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
-}
-function getCenterPoint(pts) {
-  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
-}
-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;
-}
-function isValidProbablitiy(num) {
-  return isValidNumber(num) && 0 <= num && num <= 1;
-}
-
-// build/src/classes/Point.js
-class Point {
-  constructor(x, y) {
-    this._x = x;
-    this._y = y;
-  }
-  get x() {
-    return this._x;
-  }
-  get y() {
-    return this._y;
-  }
-  add(pt) {
-    return new Point(this.x + pt.x, this.y + pt.y);
-  }
-  sub(pt) {
-    return new Point(this.x - pt.x, this.y - pt.y);
-  }
-  mul(pt) {
-    return new Point(this.x * pt.x, this.y * pt.y);
-  }
-  div(pt) {
-    return new Point(this.x / pt.x, this.y / pt.y);
-  }
-  abs() {
-    return new Point(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 Point(Math.floor(this.x), Math.floor(this.y));
-  }
-}
-
-// build/src/classes/Box.js
-class Box {
-  constructor(_box, allowNegativeDimensions = true) {
-    const box = _box || {};
-    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
-    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
-    if (!isRect && !isBbox) {
-      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);
-    }
-    const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top];
-    Box.assertIsValidBox({x, y, width, height}, "Box.constructor", allowNegativeDimensions);
-    this._x = x;
-    this._y = y;
-    this._width = width;
-    this._height = height;
-  }
-  static isRect(rect) {
-    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
-  }
-  static assertIsValidBox(box, callee, allowNegativeDimensions = false) {
-    if (!Box.isRect(box)) {
-      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);
-    }
-    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
-      throw new Error(`${callee} - width (${box.width}) and height (${box.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 Point(this.left, this.top);
-  }
-  get topRight() {
-    return new Point(this.right, this.top);
-  }
-  get bottomLeft() {
-    return new Point(this.left, this.bottom);
-  }
-  get bottomRight() {
-    return new Point(this.right, this.bottom);
-  }
-  round() {
-    const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val));
-    return new Box({x, y, width, height});
-  }
-  floor() {
-    const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val));
-    return new Box({x, y, width, height});
-  }
-  toSquare() {
-    let {x, y, width, height} = this;
-    const diff = Math.abs(width - height);
-    if (width < height) {
-      x -= diff / 2;
-      width += diff;
-    }
-    if (height < width) {
-      y -= diff / 2;
-      height += diff;
-    }
-    return new Box({x, y, width, height});
-  }
-  rescale(s) {
-    const scaleX = isDimensions(s) ? s.width : s;
-    const scaleY = isDimensions(s) ? s.height : s;
-    return new Box({
-      x: this.x * scaleX,
-      y: this.y * scaleY,
-      width: this.width * scaleX,
-      height: this.height * scaleY
-    });
-  }
-  pad(padX, padY) {
-    let [x, y, width, height] = [
-      this.x - padX / 2,
-      this.y - padY / 2,
-      this.width + padX,
-      this.height + padY
-    ];
-    return new Box({x, y, width, height});
-  }
-  clipAtImageBorders(imgWidth, imgHeight) {
-    const {x, y, right, bottom} = this;
-    const clippedX = Math.max(x, 0);
-    const clippedY = Math.max(y, 0);
-    const newWidth = right - clippedX;
-    const newHeight = bottom - clippedY;
-    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);
-    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);
-    return new Box({x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight}).floor();
-  }
-  shift(sx, sy) {
-    const {width, height} = this;
-    const x = this.x + sx;
-    const y = this.y + sy;
-    return new Box({x, y, width, height});
-  }
-  padAtBorders(imageHeight, imageWidth) {
-    const w = this.width + 1;
-    const h = this.height + 1;
-    let dx = 1;
-    let dy = 1;
-    let edx = w;
-    let edy = h;
-    let x = this.left;
-    let y = this.top;
-    let ex = this.right;
-    let ey = this.bottom;
-    if (ex > imageWidth) {
-      edx = -ex + imageWidth + w;
-      ex = imageWidth;
-    }
-    if (ey > imageHeight) {
-      edy = -ey + imageHeight + h;
-      ey = imageHeight;
-    }
-    if (x < 1) {
-      edy = 2 - x;
-      x = 1;
-    }
-    if (y < 1) {
-      edy = 2 - y;
-      y = 1;
-    }
-    return {dy, edy, dx, edx, y, ey, x, ex, w, h};
-  }
-  calibrate(region) {
-    return new Box({
-      left: this.left + region.left * this.width,
-      top: this.top + region.top * this.height,
-      right: this.right + region.right * this.width,
-      bottom: this.bottom + region.bottom * this.height
-    }).toSquare().round();
-  }
-}
-
-// build/src/classes/BoundingBox.js
-class BoundingBox extends Box {
-  constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-    super({left, top, right, bottom}, allowNegativeDimensions);
-  }
-}
-
-// build/src/classes/ObjectDetection.js
-class ObjectDetection {
-  constructor(score, classScore, className, relativeBox, imageDims) {
-    this._imageDims = new Dimensions(imageDims.width, imageDims.height);
-    this._score = score;
-    this._classScore = classScore;
-    this._className = className;
-    this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse());
-  }
-  forSize(width, height) {
-    return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, {width, height});
-  }
-}
-
-// build/src/classes/FaceDetection.js
-class FaceDetection extends ObjectDetection {
-  constructor(score, relativeBox, imageDims) {
-    super(score, score, "", relativeBox, imageDims);
-  }
-  forSize(width, height) {
-    const {score, relativeBox, imageDims} = super.forSize(width, height);
-    return new FaceDetection(score, relativeBox, imageDims);
-  }
-}
-
-// build/src/ops/iou.js
-function iou(box1, box2, isIOU = true) {
-  const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
-  const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
-  const interSection = width * height;
-  return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area);
-}
-
-// build/src/ops/minBbox.js
-function minBbox(pts) {
-  const xs = pts.map((pt) => pt.x);
-  const ys = pts.map((pt) => pt.y);
-  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 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) {
-    const curr = indicesSortedByScore.pop();
-    pick.push(curr);
-    const indices = indicesSortedByScore;
-    const outputs = [];
-    for (let i = 0; i < indices.length; i++) {
-      const idx = indices[i];
-      const currBox = boxes[curr];
-      const idxBox = boxes[idx];
-      outputs.push(iou(currBox, idxBox, isIOU));
-    }
-    indicesSortedByScore = indicesSortedByScore.filter((_, j) => outputs[j] <= iouThreshold);
-  }
-  return pick;
-}
-
-// build/src/ops/normalize.js
-const tf2 = __toModule(require("@tensorflow/tfjs"));
-function normalize(x, meanRgb) {
-  return tf2.tidy(() => {
-    const [r, g, b] = meanRgb;
-    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 tf3.tidy(() => {
-    const [height, width] = imgTensor.shape.slice(1);
-    if (height === width) {
-      return imgTensor;
-    }
-    const dimDiff = Math.abs(height - width);
-    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
-    const paddingAxis = height > width ? 2 : 1;
-    const createPaddingTensor = (paddingAmount2) => {
-      const paddingTensorShape = imgTensor.shape.slice();
-      paddingTensorShape[paddingAxis] = paddingAmount2;
-      return tf3.fill(paddingTensorShape, 0);
-    };
-    const paddingTensorAppend = createPaddingTensor(paddingAmount);
-    const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
-    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;
-    const tensorsToStack = [
-      paddingTensorPrepend,
-      imgTensor,
-      paddingTensorAppend
-    ].filter((t) => !!t).map((t) => tf3.cast(t, "float32"));
-    return tf3.concat(tensorsToStack, paddingAxis);
-  });
-}
-
-// build/src/ops/shuffleArray.js
-function shuffleArray(inputArray) {
-  const array = inputArray.slice();
-  for (let i = array.length - 1; i > 0; i--) {
-    const j = Math.floor(Math.random() * (i + 1));
-    const x = array[i];
-    array[i] = array[j];
-    array[j] = x;
-  }
-  return array;
-}
-
-// build/src/ops/index.js
-function sigmoid(x) {
-  return 1 / (1 + Math.exp(-x));
-}
-function inverseSigmoid(x) {
-  return Math.log(x / (1 - x));
-}
-
-// build/src/classes/Rect.js
-class Rect extends Box {
-  constructor(x, y, width, height, allowNegativeDimensions = false) {
-    super({x, y, width, height}, allowNegativeDimensions);
-  }
-}
-
-// build/src/classes/FaceLandmarks.js
-const relX = 0.5;
-const relY = 0.43;
-const relScale = 0.45;
-class FaceLandmarks {
-  constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) {
-    const {width, height} = imgDims;
-    this._imgDims = new Dimensions(width, height);
-    this._shift = shift;
-    this._positions = relativeFaceLandmarkPositions.map((pt) => pt.mul(new Point(width, height)).add(shift));
-  }
-  get shift() {
-    return new Point(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((pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)));
-  }
-  forSize(width, height) {
-    return new this.constructor(this.relativePositions, {width, height});
-  }
-  shiftBy(x, y) {
-    return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
-  }
-  shiftByPoint(pt) {
-    return this.shiftBy(pt.x, pt.y);
-  }
-  align(detection, options = {}) {
-    if (detection) {
-      const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection);
-      return this.shiftBy(box.x, box.y).align(null, options);
-    }
-    const {useDlibAlignment, minBoxPadding} = Object.assign({}, {useDlibAlignment: false, minBoxPadding: 0.2}, options);
-    if (useDlibAlignment) {
-      return this.alignDlib();
-    }
-    return this.alignMinBbox(minBoxPadding);
-  }
-  alignDlib() {
-    const centers = this.getRefPointsForAlignment();
-    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;
-    const distToMouth = (pt) => mouthCenter.sub(pt).magnitude();
-    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
-    const size = Math.floor(eyeToMouthDist / relScale);
-    const refPoint = getCenterPoint(centers);
-    const x = Math.floor(Math.max(0, refPoint.x - relX * size));
-    const y = Math.floor(Math.max(0, refPoint.y - relY * size));
-    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
-  }
-  alignMinBbox(padding) {
-    const box = minBbox(this.positions);
-    return box.pad(box.width * padding, box.height * padding);
-  }
-  getRefPointsForAlignment() {
-    throw new Error("getRefPointsForAlignment not implemented by base class");
-  }
-}
-
-// build/src/classes/FaceLandmarks5.js
-class FaceLandmarks5 extends FaceLandmarks {
-  getRefPointsForAlignment() {
-    const pts = this.positions;
-    return [
-      pts[0],
-      pts[1],
-      getCenterPoint([pts[3], pts[4]])
-    ];
-  }
-}
-
-// build/src/classes/FaceLandmarks68.js
-class FaceLandmarks68 extends FaceLandmarks {
-  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(getCenterPoint);
-  }
-}
-
-// build/src/classes/FaceMatch.js
-class FaceMatch {
-  constructor(label, distance) {
-    this._label = label;
-    this._distance = distance;
-  }
-  get label() {
-    return this._label;
-  }
-  get distance() {
-    return this._distance;
-  }
-  toString(withDistance = true) {
-    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
-  }
-}
-
-// build/src/classes/LabeledBox.js
-class LabeledBox extends Box {
-  constructor(box, label) {
-    super(box);
-    this._label = label;
-  }
-  static assertIsValidLabeledBox(box, callee) {
-    Box.assertIsValidBox(box, callee);
-    if (!isValidNumber(box.label)) {
-      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);
-    }
-  }
-  get label() {
-    return this._label;
-  }
-}
-
-// build/src/classes/LabeledFaceDescriptors.js
-class LabeledFaceDescriptors {
-  constructor(label, descriptors) {
-    if (!(typeof label === "string")) {
-      throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");
-    }
-    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {
-      throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");
-    }
-    this._label = label;
-    this._descriptors = descriptors;
-  }
-  get label() {
-    return this._label;
-  }
-  get descriptors() {
-    return this._descriptors;
-  }
-  toJSON() {
-    return {
-      label: this.label,
-      descriptors: this.descriptors.map((d) => Array.from(d))
-    };
-  }
-  static fromJSON(json) {
-    const descriptors = json.descriptors.map((d) => {
-      return new Float32Array(d);
-    });
-    return new LabeledFaceDescriptors(json.label, descriptors);
-  }
-}
-
-// build/src/classes/PredictedBox.js
-class PredictedBox extends LabeledBox {
-  constructor(box, label, score, classScore) {
-    super(box, label);
-    this._score = score;
-    this._classScore = classScore;
-  }
-  static assertIsValidPredictedBox(box, callee) {
-    LabeledBox.assertIsValidLabeledBox(box, callee);
-    if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) {
-      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);
-    }
-  }
-  get score() {
-    return this._score;
-  }
-  get classScore() {
-    return this._classScore;
-  }
-}
-
-// build/src/classes/index.js
-
-// build/src/factories/WithFaceDetection.js
-function isWithFaceDetection(obj) {
-  return obj["detection"] instanceof FaceDetection;
-}
-function extendWithFaceDetection(sourceObj, detection) {
-  const extension = {detection};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/env/createBrowserEnv.js
-function createBrowserEnv() {
-  const fetch = window["fetch"] || function() {
-    throw new Error("fetch - missing fetch implementation for browser environment");
-  };
-  const readFile = 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,
-    readFile
-  };
-}
-
-// build/src/env/createFileSystem.js
-function createFileSystem(fs) {
-  let requireFsError = "";
-  if (!fs) {
-    try {
-      fs = require("fs");
-    } catch (err) {
-      requireFsError = err.toString();
-    }
-  }
-  const readFile = fs ? function(filePath) {
-    return new Promise((res, rej) => {
-      fs.readFile(filePath, function(err, buffer) {
-        return err ? rej(err) : res(buffer);
-      });
-    });
-  } : function() {
-    throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);
-  };
-  return {
-    readFile
-  };
-}
-
-// build/src/env/createNodejsEnv.js
-function createNodejsEnv() {
-  const Canvas = global["Canvas"] || global["HTMLCanvasElement"];
-  const Image = global["Image"] || global["HTMLImageElement"];
-  const createCanvasElement = function() {
-    if (Canvas) {
-      return new Canvas();
-    }
-    throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment");
-  };
-  const createImageElement = function() {
-    if (Image) {
-      return new Image();
-    }
-    throw new Error("createImageElement - missing Image implementation for nodejs environment");
-  };
-  const fetch = global["fetch"] || function() {
-    throw new Error("fetch - missing fetch implementation for nodejs environment");
-  };
-  const fileSystem = createFileSystem();
-  return {
-    Canvas: Canvas || class {
-    },
-    CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
-    },
-    Image: Image || class {
-    },
-    ImageData: global["ImageData"] || class {
-    },
-    Video: global["HTMLVideoElement"] || class {
-    },
-    createCanvasElement,
-    createImageElement,
-    fetch,
-    ...fileSystem
-  };
-}
-
-// build/src/env/isBrowser.js
-function isBrowser() {
-  return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
-}
-
-// build/src/env/types.js
-
-// build/src/env/index.js
-const isNodejs = __toModule(require_isNodejs());
-let environment;
-function getEnv() {
-  if (!environment) {
-    throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
-  }
-  return environment;
-}
-function setEnv(env16) {
-  environment = env16;
-}
-function initialize() {
-  if (isBrowser()) {
-    return setEnv(createBrowserEnv());
-  }
-  if (isNodejs.isNodejs()) {
-    return setEnv(createNodejsEnv());
-  }
-}
-function monkeyPatch(env16) {
-  if (!environment) {
-    initialize();
-  }
-  if (!environment) {
-    throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
-  }
-  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 env = {
-  getEnv,
-  setEnv,
-  initialize,
-  createBrowserEnv,
-  createFileSystem,
-  createNodejsEnv,
-  monkeyPatch,
-  isBrowser,
-  isNodejs: isNodejs.isNodejs
-};
-initialize();
-
-// build/src/dom/resolveInput.js
-function resolveInput(arg) {
-  if (!env.isNodejs() && typeof arg === "string") {
-    return document.getElementById(arg);
-  }
-  return arg;
-}
-
-// build/src/dom/getContext2dOrThrow.js
-function getContext2dOrThrow(canvasArg) {
-  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
-  if (canvasArg instanceof CanvasRenderingContext2D2) {
-    return canvasArg;
-  }
-  const canvas = resolveInput(canvasArg);
-  if (!(canvas instanceof Canvas)) {
-    throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");
-  }
-  const ctx = canvas.getContext("2d");
-  if (!ctx) {
-    throw new Error("resolveContext2d - canvas 2d context is null");
-  }
-  return ctx;
-}
-
-// build/src/draw/DrawTextField.js
-var AnchorPosition;
-(function(AnchorPosition2) {
-  AnchorPosition2["TOP_LEFT"] = "TOP_LEFT";
-  AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT";
-  AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT";
-  AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
-})(AnchorPosition || (AnchorPosition = {}));
-class DrawTextFieldOptions {
-  constructor(options = {}) {
-    const {anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding} = options;
-    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;
-    this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)";
-    this.fontColor = fontColor || "rgba(255, 255, 255, 1)";
-    this.fontSize = fontSize || 14;
-    this.fontStyle = fontStyle || "Georgia";
-    this.padding = padding || 4;
-  }
-}
-class DrawTextField {
-  constructor(text, anchor, options = {}) {
-    this.text = typeof text === "string" ? [text] : text instanceof DrawTextField ? text.text : text;
-    this.anchor = anchor;
-    this.options = new DrawTextFieldOptions(options);
-  }
-  measureWidth(ctx) {
-    const {padding} = this.options;
-    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding;
-  }
-  measureHeight() {
-    const {fontSize, padding} = this.options;
-    return this.text.length * fontSize + 2 * padding;
-  }
-  getUpperLeft(ctx, canvasDims) {
-    const {anchorPosition} = this.options;
-    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;
-    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;
-    const textFieldWidth = this.measureWidth(ctx);
-    const textFieldHeight = this.measureHeight();
-    const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x;
-    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;
-    if (canvasDims) {
-      const {width, height} = canvasDims;
-      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);
-      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);
-      return {x: newX, y: newY};
-    }
-    return {x, y};
-  }
-  draw(canvasArg) {
-    const canvas = resolveInput(canvasArg);
-    const ctx = getContext2dOrThrow(canvas);
-    const {backgroundColor, fontColor, fontSize, fontStyle, padding} = this.options;
-    ctx.font = `${fontSize}px ${fontStyle}`;
-    const maxTextWidth = this.measureWidth(ctx);
-    const textHeight = this.measureHeight();
-    ctx.fillStyle = backgroundColor;
-    const upperLeft = this.getUpperLeft(ctx, canvas);
-    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);
-    ctx.fillStyle = fontColor;
-    this.text.forEach((textLine, i) => {
-      const x = padding + upperLeft.x;
-      const y = padding + upperLeft.y + (i + 1) * fontSize;
-      ctx.fillText(textLine, x, y);
-    });
-  }
-}
-
-// build/src/draw/DrawBox.js
-class DrawBoxOptions {
-  constructor(options = {}) {
-    const {boxColor, lineWidth, label, drawLabelOptions} = options;
-    this.boxColor = boxColor || "rgba(0, 0, 255, 1)";
-    this.lineWidth = lineWidth || 2;
-    this.label = label;
-    const defaultDrawLabelOptions = {
-      anchorPosition: AnchorPosition.BOTTOM_LEFT,
-      backgroundColor: this.boxColor
-    };
-    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions));
-  }
-}
-class DrawBox {
-  constructor(box, options = {}) {
-    this.box = new Box(box);
-    this.options = new DrawBoxOptions(options);
-  }
-  draw(canvasArg) {
-    const ctx = getContext2dOrThrow(canvasArg);
-    const {boxColor, lineWidth} = this.options;
-    const {x, y, width, height} = this.box;
-    ctx.strokeStyle = boxColor;
-    ctx.lineWidth = lineWidth;
-    ctx.strokeRect(x, y, width, height);
-    const {label} = this.options;
-    if (label) {
-      new DrawTextField([label], {x: x - lineWidth / 2, y}, this.options.drawLabelOptions).draw(canvasArg);
-    }
-  }
-}
-
-// build/src/draw/drawDetections.js
-function drawDetections(canvasArg, detections) {
-  const detectionsArray = Array.isArray(detections) ? detections : [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 ? `${round(score)}` : void 0;
-    new DrawBox(box, {label}).draw(canvasArg);
-  });
-}
-
-// build/src/dom/isMediaLoaded.js
-function isMediaLoaded(media) {
-  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 env.getEnv().Canvas || isMediaLoaded(media)) {
-      return resolve(null);
-    }
-    function onLoad(e) {
-      if (!e.currentTarget)
-        return;
-      e.currentTarget.removeEventListener("load", onLoad);
-      e.currentTarget.removeEventListener("error", onError);
-      resolve(e);
-    }
-    function onError(e) {
-      if (!e.currentTarget)
-        return;
-      e.currentTarget.removeEventListener("load", onLoad);
-      e.currentTarget.removeEventListener("error", onError);
-      reject(e);
-    }
-    media.addEventListener("load", onLoad);
-    media.addEventListener("error", onError);
-  });
-}
-
-// build/src/dom/bufferToImage.js
-function bufferToImage(buf) {
-  return new Promise((resolve, reject) => {
-    if (!(buf instanceof Blob)) {
-      return reject("bufferToImage - expected buf to be of type: Blob");
-    }
-    const reader = new FileReader();
-    reader.onload = () => {
-      if (typeof reader.result !== "string") {
-        return reject("bufferToImage - expected reader.result to be a string, in onload");
-      }
-      const img = env.getEnv().createImageElement();
-      img.onload = () => resolve(img);
-      img.onerror = reject;
-      img.src = reader.result;
-    };
-    reader.onerror = reject;
-    reader.readAsDataURL(buf);
-  });
-}
-
-// build/src/dom/getMediaDimensions.js
-function getMediaDimensions(input) {
-  const {Image, Video} = env.getEnv();
-  if (input instanceof Image) {
-    return new Dimensions(input.naturalWidth, input.naturalHeight);
-  }
-  if (input instanceof Video) {
-    return new Dimensions(input.videoWidth, input.videoHeight);
-  }
-  return new Dimensions(input.width, input.height);
-}
-
-// build/src/dom/createCanvas.js
-function createCanvas({width, height}) {
-  const {createCanvasElement} = env.getEnv();
-  const canvas = createCanvasElement();
-  canvas.width = width;
-  canvas.height = height;
-  return canvas;
-}
-function createCanvasFromMedia(media, dims) {
-  const {ImageData: ImageData2} = env.getEnv();
-  if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
-    throw new Error("createCanvasFromMedia - media has not finished loading yet");
-  }
-  const {width, height} = dims || getMediaDimensions(media);
-  const canvas = createCanvas({width, height});
-  if (media instanceof ImageData2) {
-    getContext2dOrThrow(canvas).putImageData(media, 0, 0);
-  } else {
-    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-  }
-  return canvas;
-}
-
-// build/src/dom/imageTensorToCanvas.js
-const tf4 = __toModule(require("@tensorflow/tfjs"));
-async function imageTensorToCanvas(imgTensor, canvas) {
-  const targetCanvas = canvas || env.getEnv().createCanvasElement();
-  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-  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} = 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} = env.getEnv();
-  if (!(input instanceof Image || input instanceof Canvas)) {
-    throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
-  }
-  const dims = getMediaDimensions(input);
-  const scale2 = inputSize / Math.max(dims.height, dims.width);
-  const width = scale2 * dims.width;
-  const height = scale2 * dims.height;
-  const targetCanvas = createCanvas({width: inputSize, height: inputSize});
-  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
-  const offset = Math.abs(width - height) / 2;
-  const dx = centerImage && width < height ? offset : 0;
-  const dy = centerImage && height < width ? offset : 0;
-  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
-  return targetCanvas;
-}
-
-// build/src/dom/NetInput.js
-const tf5 = __toModule(require("@tensorflow/tfjs"));
-class NetInput {
-  constructor(inputs, treatAsBatchInput = false) {
-    this._imageTensors = [];
-    this._canvases = [];
-    this._treatAsBatchInput = false;
-    this._inputDimensions = [];
-    if (!Array.isArray(inputs)) {
-      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
-    }
-    this._treatAsBatchInput = treatAsBatchInput;
-    this._batchSize = inputs.length;
-    inputs.forEach((input, idx) => {
-      if (isTensor3D(input)) {
-        this._imageTensors[idx] = input;
-        this._inputDimensions[idx] = input.shape;
-        return;
-      }
-      if (isTensor4D(input)) {
-        const batchSize = input.shape[0];
-        if (batchSize !== 1) {
-          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-        }
-        this._imageTensors[idx] = input;
-        this._inputDimensions[idx] = input.shape.slice(1);
-        return;
-      }
-      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
-      this._canvases[idx] = canvas;
-      this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
-  }
-  getInput(batchIdx) {
-    return this.canvases[batchIdx] || this.imageTensors[batchIdx];
-  }
-  getInputDimensions(batchIdx) {
-    return this._inputDimensions[batchIdx];
-  }
-  getInputHeight(batchIdx) {
-    return this._inputDimensions[batchIdx][0];
-  }
-  getInputWidth(batchIdx) {
-    return this._inputDimensions[batchIdx][1];
-  }
-  getReshapedInputDimensions(batchIdx) {
-    if (typeof this.inputSize !== "number") {
-      throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");
-    }
-    const width = this.getInputWidth(batchIdx);
-    const height = this.getInputHeight(batchIdx);
-    return computeReshapedDimensions({width, height}, this.inputSize);
-  }
-  toBatchTensor(inputSize, isCenterInputs = true) {
-    this._inputSize = inputSize;
-    return tf5.tidy(() => {
-      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
-        const input = this.getInput(batchIdx);
-        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 = tf5.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
-          }
-          return imgTensor.as3D(inputSize, inputSize, 3);
-        }
-        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 = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
-      return batchTensor;
-    });
-  }
-}
-
-// build/src/dom/toNetInput.js
-async function toNetInput(inputs) {
-  if (inputs instanceof NetInput) {
-    return inputs;
-  }
-  let inputArgArray = Array.isArray(inputs) ? inputs : [inputs];
-  if (!inputArgArray.length) {
-    throw new Error("toNetInput - empty array passed as input");
-  }
-  const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : "";
-  const inputArray = inputArgArray.map(resolveInput);
-  inputArray.forEach((input, i) => {
-    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
-      if (typeof inputArgArray[i] === "string") {
-        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);
-      }
-      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);
-    }
-    if (isTensor4D(input)) {
-      const batchSize = input.shape[0];
-      if (batchSize !== 1) {
-        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-      }
-    }
-  });
-  await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));
-  return new NetInput(inputArray, Array.isArray(inputs));
-}
-
-// build/src/dom/extractFaces.js
-async function extractFaces(input, detections) {
-  const {Canvas} = env.getEnv();
-  let canvas = input;
-  if (!(input instanceof Canvas)) {
-    const netInput = await toNetInput(input);
-    if (netInput.batchSize > 1) {
-      throw new Error("extractFaces - batchSize > 1 not supported");
-    }
-    const tensorOrCanvas = netInput.getInput(0);
-    canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);
-  }
-  const ctx = getContext2dOrThrow(canvas);
-  const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
-  return boxes.map(({x, y, width, height}) => {
-    const faceImg = createCanvas({width, height});
-    getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
-    return faceImg;
-  });
-}
-
-// 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");
-  }
-  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
-    throw new Error("extractFaceTensors - batchSize > 1 not supported");
-  }
-  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}) => 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 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}`);
-  }
-  return res;
-}
-
-// build/src/dom/fetchImage.js
-async function fetchImage(uri) {
-  const res = await fetchOrThrow(uri);
-  const blob = await res.blob();
-  if (!blob.type.startsWith("image/")) {
-    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);
-  }
-  return bufferToImage(blob);
-}
-
-// build/src/dom/fetchJson.js
-async function fetchJson(uri) {
-  return (await fetchOrThrow(uri)).json();
-}
-
-// build/src/dom/fetchNetWeights.js
-async function fetchNetWeights(uri) {
-  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-}
-
-// build/src/common/getModelUris.js
-function getModelUris(uri, defaultModelName) {
-  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;
-  if (!uri) {
-    return {
-      modelBaseUri: "",
-      manifestUri: defaultManifestFilename
-    };
-  }
-  if (uri === "/") {
-    return {
-      modelBaseUri: "/",
-      manifestUri: `/${defaultManifestFilename}`
-    };
-  }
-  const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : "";
-  uri = uri.replace(protocol, "");
-  const parts = uri.split("/").filter((s) => s);
-  const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename;
-  let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/");
-  modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri;
-  return {
-    modelBaseUri,
-    manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`
-  };
-}
-
-// 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 tf7.io.loadWeights(manifest, modelBaseUri);
-}
-
-// build/src/dom/matchDimensions.js
-function matchDimensions(input, reference, useMediaDimensions = false) {
-  const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
-  input.width = width;
-  input.height = height;
-  return {width, height};
-}
-
-// build/src/dom/types.js
-
-// build/src/dom/index.js
-
-// build/src/NeuralNetwork.js
-const tf8 = __toModule(require("@tensorflow/tfjs"));
-class NeuralNetwork {
-  constructor(_name) {
-    this._name = _name;
-    this._params = void 0;
-    this._paramMappings = [];
-  }
-  get params() {
-    return this._params;
-  }
-  get paramMappings() {
-    return this._paramMappings;
-  }
-  get isLoaded() {
-    return !!this.params;
-  }
-  getParamFromPath(paramPath) {
-    const {obj, objProp} = this.traversePropertyPath(paramPath);
-    return obj[objProp];
-  }
-  reassignParamFromPath(paramPath, tensor2) {
-    const {obj, objProp} = this.traversePropertyPath(paramPath);
-    obj[objProp].dispose();
-    obj[objProp] = tensor2;
-  }
-  getParamList() {
-    return this._paramMappings.map(({paramPath}) => ({
-      path: paramPath,
-      tensor: this.getParamFromPath(paramPath)
-    }));
-  }
-  getTrainableParams() {
-    return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable);
-  }
-  getFrozenParams() {
-    return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable));
-  }
-  variable() {
-    this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
-      this.reassignParamFromPath(path, tensor2.variable());
-    });
-  }
-  freeze() {
-    this.getTrainableParams().forEach(({path, tensor: variable}) => {
-      const tensor2 = tf8.tensor(variable.dataSync());
-      variable.dispose();
-      this.reassignParamFromPath(path, tensor2);
-    });
-  }
-  dispose(throwOnRedispose = true) {
-    this.getParamList().forEach((param) => {
-      if (throwOnRedispose && param.tensor.isDisposed) {
-        throw new Error(`param tensor has already been disposed for path ${param.path}`);
-      }
-      param.tensor.dispose();
-    });
-    this._params = void 0;
-  }
-  serializeParams() {
-    return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
-  }
-  async load(weightsOrUrl) {
-    if (weightsOrUrl instanceof Float32Array) {
-      this.extractWeights(weightsOrUrl);
-      return;
-    }
-    await this.loadFromUri(weightsOrUrl);
-  }
-  async loadFromUri(uri) {
-    if (uri && typeof uri !== "string") {
-      throw new Error(`${this._name}.loadFromUri - expected model uri`);
-    }
-    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());
-    this.loadFromWeightMap(weightMap);
-  }
-  async loadFromDisk(filePath) {
-    if (filePath && typeof filePath !== "string") {
-      throw new Error(`${this._name}.loadFromDisk - expected model file path`);
-    }
-    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 loadWeights = tf8.io.weightsLoaderFactory(fetchWeightsFromDisk);
-    const manifest = JSON.parse((await readFile(manifestUri)).toString());
-    const weightMap = await loadWeights(manifest, modelBaseUri);
-    this.loadFromWeightMap(weightMap);
-  }
-  loadFromWeightMap(weightMap) {
-    const {paramMappings, params} = this.extractParamsFromWeigthMap(weightMap);
-    this._paramMappings = paramMappings;
-    this._params = params;
-  }
-  extractWeights(weights) {
-    const {paramMappings, params} = this.extractParams(weights);
-    this._paramMappings = paramMappings;
-    this._params = params;
-  }
-  traversePropertyPath(paramPath) {
-    if (!this.params) {
-      throw new Error(`traversePropertyPath - model has no loaded params`);
-    }
-    const result = paramPath.split("/").reduce((res, objProp2) => {
-      if (!res.nextObj.hasOwnProperty(objProp2)) {
-        throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);
-      }
-      return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
-    }, {nextObj: this.params});
-    const {obj, objProp} = result;
-    if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) {
-      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
-    }
-    return {obj, objProp};
-  }
-}
-
-// build/src/common/depthwiseSeparableConv.js
-const tf9 = __toModule(require("@tensorflow/tfjs"));
-function depthwiseSeparableConv(x, params, stride) {
-  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 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 = tf10.relu(tf10.add(out1, out2));
-    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    return tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
-  });
-}
-function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-  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 = tf10.relu(tf10.add(out1, out2));
-    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
-    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-    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 tf11.tidy(() => {
-    const out = tf11.add(tf11.conv2d(x, params.filters, [1, 1], padding), params.bias);
-    return withRelu ? tf11.relu(out) : out;
-  });
-}
-
-// build/src/common/disposeUnusedWeightTensors.js
-function disposeUnusedWeightTensors(weightMap, paramMappings) {
-  Object.keys(weightMap).forEach((path) => {
-    if (!paramMappings.some((pm) => pm.originalPath === path)) {
-      weightMap[path].dispose();
-    }
-  });
-}
-
-// build/src/common/extractConvParamsFactory.js
-const tf12 = __toModule(require("@tensorflow/tfjs"));
-function extractConvParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
-    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: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  };
-}
-
-// build/src/common/extractFCParamsFactory.js
-const tf13 = __toModule(require("@tensorflow/tfjs"));
-function extractFCParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tf13.tensor1d(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
-    return {
-      weights: fc_weights,
-      bias: fc_bias
-    };
-  };
-}
-
-// build/src/common/types.js
-class SeparableConvParams {
-  constructor(depthwise_filter, pointwise_filter, bias) {
-    this.depthwise_filter = depthwise_filter;
-    this.pointwise_filter = pointwise_filter;
-    this.bias = bias;
-  }
-}
-
-// build/src/common/extractSeparableConvParamsFactory.js
-const tf14 = __toModule(require("@tensorflow/tfjs"));
-function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tf14.tensor1d(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-function loadSeparableConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);
-    const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-
-// build/src/common/extractWeightEntryFactory.js
-function extractWeightEntryFactory(weightMap, paramMappings) {
-  return function(originalPath, paramRank, mappedPath) {
-    const tensor2 = weightMap[originalPath];
-    if (!isTensor(tensor2, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
-    }
-    paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor2;
-  };
-}
-
-// build/src/common/extractWeightsFactory.js
-function extractWeightsFactory(weights) {
-  let remainingWeights = weights;
-  function extractWeights(numWeights) {
-    const ret = remainingWeights.slice(0, numWeights);
-    remainingWeights = remainingWeights.slice(numWeights);
-    return ret;
-  }
-  function getRemainingWeights() {
-    return remainingWeights;
-  }
-  return {
-    extractWeights,
-    getRemainingWeights
-  };
-}
-
-// build/src/common/index.js
-
-// build/src/faceFeatureExtractor/extractorsFactory.js
-function extractorsFactory(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);
-    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);
-    const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);
-    return {conv0, conv1, conv2: conv22};
-  }
-  function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const {conv0, conv1, conv2: conv22} = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);
-    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);
-    return {conv0, conv1, conv2: conv22, conv3};
-  }
-  return {
-    extractDenseBlock3Params,
-    extractDenseBlock4Params
-  };
-}
-
-// build/src/faceFeatureExtractor/extractParams.js
-function extractParams(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractDenseBlock4Params} = extractorsFactory(extractWeights, paramMappings);
-  const dense0 = extractDenseBlock4Params(3, 32, "dense0", true);
-  const dense1 = extractDenseBlock4Params(32, 64, "dense1");
-  const dense2 = extractDenseBlock4Params(64, 128, "dense2");
-  const dense3 = extractDenseBlock4Params(128, 256, "dense3");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2, dense3}
-  };
-}
-
-// build/src/common/loadConvParamsFactory.js
-function loadConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  };
-}
-
-// build/src/faceFeatureExtractor/loadParamsFactory.js
-function loadParamsFactory(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractDenseBlock3Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-    const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-    return {conv0, conv1, conv2: conv22};
-  }
-  function extractDenseBlock4Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-    const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);
-    return {conv0, conv1, conv2: conv22, conv3};
-  }
-  return {
-    extractDenseBlock3Params,
-    extractDenseBlock4Params
-  };
-}
-
-// build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap(weightMap) {
-  const paramMappings = [];
-  const {extractDenseBlock4Params} = loadParamsFactory(weightMap, paramMappings);
-  const params = {
-    dense0: extractDenseBlock4Params("dense0", true),
-    dense1: extractDenseBlock4Params("dense1"),
-    dense2: extractDenseBlock4Params("dense2"),
-    dense3: extractDenseBlock4Params("dense3")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceFeatureExtractor/FaceFeatureExtractor.js
-const tf15 = __toModule(require("@tensorflow/tfjs"));
-class FaceFeatureExtractor extends NeuralNetwork {
-  constructor() {
-    super("FaceFeatureExtractor");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceFeatureExtractor - load model before inference");
-    }
-    return tf15.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      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 = tf15.avgPool(out, [7, 7], [2, 2], "valid");
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "face_feature_extractor_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams(weights);
-  }
-}
-
-// build/src/common/fullyConnectedLayer.js
-const tf16 = __toModule(require("@tensorflow/tfjs"));
-function fullyConnectedLayer(x, params) {
-  return tf16.tidy(() => tf16.add(tf16.matMul(x, params.weights), params.bias));
-}
-
-// build/src/faceProcessor/extractParams.js
-function extractParams3(weights, channelsIn, channelsOut) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-  const fc = extractFCParams(channelsIn, channelsOut, "fc");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc}
-  };
-}
-
-// build/src/faceProcessor/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap3(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: extractFcParams("fc")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceProcessor/util.js
-function seperateWeightMaps(weightMap) {
-  const featureExtractorMap = {};
-  const classifierMap = {};
-  Object.keys(weightMap).forEach((key) => {
-    const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
-    map[key] = weightMap[key];
-  });
-  return {featureExtractorMap, classifierMap};
-}
-
-// build/src/faceProcessor/FaceProcessor.js
-const tf17 = __toModule(require("@tensorflow/tfjs"));
-class FaceProcessor extends NeuralNetwork {
-  constructor(_name, faceFeatureExtractor) {
-    super(_name);
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${this._name} - load model before inference`);
-    }
-    return tf17.tidy(() => {
-      const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
-    });
-  }
-  dispose(throwOnRedispose = true) {
-    this.faceFeatureExtractor.dispose(throwOnRedispose);
-    super.dispose(throwOnRedispose);
-  }
-  loadClassifierParams(weights) {
-    const {params, paramMappings} = this.extractClassifierParams(weights);
-    this._params = params;
-    this._paramMappings = paramMappings;
-  }
-  extractClassifierParams(weights) {
-    return extractParams3(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-    return extractParamsFromWeigthMap3(classifierMap);
-  }
-  extractParams(weights) {
-    const cIn = this.getClassifierChannelsIn();
-    const cOut = this.getClassifierChannelsOut();
-    const classifierWeightSize = cOut * cIn + cOut;
-    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-    const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-    return this.extractClassifierParams(classifierWeights);
-  }
-}
-
-// build/src/faceExpressionNet/FaceExpressions.js
-const FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
-class FaceExpressions {
-  constructor(probabilities) {
-    if (probabilities.length !== 7) {
-      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);
-    }
-    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {
-      this[expression] = probabilities[idx];
-    });
-  }
-  asSortedArray() {
-    return FACE_EXPRESSION_LABELS.map((expression) => ({expression, probability: this[expression]})).sort((e0, e1) => e1.probability - e0.probability);
-  }
-}
-
-// 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 tf18.tidy(() => tf18.softmax(this.runNet(input)));
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async predictExpressions(input) {
-    const netInput = await toNetInput(input);
-    const out = await this.forwardInput(netInput);
-    const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => {
-      const data = await t.data();
-      t.dispose();
-      return data;
-    }));
-    out.dispose();
-    const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites));
-    return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-  }
-  getDefaultModelName() {
-    return "face_expression_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-  getClassifierChannelsOut() {
-    return 7;
-  }
-}
-
-// build/src/faceExpressionNet/index.js
-
-// build/src/factories/WithFaceExpressions.js
-function isWithFaceExpressions(obj) {
-  return obj["expressions"] instanceof FaceExpressions;
-}
-function extendWithFaceExpressions(sourceObj, expressions) {
-  const extension = {expressions};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/draw/drawFaceExpressions.js
-function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) {
-  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
-  faceExpressionsArray.forEach((e) => {
-    const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0;
-    if (!expr) {
-      throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");
-    }
-    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} (${round(expr2.probability)})`), anchor);
-    drawTextField.draw(canvasArg);
-  });
-}
-
-// build/src/factories/WithFaceLandmarks.js
-function isWithFaceLandmarks(obj) {
-  return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection;
-}
-function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
-  const {box: shift} = sourceObj.detection;
-  const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
-  const rect = landmarks.align();
-  const {imageDims} = sourceObj.detection;
-  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
-  const extension = {
-    landmarks,
-    unshiftedLandmarks,
-    alignedRect
-  };
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/draw/DrawFaceLandmarks.js
-class DrawFaceLandmarksOptions {
-  constructor(options = {}) {
-    const {drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor} = options;
-    this.drawLines = drawLines;
-    this.drawPoints = drawPoints;
-    this.lineWidth = lineWidth || 1;
-    this.pointSize = pointSize || 2;
-    this.lineColor = lineColor || "rgba(0, 255, 255, 1)";
-    this.pointColor = pointColor || "rgba(255, 0, 255, 1)";
-  }
-}
-class DrawFaceLandmarks {
-  constructor(faceLandmarks, options = {}) {
-    this.faceLandmarks = faceLandmarks;
-    this.options = new DrawFaceLandmarksOptions(options);
-  }
-  draw(canvasArg) {
-    const ctx = getContext2dOrThrow(canvasArg);
-    const {drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor} = this.options;
-    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {
-      ctx.strokeStyle = lineColor;
-      ctx.lineWidth = lineWidth;
-      drawContour(ctx, this.faceLandmarks.getJawOutline());
-      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
-      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
-      drawContour(ctx, this.faceLandmarks.getNose());
-      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
-      drawContour(ctx, this.faceLandmarks.getRightEye(), true);
-      drawContour(ctx, this.faceLandmarks.getMouth(), true);
-    }
-    if (drawPoints) {
-      ctx.strokeStyle = pointColor;
-      ctx.fillStyle = pointColor;
-      const drawPoint = (pt) => {
-        ctx.beginPath();
-        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
-        ctx.fill();
-      };
-      this.faceLandmarks.positions.forEach(drawPoint);
-    }
-  }
-}
-function drawFaceLandmarks(canvasArg, faceLandmarks) {
-  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
-  faceLandmarksArray.forEach((f) => {
-    const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0;
-    if (!landmarks) {
-      throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");
-    }
-    new DrawFaceLandmarks(landmarks).draw(canvasArg);
-  });
-}
-
-// build/src/draw/index.js
-const draw_exports = {};
-__export(draw_exports, {
-  AnchorPosition: () => AnchorPosition,
-  DrawBox: () => DrawBox,
-  DrawBoxOptions: () => DrawBoxOptions,
-  DrawFaceLandmarks: () => DrawFaceLandmarks,
-  DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions,
-  DrawTextField: () => DrawTextField,
-  DrawTextFieldOptions: () => DrawTextFieldOptions,
-  drawContour: () => drawContour,
-  drawDetections: () => drawDetections,
-  drawFaceExpressions: () => drawFaceExpressions,
-  drawFaceLandmarks: () => drawFaceLandmarks
-});
-
-// build/src/xception/extractParams.js
-function extractorsFactory3(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);
-    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);
-    return {separable_conv0, separable_conv1, expansion_conv};
-  }
-  function extractMainBlockParams(channels, mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);
-    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);
-    return {separable_conv0, separable_conv1, separable_conv2};
-  }
-  return {
-    extractConvParams,
-    extractSeparableConvParams,
-    extractReductionBlockParams,
-    extractMainBlockParams
-  };
-}
-function extractParams5(weights, numMainBlocks) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = extractorsFactory3(extractWeights, paramMappings);
-  const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in");
-  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0");
-  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1");
-  const entry_flow = {
-    conv_in: entry_flow_conv_in,
-    reduction_block_0: entry_flow_reduction_block_0,
-    reduction_block_1: entry_flow_reduction_block_1
-  };
-  const middle_flow = {};
-  range(numMainBlocks, 0, 1).forEach((idx) => {
-    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
-  });
-  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
-  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv");
-  const exit_flow = {
-    reduction_block: exit_flow_reduction_block,
-    separable_conv: exit_flow_separable_conv
-  };
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {entry_flow, middle_flow, exit_flow}
-  };
-}
-
-// build/src/xception/extractParamsFromWeigthMap.js
-function loadParamsFactory3(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractReductionBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);
-    return {separable_conv0, separable_conv1, expansion_conv};
-  }
-  function extractMainBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);
-    return {separable_conv0, separable_conv1, separable_conv2};
-  }
-  return {
-    extractConvParams,
-    extractSeparableConvParams,
-    extractReductionBlockParams,
-    extractMainBlockParams
-  };
-}
-function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
-  const paramMappings = [];
-  const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = loadParamsFactory3(weightMap, paramMappings);
-  const entry_flow_conv_in = extractConvParams("entry_flow/conv_in");
-  const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0");
-  const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1");
-  const entry_flow = {
-    conv_in: entry_flow_conv_in,
-    reduction_block_0: entry_flow_reduction_block_0,
-    reduction_block_1: entry_flow_reduction_block_1
-  };
-  const middle_flow = {};
-  range(numMainBlocks, 0, 1).forEach((idx) => {
-    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
-  });
-  const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
-  const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv");
-  const exit_flow = {
-    reduction_block: exit_flow_reduction_block,
-    separable_conv: exit_flow_separable_conv
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params: {entry_flow, middle_flow, exit_flow}, paramMappings};
-}
-
-// build/src/xception/TinyXception.js
-const tf19 = __toModule(require("@tensorflow/tfjs"));
-function conv(x, params, stride) {
-  return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias);
-}
-function reductionBlock(x, params, isActivateInput = true) {
-  let out = isActivateInput ? tf19.relu(x) : x;
-  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-  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(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 {
-  constructor(numMainBlocks) {
-    super("TinyXception");
-    this._numMainBlocks = numMainBlocks;
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyXception - load model before inference");
-    }
-    return tf19.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      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);
-      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 = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "tiny_xception_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap5(weightMap, this._numMainBlocks);
-  }
-  extractParams(weights) {
-    return extractParams5(weights, this._numMainBlocks);
-  }
-}
-
-// build/src/ageGenderNet/extractParams.js
-function extractParams7(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-  const age = extractFCParams(512, 1, "fc/age");
-  const gender = extractFCParams(512, 2, "fc/gender");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc: {age, gender}}
-  };
-}
-
-// build/src/ageGenderNet/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap7(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: {
-      age: extractFcParams("fc/age"),
-      gender: extractFcParams("fc/gender")
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/ageGenderNet/types.js
-var Gender;
-(function(Gender2) {
-  Gender2["FEMALE"] = "female";
-  Gender2["MALE"] = "male";
-})(Gender || (Gender = {}));
-
-// build/src/ageGenderNet/AgeGenderNet.js
-const tf20 = __toModule(require("@tensorflow/tfjs"));
-class AgeGenderNet extends NeuralNetwork {
-  constructor(faceFeatureExtractor = new TinyXception(2)) {
-    super("AgeGenderNet");
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${this._name} - load model before inference`);
-    }
-    return tf20.tidy(() => {
-      const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      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 tf20.tidy(() => {
-      const {age, gender} = this.runNet(input);
-      return {age, gender: tf20.softmax(gender)};
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async predictAgeAndGender(input) {
-    const netInput = await toNetInput(input);
-    const out = await this.forwardInput(netInput);
-    const ages = tf20.unstack(out.age);
-    const genders = tf20.unstack(out.gender);
-    const ageAndGenderTensors = ages.map((ageTensor, i) => ({
-      ageTensor,
-      genderTensor: genders[i]
-    }));
-    const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ageTensor, genderTensor}) => {
-      const age = (await ageTensor.data())[0];
-      const probMale = (await genderTensor.data())[0];
-      const isMale = probMale > 0.5;
-      const gender = isMale ? Gender.MALE : Gender.FEMALE;
-      const genderProbability = isMale ? probMale : 1 - probMale;
-      ageTensor.dispose();
-      genderTensor.dispose();
-      return {age, gender, genderProbability};
-    }));
-    out.age.dispose();
-    out.gender.dispose();
-    return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-  }
-  getDefaultModelName() {
-    return "age_gender_model";
-  }
-  dispose(throwOnRedispose = true) {
-    this.faceFeatureExtractor.dispose(throwOnRedispose);
-    super.dispose(throwOnRedispose);
-  }
-  loadClassifierParams(weights) {
-    const {params, paramMappings} = this.extractClassifierParams(weights);
-    this._params = params;
-    this._paramMappings = paramMappings;
-  }
-  extractClassifierParams(weights) {
-    return extractParams7(weights);
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-    return extractParamsFromWeigthMap7(classifierMap);
-  }
-  extractParams(weights) {
-    const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2);
-    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-    const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-    return this.extractClassifierParams(classifierWeights);
-  }
-}
-
-// 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}) => {
-      const scale2 = inputSize / Math.max(height, width);
-      return {
-        width: width * scale2,
-        height: height * scale2
-      };
-    });
-    const batchSize = inputDimensions.length;
-    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];
-        return cond(width, height) ? Math.abs(width - height) / 2 : 0;
-      };
-      const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
-      const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      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 tf21.tidy(() => {
-      const out = this.runNet(input);
-      return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async detectLandmarks(input) {
-    const netInput = await toNetInput(input);
-    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));
-      const yCoords = landmarksArray.filter((_, i) => !isEven(i));
-      return new FaceLandmarks68(Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), {
-        height: netInput.getInputHeight(batchIdx),
-        width: netInput.getInputWidth(batchIdx)
-      });
-    }));
-    landmarkTensors.forEach((t) => t.dispose());
-    return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0];
-  }
-  getClassifierChannelsOut() {
-    return 136;
-  }
-}
-
-// build/src/faceLandmarkNet/FaceLandmark68Net.js
-class FaceLandmark68Net extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-    super("FaceLandmark68Net", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-}
-
-// build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js
-function extractParamsFromWeigthMapTiny(weightMap) {
-  const paramMappings = [];
-  const {extractDenseBlock3Params} = loadParamsFactory(weightMap, paramMappings);
-  const params = {
-    dense0: extractDenseBlock3Params("dense0", true),
-    dense1: extractDenseBlock3Params("dense1"),
-    dense2: extractDenseBlock3Params("dense2")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceFeatureExtractor/extractParamsTiny.js
-function extractParamsTiny(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractDenseBlock3Params} = extractorsFactory(extractWeights, paramMappings);
-  const dense0 = extractDenseBlock3Params(3, 32, "dense0", true);
-  const dense1 = extractDenseBlock3Params(32, 64, "dense1");
-  const dense2 = extractDenseBlock3Params(64, 128, "dense2");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2}
-  };
-}
-
-// build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
-const tf22 = __toModule(require("@tensorflow/tfjs"));
-class TinyFaceFeatureExtractor extends NeuralNetwork {
-  constructor() {
-    super("TinyFaceFeatureExtractor");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyFaceFeatureExtractor - load model before inference");
-    }
-    return tf22.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      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 = tf22.avgPool(out, [14, 14], [2, 2], "valid");
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "face_feature_extractor_tiny_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMapTiny(weightMap);
-  }
-  extractParams(weights) {
-    return extractParamsTiny(weights);
-  }
-}
-
-// build/src/faceLandmarkNet/FaceLandmark68TinyNet.js
-class FaceLandmark68TinyNet extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-    super("FaceLandmark68TinyNet", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_tiny_model";
-  }
-  getClassifierChannelsIn() {
-    return 128;
-  }
-}
-
-// build/src/faceLandmarkNet/index.js
-class FaceLandmarkNet extends FaceLandmark68Net {
-}
-
-// build/src/faceRecognitionNet/scaleLayer.js
-const tf23 = __toModule(require("@tensorflow/tfjs"));
-function scale(x, params) {
-  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 = tf24.conv2d(x, filters, strides, padding);
-  out = tf24.add(out, bias);
-  out = scale(out, params.scale);
-  return withRelu ? tf24.relu(out) : out;
-}
-function conv2(x, params) {
-  return convLayer2(x, params, [1, 1], true);
-}
-function convNoRelu(x, params) {
-  return convLayer2(x, params, [1, 1], false);
-}
-function convDown(x, params) {
-  return convLayer2(x, params, [2, 2], true, "valid");
-}
-
-// build/src/faceRecognitionNet/extractParams.js
-const tf25 = __toModule(require("@tensorflow/tfjs"));
-function extractorsFactory5(extractWeights, paramMappings) {
-  function extractFilterValues(numFilterValues, numFilters, filterSize) {
-    const weights = extractWeights(numFilterValues);
-    const depth = weights.length / (numFilters * filterSize * filterSize);
-    if (isFloat(depth)) {
-      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
-    }
-    return tf25.tidy(() => tf25.transpose(tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
-  }
-  function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-    const bias = tf25.tensor1d(extractWeights(numFilters));
-    paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  }
-  function extractScaleLayerParams(numWeights, mappedPrefix) {
-    const weights = tf25.tensor1d(extractWeights(numWeights));
-    const biases = tf25.tensor1d(extractWeights(numWeights));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
-    return {
-      weights,
-      biases
-    };
-  }
-  function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-    const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);
-    const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);
-    return {conv: conv3, scale: scale2};
-  }
-  function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) {
-    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);
-    const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);
-    return {conv1, conv2: conv22};
-  }
-  return {
-    extractConvLayerParams,
-    extractResidualLayerParams
-  };
-}
-function extractParams9(weights) {
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const paramMappings = [];
-  const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory5(extractWeights, paramMappings);
-  const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down");
-  const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1");
-  const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2");
-  const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3");
-  const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true);
-  const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1");
-  const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2");
-  const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3");
-  const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true);
-  const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1");
-  const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2");
-  const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true);
-  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 = 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: ${getRemainingWeights().length}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  return {params, paramMappings};
-}
-
-// build/src/faceRecognitionNet/extractParamsFromWeigthMap.js
-function extractorsFactory6(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractScaleLayerParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);
-    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);
-    return {weights, biases};
-  }
-  function extractConvLayerParams(prefix) {
-    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);
-    const scale2 = extractScaleLayerParams(prefix);
-    return {conv: {filters, bias}, scale: scale2};
-  }
-  function extractResidualLayerParams(prefix) {
-    return {
-      conv1: extractConvLayerParams(`${prefix}/conv1`),
-      conv2: extractConvLayerParams(`${prefix}/conv2`)
-    };
-  }
-  return {
-    extractConvLayerParams,
-    extractResidualLayerParams
-  };
-}
-function extractParamsFromWeigthMap9(weightMap) {
-  const paramMappings = [];
-  const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory6(weightMap, paramMappings);
-  const conv32_down = extractConvLayerParams("conv32_down");
-  const conv32_1 = extractResidualLayerParams("conv32_1");
-  const conv32_2 = extractResidualLayerParams("conv32_2");
-  const conv32_3 = extractResidualLayerParams("conv32_3");
-  const conv64_down = extractResidualLayerParams("conv64_down");
-  const conv64_1 = extractResidualLayerParams("conv64_1");
-  const conv64_2 = extractResidualLayerParams("conv64_2");
-  const conv64_3 = extractResidualLayerParams("conv64_3");
-  const conv128_down = extractResidualLayerParams("conv128_down");
-  const conv128_1 = extractResidualLayerParams("conv128_1");
-  const conv128_2 = extractResidualLayerParams("conv128_2");
-  const conv256_down = extractResidualLayerParams("conv256_down");
-  const conv256_1 = extractResidualLayerParams("conv256_1");
-  const conv256_2 = extractResidualLayerParams("conv256_2");
-  const conv256_down_out = extractResidualLayerParams("conv256_down_out");
-  const fc = weightMap["fc"];
-  paramMappings.push({originalPath: "fc", paramPath: "fc"});
-  if (!isTensor2D(fc)) {
-    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// 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 = 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 = 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 = tf26.zeros(padShapeX);
-    out = tf26.concat([out, zerosW], 1);
-    const padShapeY = [...out.shape];
-    padShapeY[2] = 1;
-    const zerosH = tf26.zeros(padShapeY);
-    out = tf26.concat([out, zerosH], 2);
-  }
-  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");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceRecognitionNet - load model before inference");
-    }
-    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(tf27.scalar(256));
-      let out = convDown(normalized, params.conv32_down);
-      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);
-      out = residualDown(out, params.conv64_down);
-      out = residual(out, params.conv64_1);
-      out = residual(out, params.conv64_2);
-      out = residual(out, params.conv64_3);
-      out = residualDown(out, params.conv128_down);
-      out = residual(out, params.conv128_1);
-      out = residual(out, params.conv128_2);
-      out = residualDown(out, params.conv256_down);
-      out = residual(out, params.conv256_1);
-      out = residual(out, params.conv256_2);
-      out = residualDown(out, params.conv256_down_out);
-      const globalAvg = out.mean([1, 2]);
-      const fullyConnected = tf27.matMul(globalAvg, params.fc);
-      return fullyConnected;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async computeFaceDescriptor(input) {
-    const netInput = await toNetInput(input);
-    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];
-  }
-  getDefaultModelName() {
-    return "face_recognition_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap9(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams9(weights);
-  }
-}
-
-// build/src/faceRecognitionNet/index.js
-function createFaceRecognitionNet(weights) {
-  const net = new FaceRecognitionNet();
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/factories/WithFaceDescriptor.js
-function extendWithFaceDescriptor(sourceObj, descriptor) {
-  const extension = {descriptor};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/factories/WithAge.js
-function isWithAge(obj) {
-  return typeof obj["age"] === "number";
-}
-function extendWithAge(sourceObj, age) {
-  const extension = {age};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/factories/WithGender.js
-function isWithGender(obj) {
-  return (obj["gender"] === Gender.MALE || obj["gender"] === Gender.FEMALE) && isValidProbablitiy(obj["genderProbability"]);
-}
-function extendWithGender(sourceObj, gender, genderProbability) {
-  const extension = {gender, genderProbability};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// 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 = 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,
-      batch_norm_scale,
-      batch_norm_offset,
-      batch_norm_mean,
-      batch_norm_variance
-    };
-  }
-  function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-    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}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
-    return {filters, bias};
-  }
-  function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
-    const {filters, bias} = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
-    return {
-      filters,
-      batch_norm_offset: bias
-    };
-  }
-  function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
-    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
-    return {depthwise_conv, pointwise_conv};
-  }
-  function extractMobilenetV1Params() {
-    const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0");
-    const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1");
-    const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2");
-    const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3");
-    const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4");
-    const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5");
-    const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6");
-    const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7");
-    const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8");
-    const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9");
-    const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10");
-    const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11");
-    const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12");
-    const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13");
-    return {
-      conv_0,
-      conv_1,
-      conv_2,
-      conv_3,
-      conv_4,
-      conv_5,
-      conv_6,
-      conv_7,
-      conv_8,
-      conv_9,
-      conv_10,
-      conv_11,
-      conv_12,
-      conv_13
-    };
-  }
-  function extractPredictionLayerParams() {
-    const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0");
-    const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1");
-    const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2");
-    const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3");
-    const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4");
-    const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5");
-    const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6");
-    const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7");
-    const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor");
-    const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor");
-    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor");
-    const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor");
-    const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor");
-    const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor");
-    const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor");
-    const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor");
-    const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor");
-    const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor");
-    const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor");
-    const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor");
-    const box_predictor_0 = {
-      box_encoding_predictor: box_encoding_0_predictor,
-      class_predictor: class_predictor_0
-    };
-    const box_predictor_1 = {
-      box_encoding_predictor: box_encoding_1_predictor,
-      class_predictor: class_predictor_1
-    };
-    const box_predictor_2 = {
-      box_encoding_predictor: box_encoding_2_predictor,
-      class_predictor: class_predictor_2
-    };
-    const box_predictor_3 = {
-      box_encoding_predictor: box_encoding_3_predictor,
-      class_predictor: class_predictor_3
-    };
-    const box_predictor_4 = {
-      box_encoding_predictor: box_encoding_4_predictor,
-      class_predictor: class_predictor_4
-    };
-    const box_predictor_5 = {
-      box_encoding_predictor: box_encoding_5_predictor,
-      class_predictor: class_predictor_5
-    };
-    return {
-      conv_0,
-      conv_1,
-      conv_2,
-      conv_3,
-      conv_4,
-      conv_5,
-      conv_6,
-      conv_7,
-      box_predictor_0,
-      box_predictor_1,
-      box_predictor_2,
-      box_predictor_3,
-      box_predictor_4,
-      box_predictor_5
-    };
-  }
-  return {
-    extractMobilenetV1Params,
-    extractPredictionLayerParams
-  };
-}
-function extractParams11(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory7(extractWeights, paramMappings);
-  const mobilenetv1 = extractMobilenetV1Params();
-  const prediction_layer = extractPredictionLayerParams();
-  const extra_dim = tf28.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
-  const output_layer = {
-    extra_dim
-  };
-  paramMappings.push({paramPath: "output_layer/extra_dim"});
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    params: {
-      mobilenetv1,
-      prediction_layer,
-      output_layer
-    },
-    paramMappings
-  };
-}
-
-// build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js
-function extractorsFactory8(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);
-    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);
-    return {filters, batch_norm_offset};
-  }
-  function extractConvPairParams(idx) {
-    const mappedPrefix = `mobilenetv1/conv_${idx}`;
-    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;
-    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;
-    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;
-    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);
-    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);
-    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);
-    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);
-    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);
-    return {
-      depthwise_conv: {
-        filters,
-        batch_norm_scale,
-        batch_norm_offset,
-        batch_norm_mean,
-        batch_norm_variance
-      },
-      pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv)
-    };
-  }
-  function extractMobilenetV1Params() {
-    return {
-      conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"),
-      conv_1: extractConvPairParams(1),
-      conv_2: extractConvPairParams(2),
-      conv_3: extractConvPairParams(3),
-      conv_4: extractConvPairParams(4),
-      conv_5: extractConvPairParams(5),
-      conv_6: extractConvPairParams(6),
-      conv_7: extractConvPairParams(7),
-      conv_8: extractConvPairParams(8),
-      conv_9: extractConvPairParams(9),
-      conv_10: extractConvPairParams(10),
-      conv_11: extractConvPairParams(11),
-      conv_12: extractConvPairParams(12),
-      conv_13: extractConvPairParams(13)
-    };
-  }
-  function extractConvParams(prefix, mappedPrefix) {
-    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);
-    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);
-    return {filters, bias};
-  }
-  function extractBoxPredictorParams(idx) {
-    const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`);
-    const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`);
-    return {box_encoding_predictor, class_predictor};
-  }
-  function extractPredictionLayerParams() {
-    return {
-      conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"),
-      conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"),
-      conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"),
-      conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"),
-      conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"),
-      conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"),
-      conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"),
-      conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"),
-      box_predictor_0: extractBoxPredictorParams(0),
-      box_predictor_1: extractBoxPredictorParams(1),
-      box_predictor_2: extractBoxPredictorParams(2),
-      box_predictor_3: extractBoxPredictorParams(3),
-      box_predictor_4: extractBoxPredictorParams(4),
-      box_predictor_5: extractBoxPredictorParams(5)
-    };
-  }
-  return {
-    extractMobilenetV1Params,
-    extractPredictionLayerParams
-  };
-}
-function extractParamsFromWeigthMap11(weightMap) {
-  const paramMappings = [];
-  const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory8(weightMap, paramMappings);
-  const extra_dim = weightMap["Output/extra_dim"];
-  paramMappings.push({originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim"});
-  if (!isTensor3D(extra_dim)) {
-    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);
-  }
-  const params = {
-    mobilenetv1: extractMobilenetV1Params(),
-    prediction_layer: extractPredictionLayerParams(),
-    output_layer: {
-      extra_dim
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/ssdMobilenetv1/pointwiseConvLayer.js
-const tf29 = __toModule(require("@tensorflow/tfjs"));
-function pointwiseConvLayer(x, params, strides) {
-  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 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 tf30.tidy(() => {
-    let conv11;
-    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
-    const convPairParams = [
-      params.conv_1,
-      params.conv_2,
-      params.conv_3,
-      params.conv_4,
-      params.conv_5,
-      params.conv_6,
-      params.conv_7,
-      params.conv_8,
-      params.conv_9,
-      params.conv_10,
-      params.conv_11,
-      params.conv_12,
-      params.conv_13
-    ];
-    convPairParams.forEach((param, i) => {
-      const layerIdx = i + 1;
-      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
-      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
-      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
-      if (layerIdx === 11) {
-        conv11 = out;
-      }
-    });
-    if (conv11 === null) {
-      throw new Error("mobileNetV1 - output of conv layer 11 is null");
-    }
-    return {
-      out,
-      conv11
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/nonMaxSuppression.js
-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);
-  const suppressFunc = (x) => x <= iouThreshold ? 1 : 0;
-  const selected = [];
-  candidates.forEach((c) => {
-    if (selected.length >= outputSize) {
-      return;
-    }
-    const originalScore = c.score;
-    for (let j = selected.length - 1; j >= 0; --j) {
-      const iou3 = IOU(boxes, c.boxIndex, selected[j]);
-      if (iou3 === 0) {
-        continue;
-      }
-      c.score *= suppressFunc(iou3);
-      if (c.score <= scoreThreshold) {
-        break;
-      }
-    }
-    if (originalScore === c.score) {
-      selected.push(c.boxIndex);
-    }
-  });
-  return selected;
-}
-function IOU(boxes, i, j) {
-  const boxesData = boxes.arraySync();
-  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);
-  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);
-  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
-  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
-  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
-  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
-  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
-  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][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);
-}
-
-// build/src/ssdMobilenetv1/outputLayer.js
-const tf31 = __toModule(require("@tensorflow/tfjs"));
-function getCenterCoordinatesAndSizesLayer(x) {
-  const vec = tf31.unstack(tf31.transpose(x, [1, 0]));
-  const sizes = [
-    tf31.sub(vec[2], vec[0]),
-    tf31.sub(vec[3], vec[1])
-  ];
-  const centers = [
-    tf31.add(vec[0], tf31.div(sizes[0], tf31.scalar(2))),
-    tf31.add(vec[1], tf31.div(sizes[1], tf31.scalar(2)))
-  ];
-  return {
-    sizes,
-    centers
-  };
-}
-function decodeBoxesLayer(x0, x1) {
-  const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-  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 tf31.tidy(() => {
-    const batchSize = boxPredictions.shape[0];
-    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
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/boxPredictionLayer.js
-const tf32 = __toModule(require("@tensorflow/tfjs"));
-function boxPredictionLayer(x, params) {
-  return tf32.tidy(() => {
-    const batchSize = x.shape[0];
-    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
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/predictionLayer.js
-const tf33 = __toModule(require("@tensorflow/tfjs"));
-function predictionLayer(x, conv11, params) {
-  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]);
-    const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]);
-    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
-    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
-    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
-    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
-    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
-    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
-    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
-    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 = tf33.concat([
-      boxPrediction0.boxPredictionEncoding,
-      boxPrediction1.boxPredictionEncoding,
-      boxPrediction2.boxPredictionEncoding,
-      boxPrediction3.boxPredictionEncoding,
-      boxPrediction4.boxPredictionEncoding,
-      boxPrediction5.boxPredictionEncoding
-    ], 1);
-    const classPredictions = tf33.concat([
-      boxPrediction0.classPrediction,
-      boxPrediction1.classPrediction,
-      boxPrediction2.classPrediction,
-      boxPrediction3.classPrediction,
-      boxPrediction4.classPrediction,
-      boxPrediction5.classPrediction
-    ], 1);
-    return {
-      boxPredictions,
-      classPredictions
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/SsdMobilenetv1Options.js
-class SsdMobilenetv1Options {
-  constructor({minConfidence, maxResults} = {}) {
-    this._name = "SsdMobilenetv1Options";
-    this._minConfidence = minConfidence || 0.5;
-    this._maxResults = maxResults || 100;
-    if (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;
-  }
-}
-
-// build/src/ssdMobilenetv1/SsdMobilenetv1.js
-const tf34 = __toModule(require("@tensorflow/tfjs"));
-class SsdMobilenetv1 extends NeuralNetwork {
-  constructor() {
-    super("SsdMobilenetv1");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("SsdMobilenetv1 - load model before inference");
-    }
-    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);
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async locateFaces(input, options = {}) {
-    const {maxResults, minConfidence} = new SsdMobilenetv1Options(options);
-    const netInput = await toNetInput(input);
-    const {boxes: _boxes, scores: _scores} = this.forwardInput(netInput);
-    const boxes = _boxes[0];
-    const scores = _scores[0];
-    for (let i = 1; i < _boxes.length; i++) {
-      _boxes[i].dispose();
-      _scores[i].dispose();
-    }
-    const scoresData = Array.from(await scores.data());
-    const iouThreshold = 0.5;
-    const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-    const reshapedDims = netInput.getReshapedInputDimensions(0);
-    const inputSize = netInput.inputSize;
-    const padX = inputSize / reshapedDims.width;
-    const padY = inputSize / reshapedDims.height;
-    const boxesData = boxes.arraySync();
-    const results = indices.map((idx) => {
-      const [top, bottom] = [
-        Math.max(0, boxesData[idx][0]),
-        Math.min(1, boxesData[idx][2])
-      ].map((val) => val * padY);
-      const [left, right] = [
-        Math.max(0, boxesData[idx][1]),
-        Math.min(1, boxesData[idx][3])
-      ].map((val) => val * padX);
-      return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
-        height: netInput.getInputHeight(0),
-        width: netInput.getInputWidth(0)
-      });
-    });
-    boxes.dispose();
-    scores.dispose();
-    return results;
-  }
-  getDefaultModelName() {
-    return "ssd_mobilenetv1_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap11(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams11(weights);
-  }
-}
-
-// build/src/ssdMobilenetv1/index.js
-function createSsdMobilenetv1(weights) {
-  const net = new SsdMobilenetv1();
-  net.extractWeights(weights);
-  return net;
-}
-function createFaceDetectionNet(weights) {
-  return createSsdMobilenetv1(weights);
-}
-class FaceDetectionNet extends SsdMobilenetv1 {
-}
-
-// build/src/tinyYolov2/const.js
-const IOU_THRESHOLD = 0.4;
-const BOX_ANCHORS = [
-  new Point(0.738768, 0.874946),
-  new Point(2.42204, 2.65704),
-  new Point(4.30971, 7.04493),
-  new Point(10.246, 4.59428),
-  new Point(12.6868, 11.8741)
-];
-const BOX_ANCHORS_SEPARABLE = [
-  new Point(1.603231, 2.094468),
-  new Point(6.041143, 7.080126),
-  new Point(2.882459, 3.518061),
-  new Point(4.266906, 5.178857),
-  new Point(9.041765, 10.66308)
-];
-const MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
-
-// build/src/tinyYolov2/config.js
-const isNumber = (arg) => typeof arg === "number";
-function validateConfig(config2) {
-  if (!config2) {
-    throw new Error(`invalid config: ${config2}`);
-  }
-  if (typeof config2.withSeparableConvs !== "boolean") {
-    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
-  }
-  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) => 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(isNumber))) {
-    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
-  }
-}
-
-// build/src/tinyYolov2/leaky.js
-const tf35 = __toModule(require("@tensorflow/tfjs"));
-function leaky(x) {
-  return tf35.tidy(() => {
-    const min = tf35.mul(x, tf35.scalar(0.10000000149011612));
-    return tf35.add(tf35.relu(tf35.sub(x, min)), min);
-  });
-}
-
-// build/src/tinyYolov2/convWithBatchNorm.js
-const tf36 = __toModule(require("@tensorflow/tfjs"));
-function convWithBatchNorm(x, params) {
-  return tf36.tidy(() => {
-    let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid");
-    out = tf36.sub(out, params.bn.sub);
-    out = tf36.mul(out, params.bn.truediv);
-    out = tf36.add(out, params.conv.bias);
-    return leaky(out);
-  });
-}
-
-// build/src/tinyYolov2/depthwiseSeparableConv.js
-const tf37 = __toModule(require("@tensorflow/tfjs"));
-function depthwiseSeparableConv3(x, params) {
-  return tf37.tidy(() => {
-    let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = tf37.add(out, params.bias);
-    return leaky(out);
-  });
-}
-
-// build/src/tinyYolov2/extractParams.js
-const tf38 = __toModule(require("@tensorflow/tfjs"));
-function extractorsFactory9(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  function extractBatchNormParams(size, mappedPrefix) {
-    const sub6 = tf38.tensor1d(extractWeights(size));
-    const truediv = tf38.tensor1d(extractWeights(size));
-    paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-    return {sub: sub6, truediv};
-  }
-  function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
-    const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
-    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);
-    return {conv: conv3, bn};
-  }
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  return {
-    extractConvParams,
-    extractConvWithBatchNormParams,
-    extractSeparableConvParams
-  };
-}
-function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const paramMappings = [];
-  const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory9(extractWeights, paramMappings);
-  let params;
-  if (config2.withSeparableConvs) {
-    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-    const conv0 = config2.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0");
-    const conv1 = extractSeparableConvParams(s1, s2, "conv1");
-    const conv22 = extractSeparableConvParams(s2, s3, "conv2");
-    const conv3 = extractSeparableConvParams(s3, s4, "conv3");
-    const conv4 = extractSeparableConvParams(s4, s5, "conv4");
-    const conv5 = extractSeparableConvParams(s5, s6, "conv5");
-    const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0;
-    const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0;
-    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8");
-    params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-  } else {
-    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-    const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0");
-    const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1");
-    const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2");
-    const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3");
-    const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4");
-    const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5");
-    const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6");
-    const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7");
-    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8");
-    params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-  }
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {params, paramMappings};
-}
-
-// build/src/tinyYolov2/extractParamsFromWeigthMap.js
-function extractorsFactory10(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractBatchNormParams(prefix) {
-    const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
-    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-    return {sub: sub6, truediv};
-  }
-  function extractConvParams(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  }
-  function extractConvWithBatchNormParams(prefix) {
-    const conv3 = extractConvParams(`${prefix}/conv`);
-    const bn = extractBatchNormParams(`${prefix}/bn`);
-    return {conv: conv3, bn};
-  }
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  return {
-    extractConvParams,
-    extractConvWithBatchNormParams,
-    extractSeparableConvParams
-  };
-}
-function extractParamsFromWeigthMap13(weightMap, config2) {
-  const paramMappings = [];
-  const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory10(weightMap, paramMappings);
-  let params;
-  if (config2.withSeparableConvs) {
-    const numFilters = config2.filterSizes && config2.filterSizes.length || 9;
-    params = {
-      conv0: config2.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"),
-      conv1: extractSeparableConvParams("conv1"),
-      conv2: extractSeparableConvParams("conv2"),
-      conv3: extractSeparableConvParams("conv3"),
-      conv4: extractSeparableConvParams("conv4"),
-      conv5: extractSeparableConvParams("conv5"),
-      conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0,
-      conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0,
-      conv8: extractConvParams("conv8")
-    };
-  } else {
-    params = {
-      conv0: extractConvWithBatchNormParams("conv0"),
-      conv1: extractConvWithBatchNormParams("conv1"),
-      conv2: extractConvWithBatchNormParams("conv2"),
-      conv3: extractConvWithBatchNormParams("conv3"),
-      conv4: extractConvWithBatchNormParams("conv4"),
-      conv5: extractConvWithBatchNormParams("conv5"),
-      conv6: extractConvWithBatchNormParams("conv6"),
-      conv7: extractConvWithBatchNormParams("conv7"),
-      conv8: extractConvParams("conv8")
-    };
-  }
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/tinyYolov2/TinyYolov2Options.js
-var TinyYolov2SizeType;
-(function(TinyYolov2SizeType2) {
-  TinyYolov2SizeType2[TinyYolov2SizeType2["XS"] = 224] = "XS";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["SM"] = 320] = "SM";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["MD"] = 416] = "MD";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["LG"] = 608] = "LG";
-})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
-class TinyYolov2Options {
-  constructor({inputSize, scoreThreshold} = {}) {
-    this._name = "TinyYolov2Options";
-    this._inputSize = inputSize || 416;
-    this._scoreThreshold = scoreThreshold || 0.5;
-    if (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;
-  }
-}
-
-// build/src/tinyYolov2/TinyYolov2Base.js
-const tf39 = __toModule(require("@tensorflow/tfjs"));
-class TinyYolov2Base extends NeuralNetwork {
-  constructor(config2) {
-    super("TinyYolov2");
-    validateConfig(config2);
-    this._config = config2;
-  }
-  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(x, params) {
-    let out = convWithBatchNorm(x, params.conv0);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv1);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv2);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv3);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv4);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv5);
-    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 = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv1);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv2);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv3);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv4);
-    out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv5);
-    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);
-  }
-  forwardInput(input, inputSize) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyYolov2 - load model before inference");
-    }
-    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(tf39.scalar(256));
-      return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
-    });
-  }
-  async forward(input, inputSize) {
-    return await this.forwardInput(await toNetInput(input), inputSize);
-  }
-  async detect(input, forwardParams = {}) {
-    const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
-    const netInput = await toNetInput(input);
-    const out = await this.forwardInput(netInput, inputSize);
-    const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims());
-    const inputDimensions = {
-      width: netInput.getInputWidth(0),
-      height: netInput.getInputHeight(0)
-    };
-    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
-    out.dispose();
-    out0.dispose();
-    const boxes = results.map((res) => res.box);
-    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 = 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;
-  }
-  getDefaultModelName() {
-    return "";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap13(weightMap, this.config);
-  }
-  extractParams(weights) {
-    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;
-    const numFilters = filterSizes ? filterSizes.length : void 0;
-    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
-      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);
-    }
-    return extractParams13(weights, this.config, this.boxEncodingSize, filterSizes);
-  }
-  async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) {
-    const {width, height} = inputBlobDimensions;
-    const inputSize = Math.max(width, height);
-    const correctionFactorX = inputSize / width;
-    const correctionFactorY = inputSize / height;
-    const numCells = outputTensor.shape[1];
-    const numBoxes = this.config.anchors.length;
-    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 ? 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 = [];
-    const scoresData = await scoresTensor.array();
-    const boxesData = await boxesTensor.array();
-    for (let row = 0; row < numCells; row++) {
-      for (let col = 0; col < numCells; col++) {
-        for (let anchor = 0; anchor < numBoxes; anchor++) {
-          const score = sigmoid(scoresData[row][col][anchor][0]);
-          if (!scoreThreshold || score > scoreThreshold) {
-            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({
-              box: new BoundingBox(x, y, x + width2, y + height2),
-              score,
-              classScore: score * classScore,
-              label,
-              ...pos
-            });
-          }
-        }
-      }
-    }
-    boxesTensor.dispose();
-    scoresTensor.dispose();
-    classScoresTensor.dispose();
-    return results;
-  }
-  async extractPredictedClass(classesTensor, pos) {
-    const {row, col, anchor} = pos;
-    const classesData = await classesTensor.array();
-    return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
-      classScore,
-      label
-    })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
-  }
-}
-TinyYolov2Base.DEFAULT_FILTER_SIZES = [
-  3,
-  16,
-  32,
-  64,
-  128,
-  256,
-  512,
-  1024,
-  1024
-];
-
-// build/src/tinyYolov2/TinyYolov2.js
-class TinyYolov2 extends TinyYolov2Base {
-  constructor(withSeparableConvs = true) {
-    const config2 = Object.assign({}, {
-      withSeparableConvs,
-      iouThreshold: IOU_THRESHOLD,
-      classes: ["face"]
-    }, withSeparableConvs ? {
-      anchors: BOX_ANCHORS_SEPARABLE,
-      meanRgb: MEAN_RGB_SEPARABLE
-    } : {
-      anchors: BOX_ANCHORS,
-      withClassScores: true
-    });
-    super(config2);
-  }
-  get withSeparableConvs() {
-    return this.config.withSeparableConvs;
-  }
-  get anchors() {
-    return this.config.anchors;
-  }
-  async locateFaces(input, forwardParams) {
-    const objectDetections = await this.detect(input, forwardParams);
-    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-  }
-  getDefaultModelName() {
-    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return super.extractParamsFromWeigthMap(weightMap);
-  }
-}
-
-// build/src/tinyYolov2/types.js
-
-// build/src/tinyYolov2/index.js
-function createTinyYolov2(weights, withSeparableConvs = true) {
-  const net = new TinyYolov2(withSeparableConvs);
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/tinyFaceDetector/TinyFaceDetectorOptions.js
-class TinyFaceDetectorOptions extends TinyYolov2Options {
-  constructor() {
-    super(...arguments);
-    this._name = "TinyFaceDetectorOptions";
-  }
-}
-
-// build/src/globalApi/ComposableTask.js
-class ComposableTask {
-  async then(onfulfilled) {
-    return onfulfilled(await this.run());
-  }
-  async run() {
-    throw new Error("ComposableTask - run is not implemented");
-  }
-}
-
-// 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 tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
-  const results = await computeResults(faces);
-  faces.forEach((f) => f instanceof tf40.Tensor && f.dispose());
-  return results;
-}
-async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
-  return extractAllFacesAndComputeResults([parentResult], input, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment);
-}
-
-// build/src/tinyFaceDetector/const.js
-const IOU_THRESHOLD2 = 0.4;
-const BOX_ANCHORS2 = [
-  new Point(1.603231, 2.094468),
-  new Point(6.041143, 7.080126),
-  new Point(2.882459, 3.518061),
-  new Point(4.266906, 5.178857),
-  new Point(9.041765, 10.66308)
-];
-const MEAN_RGB = [117.001, 114.697, 97.404];
-
-// build/src/tinyFaceDetector/TinyFaceDetector.js
-class TinyFaceDetector extends TinyYolov2Base {
-  constructor() {
-    const config2 = {
-      withSeparableConvs: true,
-      iouThreshold: IOU_THRESHOLD2,
-      classes: ["face"],
-      anchors: BOX_ANCHORS2,
-      meanRgb: MEAN_RGB,
-      isFirstLayerConv2d: true,
-      filterSizes: [3, 16, 32, 64, 128, 256, 512]
-    };
-    super(config2);
-  }
-  get anchors() {
-    return this.config.anchors;
-  }
-  async locateFaces(input, forwardParams) {
-    const objectDetections = await this.detect(input, forwardParams);
-    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-  }
-  getDefaultModelName() {
-    return "tiny_face_detector_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return super.extractParamsFromWeigthMap(weightMap);
-  }
-}
-
-// build/src/globalApi/nets.js
-const nets = {
-  ssdMobilenetv1: new SsdMobilenetv1(),
-  tinyFaceDetector: new TinyFaceDetector(),
-  tinyYolov2: new TinyYolov2(),
-  faceLandmark68Net: new FaceLandmark68Net(),
-  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
-  faceRecognitionNet: new FaceRecognitionNet(),
-  faceExpressionNet: new FaceExpressionNet(),
-  ageGenderNet: new AgeGenderNet()
-};
-const ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-const tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-const tinyYolov23 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-const detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-const detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-const computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-const recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-const predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-const loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-const loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-const loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-const loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-const loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-const loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-const loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-const loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-const loadFaceDetectionModel = loadSsdMobilenetv1Model;
-const locateFaces = ssdMobilenetv1;
-const detectLandmarks = detectFaceLandmarks;
-
-// build/src/globalApi/PredictFaceExpressionsTask.js
-class PredictFaceExpressionsTaskBase extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-}
-class PredictAllFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const faceExpressionsByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.faceExpressionNet.predictExpressions(face))), this.extractedFaces);
-    return parentResults.map((parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]));
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderTask(this, this.input);
-  }
-}
-class PredictSingleFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const faceExpressions = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceExpressionNet.predictExpressions(face), this.extractedFaces);
-    return extendWithFaceExpressions(parentResult, faceExpressions);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderTask(this, this.input);
-  }
-}
-class PredictAllFaceExpressionsWithFaceAlignmentTask extends PredictAllFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class PredictSingleFaceExpressionsWithFaceAlignmentTask extends PredictSingleFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/PredictAgeAndGenderTask.js
-class PredictAgeAndGenderTaskBase extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-}
-class PredictAllAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const ageAndGenderByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), this.extractedFaces);
-    return parentResults.map((parentResult, i) => {
-      const {age, gender, genderProbability} = ageAndGenderByFace[i];
-      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-    });
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsTask(this, this.input);
-  }
-}
-class PredictSingleAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const {age, gender, genderProbability} = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.ageGenderNet.predictAgeAndGender(face), this.extractedFaces);
-    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsTask(this, this.input);
-  }
-}
-class PredictAllAgeAndGenderWithFaceAlignmentTask extends PredictAllAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class PredictSingleAgeAndGenderWithFaceAlignmentTask extends PredictSingleAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/ComputeFaceDescriptorsTasks.js
-class ComputeFaceDescriptorsTaskBase extends ComposableTask {
-  constructor(parentTask, input) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-  }
-}
-class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const descriptors = await extractAllFacesAndComputeResults(parentResults, this.input, (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), null, (parentResult) => parentResult.landmarks.align(null, {useDlibAlignment: true}));
-    return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-}
-class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const descriptor = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), null, (parentResult2) => parentResult2.landmarks.align(null, {useDlibAlignment: true}));
-    return extendWithFaceDescriptor(parentResult, descriptor);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/DetectFaceLandmarksTasks.js
-const tf41 = __toModule(require("@tensorflow/tfjs"));
-class DetectFaceLandmarksTaskBase extends ComposableTask {
-  constructor(parentTask, input, useTinyLandmarkNet) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.useTinyLandmarkNet = useTinyLandmarkNet;
-  }
-  get landmarkNet() {
-    return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net;
-  }
-}
-class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const detections = parentResults.map((res) => res.detection);
-    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 tf41.Tensor && f.dispose());
-    return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const {detection} = parentResult;
-    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 tf41.Tensor && f.dispose());
-    return extendWithFaceLandmarks(parentResult, landmarks);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/DetectFacesTasks.js
-class DetectFacesTaskBase extends ComposableTask {
-  constructor(input, options = new SsdMobilenetv1Options()) {
-    super();
-    this.input = input;
-    this.options = options;
-  }
-}
-class DetectAllFacesTask extends DetectFacesTaskBase {
-  async run() {
-    const {input, options} = this;
-    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions ? (input2) => nets.tinyFaceDetector.locateFaces(input2, options) : options instanceof SsdMobilenetv1Options ? (input2) => nets.ssdMobilenetv1.locateFaces(input2, options) : options instanceof TinyYolov2Options ? (input2) => nets.tinyYolov2.locateFaces(input2, options) : null;
-    if (!faceDetectionFunction) {
-      throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");
-    }
-    return faceDetectionFunction(input);
-  }
-  runAndExtendWithFaceDetections() {
-    return new Promise(async (res) => {
-      const detections = await this.run();
-      return res(detections.map((detection) => extendWithFaceDetection({}, detection)));
-    });
-  }
-  withFaceLandmarks(useTinyLandmarkNet = false) {
-    return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);
-  }
-}
-class DetectSingleFaceTask extends DetectFacesTaskBase {
-  async run() {
-    const faceDetections = await new DetectAllFacesTask(this.input, this.options);
-    let faceDetectionWithHighestScore = faceDetections[0];
-    faceDetections.forEach((faceDetection) => {
-      if (faceDetection.score > faceDetectionWithHighestScore.score) {
-        faceDetectionWithHighestScore = faceDetection;
-      }
-    });
-    return faceDetectionWithHighestScore;
-  }
-  runAndExtendWithFaceDetection() {
-    return new Promise(async (res) => {
-      const detection = await this.run();
-      return res(detection ? extendWithFaceDetection({}, detection) : void 0);
-    });
-  }
-  withFaceLandmarks(useTinyLandmarkNet = false) {
-    return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);
-  }
-}
-
-// build/src/globalApi/detectFaces.js
-function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-  return new DetectSingleFaceTask(input, options);
-}
-function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-  return new DetectAllFacesTask(input, options);
-}
-
-// build/src/globalApi/allFaces.js
-async function allFacesSsdMobilenetv1(input, minConfidence) {
-  console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead");
-  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? {minConfidence} : {})).withFaceLandmarks().withFaceDescriptors();
-}
-async function allFacesTinyYolov2(input, forwardParams = {}) {
-  console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead");
-  return await detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors();
-}
-const allFaces = allFacesSsdMobilenetv1;
-
-// build/src/euclideanDistance.js
-function euclideanDistance(arr1, arr2) {
-  if (arr1.length !== arr2.length)
-    throw new Error("euclideanDistance: arr1.length !== arr2.length");
-  const desc1 = Array.from(arr1);
-  const desc2 = Array.from(arr2);
-  return Math.sqrt(desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + Math.pow(diff, 2), 0));
-}
-
-// build/src/globalApi/FaceMatcher.js
-class FaceMatcher {
-  constructor(inputs, distanceThreshold = 0.6) {
-    this._distanceThreshold = distanceThreshold;
-    const inputArray = Array.isArray(inputs) ? inputs : [inputs];
-    if (!inputArray.length) {
-      throw new Error(`FaceRecognizer.constructor - expected atleast one input`);
-    }
-    let count = 1;
-    const createUniqueLabel = () => `person ${count++}`;
-    this._labeledDescriptors = inputArray.map((desc) => {
-      if (desc instanceof LabeledFaceDescriptors) {
-        return desc;
-      }
-      if (desc instanceof Float32Array) {
-        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
-      }
-      if (desc.descriptor && desc.descriptor instanceof Float32Array) {
-        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) {
-    return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);
-  }
-  matchDescriptor(queryDescriptor) {
-    return this.labeledDescriptors.map(({descriptors, label}) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr);
-  }
-  findBestMatch(queryDescriptor) {
-    const bestMatch = this.matchDescriptor(queryDescriptor);
-    return bestMatch.distance < this.distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance);
-  }
-  toJSON() {
-    return {
-      distanceThreshold: this.distanceThreshold,
-      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())
-    };
-  }
-  static fromJSON(json) {
-    const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld));
-    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);
-  }
-}
-
-// build/src/globalApi/types.js
-
-// build/src/globalApi/index.js
-
-// build/src/tinyFaceDetector/index.js
-function createTinyFaceDetector(weights) {
-  const net = new TinyFaceDetector();
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/resizeResults.js
-function resizeResults(results, dimensions) {
-  const {width, height} = new Dimensions(dimensions.width, dimensions.height);
-  if (width <= 0 || height <= 0) {
-    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width, height})}`);
-  }
-  if (Array.isArray(results)) {
-    return results.map((obj) => resizeResults(obj, {width, height}));
-  }
-  if (isWithFaceLandmarks(results)) {
-    const resizedDetection = results.detection.forSize(width, height);
-    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
-    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
-  }
-  if (isWithFaceDetection(results)) {
-    return extendWithFaceDetection(results, results.detection.forSize(width, height));
-  }
-  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
-    return results.forSize(width, height);
-  }
-  return results;
-}
-
-// build/package.json
-var version = "0.5.3";
-
-// build/src/index.js
-__export(exports, {
-  AgeGenderNet: () => AgeGenderNet,
-  BoundingBox: () => BoundingBox,
-  Box: () => Box,
-  ComposableTask: () => ComposableTask,
-  ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask,
-  ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase,
-  ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask,
-  DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask,
-  DetectAllFacesTask: () => DetectAllFacesTask,
-  DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase,
-  DetectFacesTaskBase: () => DetectFacesTaskBase,
-  DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask,
-  DetectSingleFaceTask: () => DetectSingleFaceTask,
-  Dimensions: () => Dimensions,
-  FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS,
-  FaceDetection: () => FaceDetection,
-  FaceDetectionNet: () => FaceDetectionNet,
-  FaceExpressionNet: () => FaceExpressionNet,
-  FaceExpressions: () => FaceExpressions,
-  FaceLandmark68Net: () => FaceLandmark68Net,
-  FaceLandmark68TinyNet: () => FaceLandmark68TinyNet,
-  FaceLandmarkNet: () => FaceLandmarkNet,
-  FaceLandmarks: () => FaceLandmarks,
-  FaceLandmarks5: () => FaceLandmarks5,
-  FaceLandmarks68: () => FaceLandmarks68,
-  FaceMatch: () => FaceMatch,
-  FaceMatcher: () => FaceMatcher,
-  FaceRecognitionNet: () => FaceRecognitionNet,
-  Gender: () => Gender,
-  LabeledBox: () => LabeledBox,
-  LabeledFaceDescriptors: () => LabeledFaceDescriptors,
-  NetInput: () => NetInput,
-  NeuralNetwork: () => NeuralNetwork,
-  ObjectDetection: () => ObjectDetection,
-  Point: () => Point,
-  PredictedBox: () => PredictedBox,
-  Rect: () => Rect,
-  SsdMobilenetv1: () => SsdMobilenetv1,
-  SsdMobilenetv1Options: () => SsdMobilenetv1Options,
-  TinyFaceDetector: () => TinyFaceDetector,
-  TinyFaceDetectorOptions: () => TinyFaceDetectorOptions,
-  TinyYolov2: () => TinyYolov2,
-  TinyYolov2Options: () => TinyYolov2Options,
-  TinyYolov2SizeType: () => TinyYolov2SizeType,
-  allFaces: () => allFaces,
-  allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1,
-  allFacesTinyYolov2: () => allFacesTinyYolov2,
-  awaitMediaLoaded: () => awaitMediaLoaded,
-  bufferToImage: () => bufferToImage,
-  computeFaceDescriptor: () => computeFaceDescriptor,
-  createCanvas: () => createCanvas,
-  createCanvasFromMedia: () => createCanvasFromMedia,
-  createFaceDetectionNet: () => createFaceDetectionNet,
-  createFaceRecognitionNet: () => createFaceRecognitionNet,
-  createSsdMobilenetv1: () => createSsdMobilenetv1,
-  createTinyFaceDetector: () => createTinyFaceDetector,
-  createTinyYolov2: () => createTinyYolov2,
-  detectAllFaces: () => detectAllFaces,
-  detectFaceLandmarks: () => detectFaceLandmarks,
-  detectFaceLandmarksTiny: () => detectFaceLandmarksTiny,
-  detectLandmarks: () => detectLandmarks,
-  detectSingleFace: () => detectSingleFace,
-  draw: () => draw_exports,
-  env: () => env,
-  euclideanDistance: () => euclideanDistance,
-  extendWithAge: () => extendWithAge,
-  extendWithFaceDescriptor: () => extendWithFaceDescriptor,
-  extendWithFaceDetection: () => extendWithFaceDetection,
-  extendWithFaceExpressions: () => extendWithFaceExpressions,
-  extendWithFaceLandmarks: () => extendWithFaceLandmarks,
-  extendWithGender: () => extendWithGender,
-  extractFaceTensors: () => extractFaceTensors,
-  extractFaces: () => extractFaces,
-  fetchImage: () => fetchImage,
-  fetchJson: () => fetchJson,
-  fetchNetWeights: () => fetchNetWeights,
-  fetchOrThrow: () => fetchOrThrow,
-  getContext2dOrThrow: () => getContext2dOrThrow,
-  getMediaDimensions: () => getMediaDimensions,
-  imageTensorToCanvas: () => imageTensorToCanvas,
-  imageToSquare: () => imageToSquare,
-  inverseSigmoid: () => inverseSigmoid,
-  iou: () => iou,
-  isMediaElement: () => isMediaElement,
-  isMediaLoaded: () => isMediaLoaded,
-  isWithAge: () => isWithAge,
-  isWithFaceDetection: () => isWithFaceDetection,
-  isWithFaceExpressions: () => isWithFaceExpressions,
-  isWithFaceLandmarks: () => isWithFaceLandmarks,
-  isWithGender: () => isWithGender,
-  loadAgeGenderModel: () => loadAgeGenderModel,
-  loadFaceDetectionModel: () => loadFaceDetectionModel,
-  loadFaceExpressionModel: () => loadFaceExpressionModel,
-  loadFaceLandmarkModel: () => loadFaceLandmarkModel,
-  loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel,
-  loadFaceRecognitionModel: () => loadFaceRecognitionModel,
-  loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model,
-  loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel,
-  loadTinyYolov2Model: () => loadTinyYolov2Model,
-  loadWeightMap: () => loadWeightMap,
-  locateFaces: () => locateFaces,
-  matchDimensions: () => matchDimensions,
-  minBbox: () => minBbox,
-  nets: () => nets,
-  nonMaxSuppression: () => nonMaxSuppression,
-  normalize: () => normalize,
-  padToSquare: () => padToSquare,
-  predictAgeAndGender: () => predictAgeAndGender,
-  recognizeFaceExpressions: () => recognizeFaceExpressions,
-  resizeResults: () => resizeResults,
-  resolveInput: () => resolveInput,
-  shuffleArray: () => shuffleArray,
-  sigmoid: () => sigmoid,
-  ssdMobilenetv1: () => ssdMobilenetv1,
-  tf: () => tf42,
-  tinyFaceDetector: () => tinyFaceDetector,
-  tinyYolov2: () => tinyYolov23,
-  toNetInput: () => toNetInput,
-  utils: () => utils_exports,
-  validateConfig: () => validateConfig,
-  version: () => version2
-});
-const tf42 = __toModule(require("@tensorflow/tfjs"));
-const node = typeof process !== "undefined" ? process.version : false;
-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
deleted file mode 100644
index 8ff9d11..0000000
--- a/dist/face-api.cjs.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["build/src/env/../../../src/env/isNodejs.ts", "build/src/draw/../../../src/draw/drawContour.ts", "build/src/classes/../../../src/classes/Dimensions.ts", "build/src/utils/../../../src/utils/index.ts", "build/src/classes/../../../src/classes/Point.ts", "build/src/classes/../../../src/classes/Box.ts", "build/src/classes/../../../src/classes/BoundingBox.ts", "build/src/classes/../../../src/classes/ObjectDetection.ts", "build/src/classes/../../../src/classes/FaceDetection.ts", "build/src/ops/../../../src/ops/iou.ts", "build/src/ops/../../../src/ops/minBbox.ts", "build/src/ops/../../../src/ops/nonMaxSuppression.ts", "build/src/ops/../../../src/ops/normalize.ts", "build/src/ops/../../../src/ops/padToSquare.ts", "build/src/ops/../../../src/ops/shuffleArray.ts", "build/src/ops/../../../src/ops/index.ts", "build/src/classes/../../../src/classes/Rect.ts", "build/src/classes/../../../src/classes/FaceLandmarks.ts", "build/src/classes/../../../src/classes/FaceLandmarks5.ts", "build/src/classes/../../../src/classes/FaceLandmarks68.ts", "build/src/classes/../../../src/classes/FaceMatch.ts", "build/src/classes/../../../src/classes/LabeledBox.ts", "build/src/classes/../../../src/classes/LabeledFaceDescriptors.ts", "build/src/classes/../../../src/classes/PredictedBox.ts", "build/src/factories/../../../src/factories/WithFaceDetection.ts", "build/src/env/../../../src/env/createBrowserEnv.ts", "build/src/env/../../../src/env/createFileSystem.ts", "build/src/env/../../../src/env/createNodejsEnv.ts", "build/src/env/../../../src/env/isBrowser.ts", "build/src/env/../../../src/env/index.ts", "build/src/dom/../../../src/dom/resolveInput.ts", "build/src/dom/../../../src/dom/getContext2dOrThrow.ts", "build/src/draw/../../../src/draw/DrawTextField.ts", "build/src/draw/../../../src/draw/DrawBox.ts", "build/src/draw/../../../src/draw/drawDetections.ts", "build/src/dom/../../../src/dom/isMediaLoaded.ts", "build/src/dom/../../../src/dom/awaitMediaLoaded.ts", "build/src/dom/../../../src/dom/bufferToImage.ts", "build/src/dom/../../../src/dom/getMediaDimensions.ts", "build/src/dom/../../../src/dom/createCanvas.ts", "build/src/dom/../../../src/dom/imageTensorToCanvas.ts", "build/src/dom/../../../src/dom/isMediaElement.ts", "build/src/dom/../../../src/dom/imageToSquare.ts", "build/src/dom/../../../src/dom/NetInput.ts", "build/src/dom/../../../src/dom/toNetInput.ts", "build/src/dom/../../../src/dom/extractFaces.ts", "build/src/dom/../../../src/dom/extractFaceTensors.ts", "build/src/dom/../../../src/dom/fetchOrThrow.ts", "build/src/dom/../../../src/dom/fetchImage.ts", "build/src/dom/../../../src/dom/fetchJson.ts", "build/src/dom/../../../src/dom/fetchNetWeights.ts", "build/src/common/../../../src/common/getModelUris.ts", "build/src/dom/../../../src/dom/loadWeightMap.ts", "build/src/dom/../../../src/dom/matchDimensions.ts", "build/src/../../src/NeuralNetwork.ts", "build/src/common/../../../src/common/depthwiseSeparableConv.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/denseBlock.ts", "build/src/common/../../../src/common/convLayer.ts", "build/src/common/../../../src/common/disposeUnusedWeightTensors.ts", "build/src/common/../../../src/common/extractConvParamsFactory.ts", "build/src/common/../../../src/common/extractFCParamsFactory.ts", "build/src/common/../../../src/common/types.ts", "build/src/common/../../../src/common/extractSeparableConvParamsFactory.ts", "build/src/common/../../../src/common/extractWeightEntryFactory.ts", "build/src/common/../../../src/common/extractWeightsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractorsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParams.ts", "build/src/common/../../../src/common/loadConvParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/loadParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "build/src/common/../../../src/common/fullyConnectedLayer.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParams.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParamsFromWeigthMap.ts", "build/src/faceProcessor/../../../src/faceProcessor/util.ts", "build/src/faceProcessor/../../../src/faceProcessor/FaceProcessor.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressions.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressionNet.ts", "build/src/factories/../../../src/factories/WithFaceExpressions.ts", "build/src/draw/../../../src/draw/drawFaceExpressions.ts", "build/src/factories/../../../src/factories/WithFaceLandmarks.ts", "build/src/draw/../../../src/draw/DrawFaceLandmarks.ts", "build/src/draw/../../../src/draw/index.ts", "build/src/xception/../../../src/xception/extractParams.ts", "build/src/xception/../../../src/xception/extractParamsFromWeigthMap.ts", "build/src/xception/../../../src/xception/TinyXception.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParams.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParamsFromWeigthMap.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/types.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/AgeGenderNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68Net.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/index.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/scaleLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/convLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParams.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/residualLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/FaceRecognitionNet.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/index.ts", "build/src/factories/../../../src/factories/WithFaceDescriptor.ts", "build/src/factories/../../../src/factories/WithAge.ts", "build/src/factories/../../../src/factories/WithGender.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParams.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "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", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParams.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParamsFromWeigthMap.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Options.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Base.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/index.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "build/src/globalApi/../../../src/globalApi/ComposableTask.ts", "build/src/globalApi/../../../src/globalApi/extractFacesAndComputeResults.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/const.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetector.ts", "build/src/globalApi/../../../src/globalApi/nets.ts", "build/src/globalApi/../../../src/globalApi/PredictFaceExpressionsTask.ts", "build/src/globalApi/../../../src/globalApi/PredictAgeAndGenderTask.ts", "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", "build/src/../../src/index.ts"],
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-  "names": []
-}
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index 5853e94..1de231a 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -725,7 +725,7 @@ function extendWithFaceDetection(sourceObj, detection) {
 
 // build/src/env/createBrowserEnv.js
 function createBrowserEnv() {
-  const fetch = window["fetch"] || function() {
+  const fetch2 = window["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for browser environment");
   };
   const readFile = function() {
@@ -739,7 +739,7 @@ function createBrowserEnv() {
     Video: HTMLVideoElement,
     createCanvasElement: () => document.createElement("canvas"),
     createImageElement: () => document.createElement("img"),
-    fetch,
+    fetch: fetch2,
     readFile
   };
 }
@@ -784,7 +784,7 @@ function createNodejsEnv() {
     }
     throw new Error("createImageElement - missing Image implementation for nodejs environment");
   };
-  const fetch = global["fetch"] || function() {
+  const fetch2 = global["fetch"] || function() {
     throw new Error("fetch - missing fetch implementation for nodejs environment");
   };
   const fileSystem = createFileSystem();
@@ -801,7 +801,7 @@ function createNodejsEnv() {
     },
     createCanvasElement,
     createImageElement,
-    fetch,
+    fetch: fetch2,
     ...fileSystem
   };
 }
@@ -822,8 +822,8 @@ function getEnv() {
   }
   return environment;
 }
-function setEnv(env16) {
-  environment = env16;
+function setEnv(env17) {
+  environment = env17;
 }
 function initialize() {
   if (isBrowser()) {
@@ -833,22 +833,22 @@ function initialize() {
     return setEnv(createNodejsEnv());
   }
 }
-function monkeyPatch(env16) {
+function monkeyPatch(env17) {
   if (!environment) {
     initialize();
   }
   if (!environment) {
     throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
   }
-  const {Canvas = environment.Canvas, Image = environment.Image} = env16;
+  const {Canvas = environment.Canvas, Image = environment.Image} = env17;
   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;
+  environment.createCanvasElement = env17.createCanvasElement || (() => new Canvas());
+  environment.createImageElement = env17.createImageElement || (() => new Image());
+  environment.ImageData = env17.ImageData || environment.ImageData;
+  environment.Video = env17.Video || environment.Video;
+  environment.fetch = env17.fetch || environment.fetch;
+  environment.readFile = env17.readFile || environment.readFile;
 }
 const env = {
   getEnv,
@@ -1303,8 +1303,8 @@ async function extractFaceTensors(imageTensor, detections) {
 
 // build/src/dom/fetchOrThrow.js
 async function fetchOrThrow(url, init) {
-  const fetch = env.getEnv().fetch;
-  const res = await fetch(url, init);
+  const fetch2 = env.getEnv().fetch;
+  const res = await fetch2(url, init);
   if (!(res.status < 400)) {
     throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
   }
@@ -4403,11 +4403,37 @@ function resizeResults(results, dimensions) {
 // build/package.json
 var version = "0.5.3";
 
+// build/src/Platform.js
+class PlatformBrowser {
+  fetch(path, init) {
+    return fetch(path, init);
+  }
+  now() {
+    return performance.now();
+  }
+  encode(text, encoding) {
+    if (encoding !== "utf-8" && encoding !== "utf8") {
+      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
+    }
+    if (this.textEncoder == null) {
+      this.textEncoder = new TextEncoder();
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes, encoding) {
+    return new TextDecoder(encoding).decode(bytes);
+  }
+}
+
 // build/src/index.js
-import * as tf42 from "@tensorflow/tfjs";
-const node = typeof process !== "undefined" ? process.version : false;
-const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
+import {
+  env as env16
+}, * as tf42 from "@tensorflow/tfjs";
+const node = typeof process !== "undefined";
+const browser3 = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined";
 const version2 = {faceapi: version, node, browser: browser3};
+if (!env16().platform && env16().get("IS_BROWSER"))
+  env16().setPlatform("browser", new PlatformBrowser());
 export {
   AgeGenderNet,
   BoundingBox,
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index ce37833..c754d50 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["build/src/env/../../../src/env/isNodejs.ts", "build/src/draw/../../../src/draw/drawContour.ts", "build/src/classes/../../../src/classes/Dimensions.ts", "build/src/utils/../../../src/utils/index.ts", "build/src/classes/../../../src/classes/Point.ts", "build/src/classes/../../../src/classes/Box.ts", "build/src/classes/../../../src/classes/BoundingBox.ts", "build/src/classes/../../../src/classes/ObjectDetection.ts", "build/src/classes/../../../src/classes/FaceDetection.ts", "build/src/ops/../../../src/ops/iou.ts", "build/src/ops/../../../src/ops/minBbox.ts", "build/src/ops/../../../src/ops/nonMaxSuppression.ts", "build/src/ops/../../../src/ops/normalize.ts", "build/src/ops/../../../src/ops/padToSquare.ts", "build/src/ops/../../../src/ops/shuffleArray.ts", "build/src/ops/../../../src/ops/index.ts", "build/src/classes/../../../src/classes/Rect.ts", "build/src/classes/../../../src/classes/FaceLandmarks.ts", "build/src/classes/../../../src/classes/FaceLandmarks5.ts", "build/src/classes/../../../src/classes/FaceLandmarks68.ts", "build/src/classes/../../../src/classes/FaceMatch.ts", "build/src/classes/../../../src/classes/LabeledBox.ts", "build/src/classes/../../../src/classes/LabeledFaceDescriptors.ts", "build/src/classes/../../../src/classes/PredictedBox.ts", "build/src/factories/../../../src/factories/WithFaceDetection.ts", "build/src/env/../../../src/env/createBrowserEnv.ts", "build/src/env/../../../src/env/createFileSystem.ts", "build/src/env/../../../src/env/createNodejsEnv.ts", "build/src/env/../../../src/env/isBrowser.ts", "build/src/env/../../../src/env/index.ts", "build/src/dom/../../../src/dom/resolveInput.ts", "build/src/dom/../../../src/dom/getContext2dOrThrow.ts", "build/src/draw/../../../src/draw/DrawTextField.ts", "build/src/draw/../../../src/draw/DrawBox.ts", "build/src/draw/../../../src/draw/drawDetections.ts", "build/src/dom/../../../src/dom/isMediaLoaded.ts", "build/src/dom/../../../src/dom/awaitMediaLoaded.ts", 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   "names": []
 }
diff --git a/dist/face-api.iife.js b/dist/face-api.iife.js
deleted file mode 100644
index 755d9ee..0000000
--- a/dist/face-api.iife.js
+++ /dev/null
@@ -1,4350 +0,0 @@
-var faceapi = (() => {
-  var __defineProperty = Object.defineProperty;
-  var __hasOwnProperty = Object.prototype.hasOwnProperty;
-  var __commonJS = (callback, module) => () => {
-    if (!module) {
-      module = {exports: {}};
-      callback(module.exports, module);
-    }
-    return module.exports;
-  };
-  var __markAsModule = (target) => {
-    return __defineProperty(target, "__esModule", {value: true});
-  };
-  var __export = (target, all) => {
-    __markAsModule(target);
-    for (var name in all)
-      __defineProperty(target, name, {get: all[name], enumerable: true});
-  };
-  var __exportStar = (target, module) => {
-    __markAsModule(target);
-    if (typeof module === "object" || typeof module === "function") {
-      for (let key in module)
-        if (!__hasOwnProperty.call(target, key) && key !== "default")
-          __defineProperty(target, key, {get: () => module[key], enumerable: true});
-    }
-    return target;
-  };
-  var __toModule = (module) => {
-    if (module && module.__esModule)
-      return module;
-    return __exportStar(__defineProperty({}, "default", {value: module, enumerable: true}), module);
-  };
-
-  // build/src/env/isNodejs.js
-  var require_isNodejs = __commonJS((exports, module) => {
-    __export(exports, {
-      isNodejs: () => isNodejs3
-    });
-    function isNodejs3() {
-      return typeof global === "object" && true && typeof module !== "undefined" && typeof process !== "undefined" && !!process.version;
-    }
-  });
-
-  // build/src/index.js
-  var require_src = __commonJS((exports) => {
-    __export(exports, {
-      AgeGenderNet: () => AgeGenderNet,
-      BoundingBox: () => BoundingBox,
-      Box: () => Box,
-      ComposableTask: () => ComposableTask,
-      ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask,
-      ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase,
-      ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask,
-      DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask,
-      DetectAllFacesTask: () => DetectAllFacesTask,
-      DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase,
-      DetectFacesTaskBase: () => DetectFacesTaskBase,
-      DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask,
-      DetectSingleFaceTask: () => DetectSingleFaceTask,
-      Dimensions: () => Dimensions,
-      FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS,
-      FaceDetection: () => FaceDetection,
-      FaceDetectionNet: () => FaceDetectionNet,
-      FaceExpressionNet: () => FaceExpressionNet,
-      FaceExpressions: () => FaceExpressions,
-      FaceLandmark68Net: () => FaceLandmark68Net,
-      FaceLandmark68TinyNet: () => FaceLandmark68TinyNet,
-      FaceLandmarkNet: () => FaceLandmarkNet,
-      FaceLandmarks: () => FaceLandmarks,
-      FaceLandmarks5: () => FaceLandmarks5,
-      FaceLandmarks68: () => FaceLandmarks68,
-      FaceMatch: () => FaceMatch,
-      FaceMatcher: () => FaceMatcher,
-      FaceRecognitionNet: () => FaceRecognitionNet,
-      Gender: () => Gender,
-      LabeledBox: () => LabeledBox,
-      LabeledFaceDescriptors: () => LabeledFaceDescriptors,
-      NetInput: () => NetInput,
-      NeuralNetwork: () => NeuralNetwork,
-      ObjectDetection: () => ObjectDetection,
-      Point: () => Point,
-      PredictedBox: () => PredictedBox,
-      Rect: () => Rect,
-      SsdMobilenetv1: () => SsdMobilenetv1,
-      SsdMobilenetv1Options: () => SsdMobilenetv1Options,
-      TinyFaceDetector: () => TinyFaceDetector,
-      TinyFaceDetectorOptions: () => TinyFaceDetectorOptions,
-      TinyYolov2: () => TinyYolov2,
-      TinyYolov2Options: () => TinyYolov2Options,
-      TinyYolov2SizeType: () => TinyYolov2SizeType,
-      allFaces: () => allFaces,
-      allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1,
-      allFacesTinyYolov2: () => allFacesTinyYolov2,
-      awaitMediaLoaded: () => awaitMediaLoaded,
-      bufferToImage: () => bufferToImage,
-      computeFaceDescriptor: () => computeFaceDescriptor,
-      createCanvas: () => createCanvas,
-      createCanvasFromMedia: () => createCanvasFromMedia,
-      createFaceDetectionNet: () => createFaceDetectionNet,
-      createFaceRecognitionNet: () => createFaceRecognitionNet,
-      createSsdMobilenetv1: () => createSsdMobilenetv1,
-      createTinyFaceDetector: () => createTinyFaceDetector,
-      createTinyYolov2: () => createTinyYolov2,
-      detectAllFaces: () => detectAllFaces,
-      detectFaceLandmarks: () => detectFaceLandmarks,
-      detectFaceLandmarksTiny: () => detectFaceLandmarksTiny,
-      detectLandmarks: () => detectLandmarks,
-      detectSingleFace: () => detectSingleFace,
-      draw: () => draw_exports,
-      env: () => env,
-      euclideanDistance: () => euclideanDistance,
-      extendWithAge: () => extendWithAge,
-      extendWithFaceDescriptor: () => extendWithFaceDescriptor,
-      extendWithFaceDetection: () => extendWithFaceDetection,
-      extendWithFaceExpressions: () => extendWithFaceExpressions,
-      extendWithFaceLandmarks: () => extendWithFaceLandmarks,
-      extendWithGender: () => extendWithGender,
-      extractFaceTensors: () => extractFaceTensors,
-      extractFaces: () => extractFaces,
-      fetchImage: () => fetchImage,
-      fetchJson: () => fetchJson,
-      fetchNetWeights: () => fetchNetWeights,
-      fetchOrThrow: () => fetchOrThrow,
-      getContext2dOrThrow: () => getContext2dOrThrow,
-      getMediaDimensions: () => getMediaDimensions,
-      imageTensorToCanvas: () => imageTensorToCanvas,
-      imageToSquare: () => imageToSquare,
-      inverseSigmoid: () => inverseSigmoid,
-      iou: () => iou,
-      isMediaElement: () => isMediaElement,
-      isMediaLoaded: () => isMediaLoaded,
-      isWithAge: () => isWithAge,
-      isWithFaceDetection: () => isWithFaceDetection,
-      isWithFaceExpressions: () => isWithFaceExpressions,
-      isWithFaceLandmarks: () => isWithFaceLandmarks,
-      isWithGender: () => isWithGender,
-      loadAgeGenderModel: () => loadAgeGenderModel,
-      loadFaceDetectionModel: () => loadFaceDetectionModel,
-      loadFaceExpressionModel: () => loadFaceExpressionModel,
-      loadFaceLandmarkModel: () => loadFaceLandmarkModel,
-      loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel,
-      loadFaceRecognitionModel: () => loadFaceRecognitionModel,
-      loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model,
-      loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel,
-      loadTinyYolov2Model: () => loadTinyYolov2Model,
-      loadWeightMap: () => loadWeightMap,
-      locateFaces: () => locateFaces,
-      matchDimensions: () => matchDimensions,
-      minBbox: () => minBbox,
-      nets: () => nets,
-      nonMaxSuppression: () => nonMaxSuppression,
-      normalize: () => normalize,
-      padToSquare: () => padToSquare,
-      predictAgeAndGender: () => predictAgeAndGender,
-      recognizeFaceExpressions: () => recognizeFaceExpressions,
-      resizeResults: () => resizeResults,
-      resolveInput: () => resolveInput,
-      shuffleArray: () => shuffleArray,
-      sigmoid: () => sigmoid,
-      ssdMobilenetv1: () => ssdMobilenetv1,
-      tf: () => tf42,
-      tinyFaceDetector: () => tinyFaceDetector,
-      tinyYolov2: () => tinyYolov23,
-      toNetInput: () => toNetInput,
-      utils: () => utils_exports,
-      validateConfig: () => validateConfig,
-      version: () => version2
-    });
-    const tf42 = __toModule(require("@tensorflow/tfjs"));
-    const node = typeof process !== "undefined" ? process.version : false;
-    const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
-    const version2 = {faceapi: version, node, browser: browser3};
-  });
-
-  // build/src/draw/drawContour.js
-  function drawContour(ctx, points, isClosed = false) {
-    ctx.beginPath();
-    points.slice(1).forEach(({x, y}, prevIdx) => {
-      const from = points[prevIdx];
-      ctx.moveTo(from.x, from.y);
-      ctx.lineTo(x, y);
-    });
-    if (isClosed) {
-      const from = points[points.length - 1];
-      const to = points[0];
-      if (!from || !to) {
-        return;
-      }
-      ctx.moveTo(from.x, from.y);
-      ctx.lineTo(to.x, to.y);
-    }
-    ctx.stroke();
-  }
-
-  // build/src/classes/Dimensions.js
-  class Dimensions {
-    constructor(width, height) {
-      if (!isValidNumber(width) || !isValidNumber(height)) {
-        throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width, height})}`);
-      }
-      this._width = width;
-      this._height = height;
-    }
-    get width() {
-      return this._width;
-    }
-    get height() {
-      return this._height;
-    }
-    reverse() {
-      return new Dimensions(1 / this.width, 1 / this.height);
-    }
-  }
-
-  // build/src/utils/index.js
-  const utils_exports = {};
-  __export(utils_exports, {
-    computeReshapedDimensions: () => computeReshapedDimensions,
-    getCenterPoint: () => getCenterPoint,
-    isDimensions: () => isDimensions,
-    isEven: () => isEven,
-    isFloat: () => isFloat,
-    isTensor: () => isTensor,
-    isTensor1D: () => isTensor1D,
-    isTensor2D: () => isTensor2D,
-    isTensor3D: () => isTensor3D,
-    isTensor4D: () => isTensor4D,
-    isValidNumber: () => isValidNumber,
-    isValidProbablitiy: () => isValidProbablitiy,
-    range: () => range,
-    round: () => round
-  });
-  const tf = __toModule(require("@tensorflow/tfjs"));
-  function isTensor(tensor2, dim) {
-    return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim;
-  }
-  function isTensor1D(tensor2) {
-    return isTensor(tensor2, 1);
-  }
-  function isTensor2D(tensor2) {
-    return isTensor(tensor2, 2);
-  }
-  function isTensor3D(tensor2) {
-    return isTensor(tensor2, 3);
-  }
-  function isTensor4D(tensor2) {
-    return isTensor(tensor2, 4);
-  }
-  function isFloat(num) {
-    return num % 1 !== 0;
-  }
-  function isEven(num) {
-    return num % 2 === 0;
-  }
-  function round(num, prec = 2) {
-    const f = Math.pow(10, prec);
-    return Math.floor(num * f) / f;
-  }
-  function isDimensions(obj) {
-    return obj && obj.width && obj.height;
-  }
-  function computeReshapedDimensions({width, height}, inputSize) {
-    const scale2 = inputSize / Math.max(height, width);
-    return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
-  }
-  function getCenterPoint(pts) {
-    return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
-  }
-  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;
-  }
-  function isValidProbablitiy(num) {
-    return isValidNumber(num) && 0 <= num && num <= 1;
-  }
-
-  // build/src/classes/Point.js
-  class Point {
-    constructor(x, y) {
-      this._x = x;
-      this._y = y;
-    }
-    get x() {
-      return this._x;
-    }
-    get y() {
-      return this._y;
-    }
-    add(pt) {
-      return new Point(this.x + pt.x, this.y + pt.y);
-    }
-    sub(pt) {
-      return new Point(this.x - pt.x, this.y - pt.y);
-    }
-    mul(pt) {
-      return new Point(this.x * pt.x, this.y * pt.y);
-    }
-    div(pt) {
-      return new Point(this.x / pt.x, this.y / pt.y);
-    }
-    abs() {
-      return new Point(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 Point(Math.floor(this.x), Math.floor(this.y));
-    }
-  }
-
-  // build/src/classes/Box.js
-  class Box {
-    constructor(_box, allowNegativeDimensions = true) {
-      const box = _box || {};
-      const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
-      const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
-      if (!isRect && !isBbox) {
-        throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);
-      }
-      const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top];
-      Box.assertIsValidBox({x, y, width, height}, "Box.constructor", allowNegativeDimensions);
-      this._x = x;
-      this._y = y;
-      this._width = width;
-      this._height = height;
-    }
-    static isRect(rect) {
-      return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
-    }
-    static assertIsValidBox(box, callee, allowNegativeDimensions = false) {
-      if (!Box.isRect(box)) {
-        throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);
-      }
-      if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
-        throw new Error(`${callee} - width (${box.width}) and height (${box.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 Point(this.left, this.top);
-    }
-    get topRight() {
-      return new Point(this.right, this.top);
-    }
-    get bottomLeft() {
-      return new Point(this.left, this.bottom);
-    }
-    get bottomRight() {
-      return new Point(this.right, this.bottom);
-    }
-    round() {
-      const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val));
-      return new Box({x, y, width, height});
-    }
-    floor() {
-      const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val));
-      return new Box({x, y, width, height});
-    }
-    toSquare() {
-      let {x, y, width, height} = this;
-      const diff = Math.abs(width - height);
-      if (width < height) {
-        x -= diff / 2;
-        width += diff;
-      }
-      if (height < width) {
-        y -= diff / 2;
-        height += diff;
-      }
-      return new Box({x, y, width, height});
-    }
-    rescale(s) {
-      const scaleX = isDimensions(s) ? s.width : s;
-      const scaleY = isDimensions(s) ? s.height : s;
-      return new Box({
-        x: this.x * scaleX,
-        y: this.y * scaleY,
-        width: this.width * scaleX,
-        height: this.height * scaleY
-      });
-    }
-    pad(padX, padY) {
-      let [x, y, width, height] = [
-        this.x - padX / 2,
-        this.y - padY / 2,
-        this.width + padX,
-        this.height + padY
-      ];
-      return new Box({x, y, width, height});
-    }
-    clipAtImageBorders(imgWidth, imgHeight) {
-      const {x, y, right, bottom} = this;
-      const clippedX = Math.max(x, 0);
-      const clippedY = Math.max(y, 0);
-      const newWidth = right - clippedX;
-      const newHeight = bottom - clippedY;
-      const clippedWidth = Math.min(newWidth, imgWidth - clippedX);
-      const clippedHeight = Math.min(newHeight, imgHeight - clippedY);
-      return new Box({x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight}).floor();
-    }
-    shift(sx, sy) {
-      const {width, height} = this;
-      const x = this.x + sx;
-      const y = this.y + sy;
-      return new Box({x, y, width, height});
-    }
-    padAtBorders(imageHeight, imageWidth) {
-      const w = this.width + 1;
-      const h = this.height + 1;
-      let dx = 1;
-      let dy = 1;
-      let edx = w;
-      let edy = h;
-      let x = this.left;
-      let y = this.top;
-      let ex = this.right;
-      let ey = this.bottom;
-      if (ex > imageWidth) {
-        edx = -ex + imageWidth + w;
-        ex = imageWidth;
-      }
-      if (ey > imageHeight) {
-        edy = -ey + imageHeight + h;
-        ey = imageHeight;
-      }
-      if (x < 1) {
-        edy = 2 - x;
-        x = 1;
-      }
-      if (y < 1) {
-        edy = 2 - y;
-        y = 1;
-      }
-      return {dy, edy, dx, edx, y, ey, x, ex, w, h};
-    }
-    calibrate(region) {
-      return new Box({
-        left: this.left + region.left * this.width,
-        top: this.top + region.top * this.height,
-        right: this.right + region.right * this.width,
-        bottom: this.bottom + region.bottom * this.height
-      }).toSquare().round();
-    }
-  }
-
-  // build/src/classes/BoundingBox.js
-  class BoundingBox extends Box {
-    constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-      super({left, top, right, bottom}, allowNegativeDimensions);
-    }
-  }
-
-  // build/src/classes/ObjectDetection.js
-  class ObjectDetection {
-    constructor(score, classScore, className, relativeBox, imageDims) {
-      this._imageDims = new Dimensions(imageDims.width, imageDims.height);
-      this._score = score;
-      this._classScore = classScore;
-      this._className = className;
-      this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse());
-    }
-    forSize(width, height) {
-      return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, {width, height});
-    }
-  }
-
-  // build/src/classes/FaceDetection.js
-  class FaceDetection extends ObjectDetection {
-    constructor(score, relativeBox, imageDims) {
-      super(score, score, "", relativeBox, imageDims);
-    }
-    forSize(width, height) {
-      const {score, relativeBox, imageDims} = super.forSize(width, height);
-      return new FaceDetection(score, relativeBox, imageDims);
-    }
-  }
-
-  // build/src/ops/iou.js
-  function iou(box1, box2, isIOU = true) {
-    const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
-    const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
-    const interSection = width * height;
-    return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area);
-  }
-
-  // build/src/ops/minBbox.js
-  function minBbox(pts) {
-    const xs = pts.map((pt) => pt.x);
-    const ys = pts.map((pt) => pt.y);
-    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 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) {
-      const curr = indicesSortedByScore.pop();
-      pick.push(curr);
-      const indices = indicesSortedByScore;
-      const outputs = [];
-      for (let i = 0; i < indices.length; i++) {
-        const idx = indices[i];
-        const currBox = boxes[curr];
-        const idxBox = boxes[idx];
-        outputs.push(iou(currBox, idxBox, isIOU));
-      }
-      indicesSortedByScore = indicesSortedByScore.filter((_, j) => outputs[j] <= iouThreshold);
-    }
-    return pick;
-  }
-
-  // build/src/ops/normalize.js
-  const tf2 = __toModule(require("@tensorflow/tfjs"));
-  function normalize(x, meanRgb) {
-    return tf2.tidy(() => {
-      const [r, g, b] = meanRgb;
-      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 tf3.tidy(() => {
-      const [height, width] = imgTensor.shape.slice(1);
-      if (height === width) {
-        return imgTensor;
-      }
-      const dimDiff = Math.abs(height - width);
-      const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
-      const paddingAxis = height > width ? 2 : 1;
-      const createPaddingTensor = (paddingAmount2) => {
-        const paddingTensorShape = imgTensor.shape.slice();
-        paddingTensorShape[paddingAxis] = paddingAmount2;
-        return tf3.fill(paddingTensorShape, 0);
-      };
-      const paddingTensorAppend = createPaddingTensor(paddingAmount);
-      const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
-      const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;
-      const tensorsToStack = [
-        paddingTensorPrepend,
-        imgTensor,
-        paddingTensorAppend
-      ].filter((t) => !!t).map((t) => tf3.cast(t, "float32"));
-      return tf3.concat(tensorsToStack, paddingAxis);
-    });
-  }
-
-  // build/src/ops/shuffleArray.js
-  function shuffleArray(inputArray) {
-    const array = inputArray.slice();
-    for (let i = array.length - 1; i > 0; i--) {
-      const j = Math.floor(Math.random() * (i + 1));
-      const x = array[i];
-      array[i] = array[j];
-      array[j] = x;
-    }
-    return array;
-  }
-
-  // build/src/ops/index.js
-  function sigmoid(x) {
-    return 1 / (1 + Math.exp(-x));
-  }
-  function inverseSigmoid(x) {
-    return Math.log(x / (1 - x));
-  }
-
-  // build/src/classes/Rect.js
-  class Rect extends Box {
-    constructor(x, y, width, height, allowNegativeDimensions = false) {
-      super({x, y, width, height}, allowNegativeDimensions);
-    }
-  }
-
-  // build/src/classes/FaceLandmarks.js
-  const relX = 0.5;
-  const relY = 0.43;
-  const relScale = 0.45;
-  class FaceLandmarks {
-    constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) {
-      const {width, height} = imgDims;
-      this._imgDims = new Dimensions(width, height);
-      this._shift = shift;
-      this._positions = relativeFaceLandmarkPositions.map((pt) => pt.mul(new Point(width, height)).add(shift));
-    }
-    get shift() {
-      return new Point(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((pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)));
-    }
-    forSize(width, height) {
-      return new this.constructor(this.relativePositions, {width, height});
-    }
-    shiftBy(x, y) {
-      return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
-    }
-    shiftByPoint(pt) {
-      return this.shiftBy(pt.x, pt.y);
-    }
-    align(detection, options = {}) {
-      if (detection) {
-        const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection);
-        return this.shiftBy(box.x, box.y).align(null, options);
-      }
-      const {useDlibAlignment, minBoxPadding} = Object.assign({}, {useDlibAlignment: false, minBoxPadding: 0.2}, options);
-      if (useDlibAlignment) {
-        return this.alignDlib();
-      }
-      return this.alignMinBbox(minBoxPadding);
-    }
-    alignDlib() {
-      const centers = this.getRefPointsForAlignment();
-      const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;
-      const distToMouth = (pt) => mouthCenter.sub(pt).magnitude();
-      const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
-      const size = Math.floor(eyeToMouthDist / relScale);
-      const refPoint = getCenterPoint(centers);
-      const x = Math.floor(Math.max(0, refPoint.x - relX * size));
-      const y = Math.floor(Math.max(0, refPoint.y - relY * size));
-      return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
-    }
-    alignMinBbox(padding) {
-      const box = minBbox(this.positions);
-      return box.pad(box.width * padding, box.height * padding);
-    }
-    getRefPointsForAlignment() {
-      throw new Error("getRefPointsForAlignment not implemented by base class");
-    }
-  }
-
-  // build/src/classes/FaceLandmarks5.js
-  class FaceLandmarks5 extends FaceLandmarks {
-    getRefPointsForAlignment() {
-      const pts = this.positions;
-      return [
-        pts[0],
-        pts[1],
-        getCenterPoint([pts[3], pts[4]])
-      ];
-    }
-  }
-
-  // build/src/classes/FaceLandmarks68.js
-  class FaceLandmarks68 extends FaceLandmarks {
-    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(getCenterPoint);
-    }
-  }
-
-  // build/src/classes/FaceMatch.js
-  class FaceMatch {
-    constructor(label, distance) {
-      this._label = label;
-      this._distance = distance;
-    }
-    get label() {
-      return this._label;
-    }
-    get distance() {
-      return this._distance;
-    }
-    toString(withDistance = true) {
-      return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
-    }
-  }
-
-  // build/src/classes/LabeledBox.js
-  class LabeledBox extends Box {
-    constructor(box, label) {
-      super(box);
-      this._label = label;
-    }
-    static assertIsValidLabeledBox(box, callee) {
-      Box.assertIsValidBox(box, callee);
-      if (!isValidNumber(box.label)) {
-        throw new Error(`${callee} - expected property label (${box.label}) to be a number`);
-      }
-    }
-    get label() {
-      return this._label;
-    }
-  }
-
-  // build/src/classes/LabeledFaceDescriptors.js
-  class LabeledFaceDescriptors {
-    constructor(label, descriptors) {
-      if (!(typeof label === "string")) {
-        throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");
-      }
-      if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {
-        throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");
-      }
-      this._label = label;
-      this._descriptors = descriptors;
-    }
-    get label() {
-      return this._label;
-    }
-    get descriptors() {
-      return this._descriptors;
-    }
-    toJSON() {
-      return {
-        label: this.label,
-        descriptors: this.descriptors.map((d) => Array.from(d))
-      };
-    }
-    static fromJSON(json) {
-      const descriptors = json.descriptors.map((d) => {
-        return new Float32Array(d);
-      });
-      return new LabeledFaceDescriptors(json.label, descriptors);
-    }
-  }
-
-  // build/src/classes/PredictedBox.js
-  class PredictedBox extends LabeledBox {
-    constructor(box, label, score, classScore) {
-      super(box, label);
-      this._score = score;
-      this._classScore = classScore;
-    }
-    static assertIsValidPredictedBox(box, callee) {
-      LabeledBox.assertIsValidLabeledBox(box, callee);
-      if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) {
-        throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);
-      }
-    }
-    get score() {
-      return this._score;
-    }
-    get classScore() {
-      return this._classScore;
-    }
-  }
-
-  // build/src/classes/index.js
-
-  // build/src/factories/WithFaceDetection.js
-  function isWithFaceDetection(obj) {
-    return obj["detection"] instanceof FaceDetection;
-  }
-  function extendWithFaceDetection(sourceObj, detection) {
-    const extension = {detection};
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // build/src/env/createBrowserEnv.js
-  function createBrowserEnv() {
-    const fetch = window["fetch"] || function() {
-      throw new Error("fetch - missing fetch implementation for browser environment");
-    };
-    const readFile = 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,
-      readFile
-    };
-  }
-
-  // build/src/env/createFileSystem.js
-  function createFileSystem(fs) {
-    let requireFsError = "";
-    if (!fs) {
-      try {
-        fs = require("fs");
-      } catch (err) {
-        requireFsError = err.toString();
-      }
-    }
-    const readFile = fs ? function(filePath) {
-      return new Promise((res, rej) => {
-        fs.readFile(filePath, function(err, buffer) {
-          return err ? rej(err) : res(buffer);
-        });
-      });
-    } : function() {
-      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);
-    };
-    return {
-      readFile
-    };
-  }
-
-  // build/src/env/createNodejsEnv.js
-  function createNodejsEnv() {
-    const Canvas = global["Canvas"] || global["HTMLCanvasElement"];
-    const Image = global["Image"] || global["HTMLImageElement"];
-    const createCanvasElement = function() {
-      if (Canvas) {
-        return new Canvas();
-      }
-      throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment");
-    };
-    const createImageElement = function() {
-      if (Image) {
-        return new Image();
-      }
-      throw new Error("createImageElement - missing Image implementation for nodejs environment");
-    };
-    const fetch = global["fetch"] || function() {
-      throw new Error("fetch - missing fetch implementation for nodejs environment");
-    };
-    const fileSystem = createFileSystem();
-    return {
-      Canvas: Canvas || class {
-      },
-      CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
-      },
-      Image: Image || class {
-      },
-      ImageData: global["ImageData"] || class {
-      },
-      Video: global["HTMLVideoElement"] || class {
-      },
-      createCanvasElement,
-      createImageElement,
-      fetch,
-      ...fileSystem
-    };
-  }
-
-  // build/src/env/isBrowser.js
-  function isBrowser() {
-    return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
-  }
-
-  // build/src/env/types.js
-
-  // build/src/env/index.js
-  const isNodejs = __toModule(require_isNodejs());
-  let environment;
-  function getEnv() {
-    if (!environment) {
-      throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
-    }
-    return environment;
-  }
-  function setEnv(env16) {
-    environment = env16;
-  }
-  function initialize() {
-    if (isBrowser()) {
-      return setEnv(createBrowserEnv());
-    }
-    if (isNodejs.isNodejs()) {
-      return setEnv(createNodejsEnv());
-    }
-  }
-  function monkeyPatch(env16) {
-    if (!environment) {
-      initialize();
-    }
-    if (!environment) {
-      throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
-    }
-    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 env = {
-    getEnv,
-    setEnv,
-    initialize,
-    createBrowserEnv,
-    createFileSystem,
-    createNodejsEnv,
-    monkeyPatch,
-    isBrowser,
-    isNodejs: isNodejs.isNodejs
-  };
-  initialize();
-
-  // build/src/dom/resolveInput.js
-  function resolveInput(arg) {
-    if (!env.isNodejs() && typeof arg === "string") {
-      return document.getElementById(arg);
-    }
-    return arg;
-  }
-
-  // build/src/dom/getContext2dOrThrow.js
-  function getContext2dOrThrow(canvasArg) {
-    const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
-    if (canvasArg instanceof CanvasRenderingContext2D2) {
-      return canvasArg;
-    }
-    const canvas = resolveInput(canvasArg);
-    if (!(canvas instanceof Canvas)) {
-      throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");
-    }
-    const ctx = canvas.getContext("2d");
-    if (!ctx) {
-      throw new Error("resolveContext2d - canvas 2d context is null");
-    }
-    return ctx;
-  }
-
-  // build/src/draw/DrawTextField.js
-  var AnchorPosition;
-  (function(AnchorPosition2) {
-    AnchorPosition2["TOP_LEFT"] = "TOP_LEFT";
-    AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT";
-    AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT";
-    AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
-  })(AnchorPosition || (AnchorPosition = {}));
-  class DrawTextFieldOptions {
-    constructor(options = {}) {
-      const {anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding} = options;
-      this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;
-      this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)";
-      this.fontColor = fontColor || "rgba(255, 255, 255, 1)";
-      this.fontSize = fontSize || 14;
-      this.fontStyle = fontStyle || "Georgia";
-      this.padding = padding || 4;
-    }
-  }
-  class DrawTextField {
-    constructor(text, anchor, options = {}) {
-      this.text = typeof text === "string" ? [text] : text instanceof DrawTextField ? text.text : text;
-      this.anchor = anchor;
-      this.options = new DrawTextFieldOptions(options);
-    }
-    measureWidth(ctx) {
-      const {padding} = this.options;
-      return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding;
-    }
-    measureHeight() {
-      const {fontSize, padding} = this.options;
-      return this.text.length * fontSize + 2 * padding;
-    }
-    getUpperLeft(ctx, canvasDims) {
-      const {anchorPosition} = this.options;
-      const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;
-      const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;
-      const textFieldWidth = this.measureWidth(ctx);
-      const textFieldHeight = this.measureHeight();
-      const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x;
-      const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;
-      if (canvasDims) {
-        const {width, height} = canvasDims;
-        const newX = Math.max(Math.min(x, width - textFieldWidth), 0);
-        const newY = Math.max(Math.min(y, height - textFieldHeight), 0);
-        return {x: newX, y: newY};
-      }
-      return {x, y};
-    }
-    draw(canvasArg) {
-      const canvas = resolveInput(canvasArg);
-      const ctx = getContext2dOrThrow(canvas);
-      const {backgroundColor, fontColor, fontSize, fontStyle, padding} = this.options;
-      ctx.font = `${fontSize}px ${fontStyle}`;
-      const maxTextWidth = this.measureWidth(ctx);
-      const textHeight = this.measureHeight();
-      ctx.fillStyle = backgroundColor;
-      const upperLeft = this.getUpperLeft(ctx, canvas);
-      ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);
-      ctx.fillStyle = fontColor;
-      this.text.forEach((textLine, i) => {
-        const x = padding + upperLeft.x;
-        const y = padding + upperLeft.y + (i + 1) * fontSize;
-        ctx.fillText(textLine, x, y);
-      });
-    }
-  }
-
-  // build/src/draw/DrawBox.js
-  class DrawBoxOptions {
-    constructor(options = {}) {
-      const {boxColor, lineWidth, label, drawLabelOptions} = options;
-      this.boxColor = boxColor || "rgba(0, 0, 255, 1)";
-      this.lineWidth = lineWidth || 2;
-      this.label = label;
-      const defaultDrawLabelOptions = {
-        anchorPosition: AnchorPosition.BOTTOM_LEFT,
-        backgroundColor: this.boxColor
-      };
-      this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions));
-    }
-  }
-  class DrawBox {
-    constructor(box, options = {}) {
-      this.box = new Box(box);
-      this.options = new DrawBoxOptions(options);
-    }
-    draw(canvasArg) {
-      const ctx = getContext2dOrThrow(canvasArg);
-      const {boxColor, lineWidth} = this.options;
-      const {x, y, width, height} = this.box;
-      ctx.strokeStyle = boxColor;
-      ctx.lineWidth = lineWidth;
-      ctx.strokeRect(x, y, width, height);
-      const {label} = this.options;
-      if (label) {
-        new DrawTextField([label], {x: x - lineWidth / 2, y}, this.options.drawLabelOptions).draw(canvasArg);
-      }
-    }
-  }
-
-  // build/src/draw/drawDetections.js
-  function drawDetections(canvasArg, detections) {
-    const detectionsArray = Array.isArray(detections) ? detections : [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 ? `${round(score)}` : void 0;
-      new DrawBox(box, {label}).draw(canvasArg);
-    });
-  }
-
-  // build/src/dom/isMediaLoaded.js
-  function isMediaLoaded(media) {
-    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 env.getEnv().Canvas || isMediaLoaded(media)) {
-        return resolve(null);
-      }
-      function onLoad(e) {
-        if (!e.currentTarget)
-          return;
-        e.currentTarget.removeEventListener("load", onLoad);
-        e.currentTarget.removeEventListener("error", onError);
-        resolve(e);
-      }
-      function onError(e) {
-        if (!e.currentTarget)
-          return;
-        e.currentTarget.removeEventListener("load", onLoad);
-        e.currentTarget.removeEventListener("error", onError);
-        reject(e);
-      }
-      media.addEventListener("load", onLoad);
-      media.addEventListener("error", onError);
-    });
-  }
-
-  // build/src/dom/bufferToImage.js
-  function bufferToImage(buf) {
-    return new Promise((resolve, reject) => {
-      if (!(buf instanceof Blob)) {
-        return reject("bufferToImage - expected buf to be of type: Blob");
-      }
-      const reader = new FileReader();
-      reader.onload = () => {
-        if (typeof reader.result !== "string") {
-          return reject("bufferToImage - expected reader.result to be a string, in onload");
-        }
-        const img = env.getEnv().createImageElement();
-        img.onload = () => resolve(img);
-        img.onerror = reject;
-        img.src = reader.result;
-      };
-      reader.onerror = reject;
-      reader.readAsDataURL(buf);
-    });
-  }
-
-  // build/src/dom/getMediaDimensions.js
-  function getMediaDimensions(input) {
-    const {Image, Video} = env.getEnv();
-    if (input instanceof Image) {
-      return new Dimensions(input.naturalWidth, input.naturalHeight);
-    }
-    if (input instanceof Video) {
-      return new Dimensions(input.videoWidth, input.videoHeight);
-    }
-    return new Dimensions(input.width, input.height);
-  }
-
-  // build/src/dom/createCanvas.js
-  function createCanvas({width, height}) {
-    const {createCanvasElement} = env.getEnv();
-    const canvas = createCanvasElement();
-    canvas.width = width;
-    canvas.height = height;
-    return canvas;
-  }
-  function createCanvasFromMedia(media, dims) {
-    const {ImageData: ImageData2} = env.getEnv();
-    if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
-      throw new Error("createCanvasFromMedia - media has not finished loading yet");
-    }
-    const {width, height} = dims || getMediaDimensions(media);
-    const canvas = createCanvas({width, height});
-    if (media instanceof ImageData2) {
-      getContext2dOrThrow(canvas).putImageData(media, 0, 0);
-    } else {
-      getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-    }
-    return canvas;
-  }
-
-  // build/src/dom/imageTensorToCanvas.js
-  const tf4 = __toModule(require("@tensorflow/tfjs"));
-  async function imageTensorToCanvas(imgTensor, canvas) {
-    const targetCanvas = canvas || env.getEnv().createCanvasElement();
-    const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-    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} = 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} = env.getEnv();
-    if (!(input instanceof Image || input instanceof Canvas)) {
-      throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
-    }
-    const dims = getMediaDimensions(input);
-    const scale2 = inputSize / Math.max(dims.height, dims.width);
-    const width = scale2 * dims.width;
-    const height = scale2 * dims.height;
-    const targetCanvas = createCanvas({width: inputSize, height: inputSize});
-    const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
-    const offset = Math.abs(width - height) / 2;
-    const dx = centerImage && width < height ? offset : 0;
-    const dy = centerImage && height < width ? offset : 0;
-    getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
-    return targetCanvas;
-  }
-
-  // build/src/dom/NetInput.js
-  const tf5 = __toModule(require("@tensorflow/tfjs"));
-  class NetInput {
-    constructor(inputs, treatAsBatchInput = false) {
-      this._imageTensors = [];
-      this._canvases = [];
-      this._treatAsBatchInput = false;
-      this._inputDimensions = [];
-      if (!Array.isArray(inputs)) {
-        throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
-      }
-      this._treatAsBatchInput = treatAsBatchInput;
-      this._batchSize = inputs.length;
-      inputs.forEach((input, idx) => {
-        if (isTensor3D(input)) {
-          this._imageTensors[idx] = input;
-          this._inputDimensions[idx] = input.shape;
-          return;
-        }
-        if (isTensor4D(input)) {
-          const batchSize = input.shape[0];
-          if (batchSize !== 1) {
-            throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-          }
-          this._imageTensors[idx] = input;
-          this._inputDimensions[idx] = input.shape.slice(1);
-          return;
-        }
-        const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
-        this._canvases[idx] = canvas;
-        this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
-    }
-    getInput(batchIdx) {
-      return this.canvases[batchIdx] || this.imageTensors[batchIdx];
-    }
-    getInputDimensions(batchIdx) {
-      return this._inputDimensions[batchIdx];
-    }
-    getInputHeight(batchIdx) {
-      return this._inputDimensions[batchIdx][0];
-    }
-    getInputWidth(batchIdx) {
-      return this._inputDimensions[batchIdx][1];
-    }
-    getReshapedInputDimensions(batchIdx) {
-      if (typeof this.inputSize !== "number") {
-        throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");
-      }
-      const width = this.getInputWidth(batchIdx);
-      const height = this.getInputHeight(batchIdx);
-      return computeReshapedDimensions({width, height}, this.inputSize);
-    }
-    toBatchTensor(inputSize, isCenterInputs = true) {
-      this._inputSize = inputSize;
-      return tf5.tidy(() => {
-        const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
-          const input = this.getInput(batchIdx);
-          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 = tf5.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
-            }
-            return imgTensor.as3D(inputSize, inputSize, 3);
-          }
-          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 = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
-        return batchTensor;
-      });
-    }
-  }
-
-  // build/src/dom/toNetInput.js
-  async function toNetInput(inputs) {
-    if (inputs instanceof NetInput) {
-      return inputs;
-    }
-    let inputArgArray = Array.isArray(inputs) ? inputs : [inputs];
-    if (!inputArgArray.length) {
-      throw new Error("toNetInput - empty array passed as input");
-    }
-    const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : "";
-    const inputArray = inputArgArray.map(resolveInput);
-    inputArray.forEach((input, i) => {
-      if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
-        if (typeof inputArgArray[i] === "string") {
-          throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);
-        }
-        throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);
-      }
-      if (isTensor4D(input)) {
-        const batchSize = input.shape[0];
-        if (batchSize !== 1) {
-          throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-        }
-      }
-    });
-    await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));
-    return new NetInput(inputArray, Array.isArray(inputs));
-  }
-
-  // build/src/dom/extractFaces.js
-  async function extractFaces(input, detections) {
-    const {Canvas} = env.getEnv();
-    let canvas = input;
-    if (!(input instanceof Canvas)) {
-      const netInput = await toNetInput(input);
-      if (netInput.batchSize > 1) {
-        throw new Error("extractFaces - batchSize > 1 not supported");
-      }
-      const tensorOrCanvas = netInput.getInput(0);
-      canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);
-    }
-    const ctx = getContext2dOrThrow(canvas);
-    const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
-    return boxes.map(({x, y, width, height}) => {
-      const faceImg = createCanvas({width, height});
-      getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
-      return faceImg;
-    });
-  }
-
-  // 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");
-    }
-    if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
-      throw new Error("extractFaceTensors - batchSize > 1 not supported");
-    }
-    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}) => 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 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}`);
-    }
-    return res;
-  }
-
-  // build/src/dom/fetchImage.js
-  async function fetchImage(uri) {
-    const res = await fetchOrThrow(uri);
-    const blob = await res.blob();
-    if (!blob.type.startsWith("image/")) {
-      throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);
-    }
-    return bufferToImage(blob);
-  }
-
-  // build/src/dom/fetchJson.js
-  async function fetchJson(uri) {
-    return (await fetchOrThrow(uri)).json();
-  }
-
-  // build/src/dom/fetchNetWeights.js
-  async function fetchNetWeights(uri) {
-    return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-  }
-
-  // build/src/common/getModelUris.js
-  function getModelUris(uri, defaultModelName) {
-    const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;
-    if (!uri) {
-      return {
-        modelBaseUri: "",
-        manifestUri: defaultManifestFilename
-      };
-    }
-    if (uri === "/") {
-      return {
-        modelBaseUri: "/",
-        manifestUri: `/${defaultManifestFilename}`
-      };
-    }
-    const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : "";
-    uri = uri.replace(protocol, "");
-    const parts = uri.split("/").filter((s) => s);
-    const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename;
-    let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/");
-    modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri;
-    return {
-      modelBaseUri,
-      manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`
-    };
-  }
-
-  // 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 tf7.io.loadWeights(manifest, modelBaseUri);
-  }
-
-  // build/src/dom/matchDimensions.js
-  function matchDimensions(input, reference, useMediaDimensions = false) {
-    const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
-    input.width = width;
-    input.height = height;
-    return {width, height};
-  }
-
-  // build/src/dom/types.js
-
-  // build/src/dom/index.js
-
-  // build/src/NeuralNetwork.js
-  const tf8 = __toModule(require("@tensorflow/tfjs"));
-  class NeuralNetwork {
-    constructor(_name) {
-      this._name = _name;
-      this._params = void 0;
-      this._paramMappings = [];
-    }
-    get params() {
-      return this._params;
-    }
-    get paramMappings() {
-      return this._paramMappings;
-    }
-    get isLoaded() {
-      return !!this.params;
-    }
-    getParamFromPath(paramPath) {
-      const {obj, objProp} = this.traversePropertyPath(paramPath);
-      return obj[objProp];
-    }
-    reassignParamFromPath(paramPath, tensor2) {
-      const {obj, objProp} = this.traversePropertyPath(paramPath);
-      obj[objProp].dispose();
-      obj[objProp] = tensor2;
-    }
-    getParamList() {
-      return this._paramMappings.map(({paramPath}) => ({
-        path: paramPath,
-        tensor: this.getParamFromPath(paramPath)
-      }));
-    }
-    getTrainableParams() {
-      return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable);
-    }
-    getFrozenParams() {
-      return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable));
-    }
-    variable() {
-      this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
-        this.reassignParamFromPath(path, tensor2.variable());
-      });
-    }
-    freeze() {
-      this.getTrainableParams().forEach(({path, tensor: variable}) => {
-        const tensor2 = tf8.tensor(variable.dataSync());
-        variable.dispose();
-        this.reassignParamFromPath(path, tensor2);
-      });
-    }
-    dispose(throwOnRedispose = true) {
-      this.getParamList().forEach((param) => {
-        if (throwOnRedispose && param.tensor.isDisposed) {
-          throw new Error(`param tensor has already been disposed for path ${param.path}`);
-        }
-        param.tensor.dispose();
-      });
-      this._params = void 0;
-    }
-    serializeParams() {
-      return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
-    }
-    async load(weightsOrUrl) {
-      if (weightsOrUrl instanceof Float32Array) {
-        this.extractWeights(weightsOrUrl);
-        return;
-      }
-      await this.loadFromUri(weightsOrUrl);
-    }
-    async loadFromUri(uri) {
-      if (uri && typeof uri !== "string") {
-        throw new Error(`${this._name}.loadFromUri - expected model uri`);
-      }
-      const weightMap = await loadWeightMap(uri, this.getDefaultModelName());
-      this.loadFromWeightMap(weightMap);
-    }
-    async loadFromDisk(filePath) {
-      if (filePath && typeof filePath !== "string") {
-        throw new Error(`${this._name}.loadFromDisk - expected model file path`);
-      }
-      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 loadWeights = tf8.io.weightsLoaderFactory(fetchWeightsFromDisk);
-      const manifest = JSON.parse((await readFile(manifestUri)).toString());
-      const weightMap = await loadWeights(manifest, modelBaseUri);
-      this.loadFromWeightMap(weightMap);
-    }
-    loadFromWeightMap(weightMap) {
-      const {paramMappings, params} = this.extractParamsFromWeigthMap(weightMap);
-      this._paramMappings = paramMappings;
-      this._params = params;
-    }
-    extractWeights(weights) {
-      const {paramMappings, params} = this.extractParams(weights);
-      this._paramMappings = paramMappings;
-      this._params = params;
-    }
-    traversePropertyPath(paramPath) {
-      if (!this.params) {
-        throw new Error(`traversePropertyPath - model has no loaded params`);
-      }
-      const result = paramPath.split("/").reduce((res, objProp2) => {
-        if (!res.nextObj.hasOwnProperty(objProp2)) {
-          throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);
-        }
-        return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
-      }, {nextObj: this.params});
-      const {obj, objProp} = result;
-      if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) {
-        throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
-      }
-      return {obj, objProp};
-    }
-  }
-
-  // build/src/common/depthwiseSeparableConv.js
-  const tf9 = __toModule(require("@tensorflow/tfjs"));
-  function depthwiseSeparableConv(x, params, stride) {
-    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 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 = tf10.relu(tf10.add(out1, out2));
-      const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-      return tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
-    });
-  }
-  function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-    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 = tf10.relu(tf10.add(out1, out2));
-      const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-      const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3)));
-      const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-      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 tf11.tidy(() => {
-      const out = tf11.add(tf11.conv2d(x, params.filters, [1, 1], padding), params.bias);
-      return withRelu ? tf11.relu(out) : out;
-    });
-  }
-
-  // build/src/common/disposeUnusedWeightTensors.js
-  function disposeUnusedWeightTensors(weightMap, paramMappings) {
-    Object.keys(weightMap).forEach((path) => {
-      if (!paramMappings.some((pm) => pm.originalPath === path)) {
-        weightMap[path].dispose();
-      }
-    });
-  }
-
-  // build/src/common/extractConvParamsFactory.js
-  const tf12 = __toModule(require("@tensorflow/tfjs"));
-  function extractConvParamsFactory(extractWeights, paramMappings) {
-    return function(channelsIn, channelsOut, filterSize, mappedPrefix) {
-      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: `${mappedPrefix}/bias`});
-      return {filters, bias};
-    };
-  }
-
-  // build/src/common/extractFCParamsFactory.js
-  const tf13 = __toModule(require("@tensorflow/tfjs"));
-  function extractFCParamsFactory(extractWeights, paramMappings) {
-    return function(channelsIn, channelsOut, mappedPrefix) {
-      const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-      const fc_bias = tf13.tensor1d(extractWeights(channelsOut));
-      paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
-      return {
-        weights: fc_weights,
-        bias: fc_bias
-      };
-    };
-  }
-
-  // build/src/common/types.js
-  class SeparableConvParams {
-    constructor(depthwise_filter, pointwise_filter, bias) {
-      this.depthwise_filter = depthwise_filter;
-      this.pointwise_filter = pointwise_filter;
-      this.bias = bias;
-    }
-  }
-
-  // build/src/common/extractSeparableConvParamsFactory.js
-  const tf14 = __toModule(require("@tensorflow/tfjs"));
-  function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-    return function(channelsIn, channelsOut, mappedPrefix) {
-      const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-      const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-      const bias = tf14.tensor1d(extractWeights(channelsOut));
-      paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
-      return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-    };
-  }
-  function loadSeparableConvParamsFactory(extractWeightEntry) {
-    return function(prefix) {
-      const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);
-      const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);
-      const bias = extractWeightEntry(`${prefix}/bias`, 1);
-      return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-    };
-  }
-
-  // build/src/common/extractWeightEntryFactory.js
-  function extractWeightEntryFactory(weightMap, paramMappings) {
-    return function(originalPath, paramRank, mappedPath) {
-      const tensor2 = weightMap[originalPath];
-      if (!isTensor(tensor2, paramRank)) {
-        throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
-      }
-      paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-      return tensor2;
-    };
-  }
-
-  // build/src/common/extractWeightsFactory.js
-  function extractWeightsFactory(weights) {
-    let remainingWeights = weights;
-    function extractWeights(numWeights) {
-      const ret = remainingWeights.slice(0, numWeights);
-      remainingWeights = remainingWeights.slice(numWeights);
-      return ret;
-    }
-    function getRemainingWeights() {
-      return remainingWeights;
-    }
-    return {
-      extractWeights,
-      getRemainingWeights
-    };
-  }
-
-  // build/src/common/index.js
-
-  // build/src/faceFeatureExtractor/extractorsFactory.js
-  function extractorsFactory(extractWeights, paramMappings) {
-    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-    function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-      const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);
-      const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);
-      const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);
-      return {conv0, conv1, conv2: conv22};
-    }
-    function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-      const {conv0, conv1, conv2: conv22} = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);
-      const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);
-      return {conv0, conv1, conv2: conv22, conv3};
-    }
-    return {
-      extractDenseBlock3Params,
-      extractDenseBlock4Params
-    };
-  }
-
-  // build/src/faceFeatureExtractor/extractParams.js
-  function extractParams(weights) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const {extractDenseBlock4Params} = extractorsFactory(extractWeights, paramMappings);
-    const dense0 = extractDenseBlock4Params(3, 32, "dense0", true);
-    const dense1 = extractDenseBlock4Params(32, 64, "dense1");
-    const dense2 = extractDenseBlock4Params(64, 128, "dense2");
-    const dense3 = extractDenseBlock4Params(128, 256, "dense3");
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      paramMappings,
-      params: {dense0, dense1, dense2, dense3}
-    };
-  }
-
-  // build/src/common/loadConvParamsFactory.js
-  function loadConvParamsFactory(extractWeightEntry) {
-    return function(prefix) {
-      const filters = extractWeightEntry(`${prefix}/filters`, 4);
-      const bias = extractWeightEntry(`${prefix}/bias`, 1);
-      return {filters, bias};
-    };
-  }
-
-  // build/src/faceFeatureExtractor/loadParamsFactory.js
-  function loadParamsFactory(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-    function extractDenseBlock3Params(prefix, isFirstLayer = false) {
-      const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-      const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-      const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-      return {conv0, conv1, conv2: conv22};
-    }
-    function extractDenseBlock4Params(prefix, isFirstLayer = false) {
-      const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-      const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-      const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-      const conv3 = extractSeparableConvParams(`${prefix}/conv3`);
-      return {conv0, conv1, conv2: conv22, conv3};
-    }
-    return {
-      extractDenseBlock3Params,
-      extractDenseBlock4Params
-    };
-  }
-
-  // build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js
-  function extractParamsFromWeigthMap(weightMap) {
-    const paramMappings = [];
-    const {extractDenseBlock4Params} = loadParamsFactory(weightMap, paramMappings);
-    const params = {
-      dense0: extractDenseBlock4Params("dense0", true),
-      dense1: extractDenseBlock4Params("dense1"),
-      dense2: extractDenseBlock4Params("dense2"),
-      dense3: extractDenseBlock4Params("dense3")
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/faceFeatureExtractor/FaceFeatureExtractor.js
-  const tf15 = __toModule(require("@tensorflow/tfjs"));
-  class FaceFeatureExtractor extends NeuralNetwork {
-    constructor() {
-      super("FaceFeatureExtractor");
-    }
-    forwardInput(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("FaceFeatureExtractor - load model before inference");
-      }
-      return tf15.tidy(() => {
-        const batchTensor = input.toBatchTensor(112, true);
-        const meanRgb = [122.782, 117.001, 104.298];
-        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 = tf15.avgPool(out, [7, 7], [2, 2], "valid");
-        return out;
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    getDefaultModelName() {
-      return "face_feature_extractor_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMap(weightMap);
-    }
-    extractParams(weights) {
-      return extractParams(weights);
-    }
-  }
-
-  // build/src/common/fullyConnectedLayer.js
-  const tf16 = __toModule(require("@tensorflow/tfjs"));
-  function fullyConnectedLayer(x, params) {
-    return tf16.tidy(() => tf16.add(tf16.matMul(x, params.weights), params.bias));
-  }
-
-  // build/src/faceProcessor/extractParams.js
-  function extractParams3(weights, channelsIn, channelsOut) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-    const fc = extractFCParams(channelsIn, channelsOut, "fc");
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      paramMappings,
-      params: {fc}
-    };
-  }
-
-  // build/src/faceProcessor/extractParamsFromWeigthMap.js
-  function extractParamsFromWeigthMap3(weightMap) {
-    const paramMappings = [];
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractFcParams(prefix) {
-      const weights = extractWeightEntry(`${prefix}/weights`, 2);
-      const bias = extractWeightEntry(`${prefix}/bias`, 1);
-      return {weights, bias};
-    }
-    const params = {
-      fc: extractFcParams("fc")
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/faceProcessor/util.js
-  function seperateWeightMaps(weightMap) {
-    const featureExtractorMap = {};
-    const classifierMap = {};
-    Object.keys(weightMap).forEach((key) => {
-      const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
-      map[key] = weightMap[key];
-    });
-    return {featureExtractorMap, classifierMap};
-  }
-
-  // build/src/faceProcessor/FaceProcessor.js
-  const tf17 = __toModule(require("@tensorflow/tfjs"));
-  class FaceProcessor extends NeuralNetwork {
-    constructor(_name, faceFeatureExtractor) {
-      super(_name);
-      this._faceFeatureExtractor = faceFeatureExtractor;
-    }
-    get faceFeatureExtractor() {
-      return this._faceFeatureExtractor;
-    }
-    runNet(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error(`${this._name} - load model before inference`);
-      }
-      return tf17.tidy(() => {
-        const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-        return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
-      });
-    }
-    dispose(throwOnRedispose = true) {
-      this.faceFeatureExtractor.dispose(throwOnRedispose);
-      super.dispose(throwOnRedispose);
-    }
-    loadClassifierParams(weights) {
-      const {params, paramMappings} = this.extractClassifierParams(weights);
-      this._params = params;
-      this._paramMappings = paramMappings;
-    }
-    extractClassifierParams(weights) {
-      return extractParams3(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-      this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-      return extractParamsFromWeigthMap3(classifierMap);
-    }
-    extractParams(weights) {
-      const cIn = this.getClassifierChannelsIn();
-      const cOut = this.getClassifierChannelsOut();
-      const classifierWeightSize = cOut * cIn + cOut;
-      const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-      const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-      this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-      return this.extractClassifierParams(classifierWeights);
-    }
-  }
-
-  // build/src/faceExpressionNet/FaceExpressions.js
-  const FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
-  class FaceExpressions {
-    constructor(probabilities) {
-      if (probabilities.length !== 7) {
-        throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);
-      }
-      FACE_EXPRESSION_LABELS.forEach((expression, idx) => {
-        this[expression] = probabilities[idx];
-      });
-    }
-    asSortedArray() {
-      return FACE_EXPRESSION_LABELS.map((expression) => ({expression, probability: this[expression]})).sort((e0, e1) => e1.probability - e0.probability);
-    }
-  }
-
-  // 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 tf18.tidy(() => tf18.softmax(this.runNet(input)));
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    async predictExpressions(input) {
-      const netInput = await toNetInput(input);
-      const out = await this.forwardInput(netInput);
-      const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => {
-        const data = await t.data();
-        t.dispose();
-        return data;
-      }));
-      out.dispose();
-      const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites));
-      return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-    }
-    getDefaultModelName() {
-      return "face_expression_model";
-    }
-    getClassifierChannelsIn() {
-      return 256;
-    }
-    getClassifierChannelsOut() {
-      return 7;
-    }
-  }
-
-  // build/src/faceExpressionNet/index.js
-
-  // build/src/factories/WithFaceExpressions.js
-  function isWithFaceExpressions(obj) {
-    return obj["expressions"] instanceof FaceExpressions;
-  }
-  function extendWithFaceExpressions(sourceObj, expressions) {
-    const extension = {expressions};
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // build/src/draw/drawFaceExpressions.js
-  function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) {
-    const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
-    faceExpressionsArray.forEach((e) => {
-      const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0;
-      if (!expr) {
-        throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");
-      }
-      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} (${round(expr2.probability)})`), anchor);
-      drawTextField.draw(canvasArg);
-    });
-  }
-
-  // build/src/factories/WithFaceLandmarks.js
-  function isWithFaceLandmarks(obj) {
-    return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection;
-  }
-  function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
-    const {box: shift} = sourceObj.detection;
-    const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
-    const rect = landmarks.align();
-    const {imageDims} = sourceObj.detection;
-    const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
-    const extension = {
-      landmarks,
-      unshiftedLandmarks,
-      alignedRect
-    };
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // build/src/draw/DrawFaceLandmarks.js
-  class DrawFaceLandmarksOptions {
-    constructor(options = {}) {
-      const {drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor} = options;
-      this.drawLines = drawLines;
-      this.drawPoints = drawPoints;
-      this.lineWidth = lineWidth || 1;
-      this.pointSize = pointSize || 2;
-      this.lineColor = lineColor || "rgba(0, 255, 255, 1)";
-      this.pointColor = pointColor || "rgba(255, 0, 255, 1)";
-    }
-  }
-  class DrawFaceLandmarks {
-    constructor(faceLandmarks, options = {}) {
-      this.faceLandmarks = faceLandmarks;
-      this.options = new DrawFaceLandmarksOptions(options);
-    }
-    draw(canvasArg) {
-      const ctx = getContext2dOrThrow(canvasArg);
-      const {drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor} = this.options;
-      if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {
-        ctx.strokeStyle = lineColor;
-        ctx.lineWidth = lineWidth;
-        drawContour(ctx, this.faceLandmarks.getJawOutline());
-        drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
-        drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
-        drawContour(ctx, this.faceLandmarks.getNose());
-        drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
-        drawContour(ctx, this.faceLandmarks.getRightEye(), true);
-        drawContour(ctx, this.faceLandmarks.getMouth(), true);
-      }
-      if (drawPoints) {
-        ctx.strokeStyle = pointColor;
-        ctx.fillStyle = pointColor;
-        const drawPoint = (pt) => {
-          ctx.beginPath();
-          ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
-          ctx.fill();
-        };
-        this.faceLandmarks.positions.forEach(drawPoint);
-      }
-    }
-  }
-  function drawFaceLandmarks(canvasArg, faceLandmarks) {
-    const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
-    faceLandmarksArray.forEach((f) => {
-      const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0;
-      if (!landmarks) {
-        throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");
-      }
-      new DrawFaceLandmarks(landmarks).draw(canvasArg);
-    });
-  }
-
-  // build/src/draw/index.js
-  const draw_exports = {};
-  __export(draw_exports, {
-    AnchorPosition: () => AnchorPosition,
-    DrawBox: () => DrawBox,
-    DrawBoxOptions: () => DrawBoxOptions,
-    DrawFaceLandmarks: () => DrawFaceLandmarks,
-    DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions,
-    DrawTextField: () => DrawTextField,
-    DrawTextFieldOptions: () => DrawTextFieldOptions,
-    drawContour: () => drawContour,
-    drawDetections: () => drawDetections,
-    drawFaceExpressions: () => drawFaceExpressions,
-    drawFaceLandmarks: () => drawFaceLandmarks
-  });
-
-  // build/src/xception/extractParams.js
-  function extractorsFactory3(extractWeights, paramMappings) {
-    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-    function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {
-      const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);
-      const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);
-      const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);
-      return {separable_conv0, separable_conv1, expansion_conv};
-    }
-    function extractMainBlockParams(channels, mappedPrefix) {
-      const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);
-      const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);
-      const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);
-      return {separable_conv0, separable_conv1, separable_conv2};
-    }
-    return {
-      extractConvParams,
-      extractSeparableConvParams,
-      extractReductionBlockParams,
-      extractMainBlockParams
-    };
-  }
-  function extractParams5(weights, numMainBlocks) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = extractorsFactory3(extractWeights, paramMappings);
-    const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in");
-    const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0");
-    const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1");
-    const entry_flow = {
-      conv_in: entry_flow_conv_in,
-      reduction_block_0: entry_flow_reduction_block_0,
-      reduction_block_1: entry_flow_reduction_block_1
-    };
-    const middle_flow = {};
-    range(numMainBlocks, 0, 1).forEach((idx) => {
-      middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
-    });
-    const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
-    const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv");
-    const exit_flow = {
-      reduction_block: exit_flow_reduction_block,
-      separable_conv: exit_flow_separable_conv
-    };
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      paramMappings,
-      params: {entry_flow, middle_flow, exit_flow}
-    };
-  }
-
-  // build/src/xception/extractParamsFromWeigthMap.js
-  function loadParamsFactory3(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-    function extractReductionBlockParams(mappedPrefix) {
-      const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-      const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-      const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);
-      return {separable_conv0, separable_conv1, expansion_conv};
-    }
-    function extractMainBlockParams(mappedPrefix) {
-      const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-      const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-      const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);
-      return {separable_conv0, separable_conv1, separable_conv2};
-    }
-    return {
-      extractConvParams,
-      extractSeparableConvParams,
-      extractReductionBlockParams,
-      extractMainBlockParams
-    };
-  }
-  function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
-    const paramMappings = [];
-    const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = loadParamsFactory3(weightMap, paramMappings);
-    const entry_flow_conv_in = extractConvParams("entry_flow/conv_in");
-    const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0");
-    const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1");
-    const entry_flow = {
-      conv_in: entry_flow_conv_in,
-      reduction_block_0: entry_flow_reduction_block_0,
-      reduction_block_1: entry_flow_reduction_block_1
-    };
-    const middle_flow = {};
-    range(numMainBlocks, 0, 1).forEach((idx) => {
-      middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
-    });
-    const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
-    const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv");
-    const exit_flow = {
-      reduction_block: exit_flow_reduction_block,
-      separable_conv: exit_flow_separable_conv
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params: {entry_flow, middle_flow, exit_flow}, paramMappings};
-  }
-
-  // build/src/xception/TinyXception.js
-  const tf19 = __toModule(require("@tensorflow/tfjs"));
-  function conv(x, params, stride) {
-    return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias);
-  }
-  function reductionBlock(x, params, isActivateInput = true) {
-    let out = isActivateInput ? tf19.relu(x) : x;
-    out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-    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(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 {
-    constructor(numMainBlocks) {
-      super("TinyXception");
-      this._numMainBlocks = numMainBlocks;
-    }
-    forwardInput(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("TinyXception - load model before inference");
-      }
-      return tf19.tidy(() => {
-        const batchTensor = input.toBatchTensor(112, true);
-        const meanRgb = [122.782, 117.001, 104.298];
-        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);
-        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 = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
-        return out;
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    getDefaultModelName() {
-      return "tiny_xception_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMap5(weightMap, this._numMainBlocks);
-    }
-    extractParams(weights) {
-      return extractParams5(weights, this._numMainBlocks);
-    }
-  }
-
-  // build/src/ageGenderNet/extractParams.js
-  function extractParams7(weights) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-    const age = extractFCParams(512, 1, "fc/age");
-    const gender = extractFCParams(512, 2, "fc/gender");
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      paramMappings,
-      params: {fc: {age, gender}}
-    };
-  }
-
-  // build/src/ageGenderNet/extractParamsFromWeigthMap.js
-  function extractParamsFromWeigthMap7(weightMap) {
-    const paramMappings = [];
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractFcParams(prefix) {
-      const weights = extractWeightEntry(`${prefix}/weights`, 2);
-      const bias = extractWeightEntry(`${prefix}/bias`, 1);
-      return {weights, bias};
-    }
-    const params = {
-      fc: {
-        age: extractFcParams("fc/age"),
-        gender: extractFcParams("fc/gender")
-      }
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/ageGenderNet/types.js
-  var Gender;
-  (function(Gender2) {
-    Gender2["FEMALE"] = "female";
-    Gender2["MALE"] = "male";
-  })(Gender || (Gender = {}));
-
-  // build/src/ageGenderNet/AgeGenderNet.js
-  const tf20 = __toModule(require("@tensorflow/tfjs"));
-  class AgeGenderNet extends NeuralNetwork {
-    constructor(faceFeatureExtractor = new TinyXception(2)) {
-      super("AgeGenderNet");
-      this._faceFeatureExtractor = faceFeatureExtractor;
-    }
-    get faceFeatureExtractor() {
-      return this._faceFeatureExtractor;
-    }
-    runNet(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error(`${this._name} - load model before inference`);
-      }
-      return tf20.tidy(() => {
-        const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-        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 tf20.tidy(() => {
-        const {age, gender} = this.runNet(input);
-        return {age, gender: tf20.softmax(gender)};
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    async predictAgeAndGender(input) {
-      const netInput = await toNetInput(input);
-      const out = await this.forwardInput(netInput);
-      const ages = tf20.unstack(out.age);
-      const genders = tf20.unstack(out.gender);
-      const ageAndGenderTensors = ages.map((ageTensor, i) => ({
-        ageTensor,
-        genderTensor: genders[i]
-      }));
-      const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ageTensor, genderTensor}) => {
-        const age = (await ageTensor.data())[0];
-        const probMale = (await genderTensor.data())[0];
-        const isMale = probMale > 0.5;
-        const gender = isMale ? Gender.MALE : Gender.FEMALE;
-        const genderProbability = isMale ? probMale : 1 - probMale;
-        ageTensor.dispose();
-        genderTensor.dispose();
-        return {age, gender, genderProbability};
-      }));
-      out.age.dispose();
-      out.gender.dispose();
-      return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-    }
-    getDefaultModelName() {
-      return "age_gender_model";
-    }
-    dispose(throwOnRedispose = true) {
-      this.faceFeatureExtractor.dispose(throwOnRedispose);
-      super.dispose(throwOnRedispose);
-    }
-    loadClassifierParams(weights) {
-      const {params, paramMappings} = this.extractClassifierParams(weights);
-      this._params = params;
-      this._paramMappings = paramMappings;
-    }
-    extractClassifierParams(weights) {
-      return extractParams7(weights);
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-      this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-      return extractParamsFromWeigthMap7(classifierMap);
-    }
-    extractParams(weights) {
-      const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2);
-      const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-      const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-      this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-      return this.extractClassifierParams(classifierWeights);
-    }
-  }
-
-  // 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}) => {
-        const scale2 = inputSize / Math.max(height, width);
-        return {
-          width: width * scale2,
-          height: height * scale2
-        };
-      });
-      const batchSize = inputDimensions.length;
-      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];
-          return cond(width, height) ? Math.abs(width - height) / 2 : 0;
-        };
-        const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
-        const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-        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 tf21.tidy(() => {
-        const out = this.runNet(input);
-        return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    async detectLandmarks(input) {
-      const netInput = await toNetInput(input);
-      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));
-        const yCoords = landmarksArray.filter((_, i) => !isEven(i));
-        return new FaceLandmarks68(Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), {
-          height: netInput.getInputHeight(batchIdx),
-          width: netInput.getInputWidth(batchIdx)
-        });
-      }));
-      landmarkTensors.forEach((t) => t.dispose());
-      return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0];
-    }
-    getClassifierChannelsOut() {
-      return 136;
-    }
-  }
-
-  // build/src/faceLandmarkNet/FaceLandmark68Net.js
-  class FaceLandmark68Net extends FaceLandmark68NetBase {
-    constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-      super("FaceLandmark68Net", faceFeatureExtractor);
-    }
-    getDefaultModelName() {
-      return "face_landmark_68_model";
-    }
-    getClassifierChannelsIn() {
-      return 256;
-    }
-  }
-
-  // build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js
-  function extractParamsFromWeigthMapTiny(weightMap) {
-    const paramMappings = [];
-    const {extractDenseBlock3Params} = loadParamsFactory(weightMap, paramMappings);
-    const params = {
-      dense0: extractDenseBlock3Params("dense0", true),
-      dense1: extractDenseBlock3Params("dense1"),
-      dense2: extractDenseBlock3Params("dense2")
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/faceFeatureExtractor/extractParamsTiny.js
-  function extractParamsTiny(weights) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const {extractDenseBlock3Params} = extractorsFactory(extractWeights, paramMappings);
-    const dense0 = extractDenseBlock3Params(3, 32, "dense0", true);
-    const dense1 = extractDenseBlock3Params(32, 64, "dense1");
-    const dense2 = extractDenseBlock3Params(64, 128, "dense2");
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      paramMappings,
-      params: {dense0, dense1, dense2}
-    };
-  }
-
-  // build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
-  const tf22 = __toModule(require("@tensorflow/tfjs"));
-  class TinyFaceFeatureExtractor extends NeuralNetwork {
-    constructor() {
-      super("TinyFaceFeatureExtractor");
-    }
-    forwardInput(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("TinyFaceFeatureExtractor - load model before inference");
-      }
-      return tf22.tidy(() => {
-        const batchTensor = input.toBatchTensor(112, true);
-        const meanRgb = [122.782, 117.001, 104.298];
-        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 = tf22.avgPool(out, [14, 14], [2, 2], "valid");
-        return out;
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    getDefaultModelName() {
-      return "face_feature_extractor_tiny_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMapTiny(weightMap);
-    }
-    extractParams(weights) {
-      return extractParamsTiny(weights);
-    }
-  }
-
-  // build/src/faceLandmarkNet/FaceLandmark68TinyNet.js
-  class FaceLandmark68TinyNet extends FaceLandmark68NetBase {
-    constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-      super("FaceLandmark68TinyNet", faceFeatureExtractor);
-    }
-    getDefaultModelName() {
-      return "face_landmark_68_tiny_model";
-    }
-    getClassifierChannelsIn() {
-      return 128;
-    }
-  }
-
-  // build/src/faceLandmarkNet/index.js
-  class FaceLandmarkNet extends FaceLandmark68Net {
-  }
-
-  // build/src/faceRecognitionNet/scaleLayer.js
-  const tf23 = __toModule(require("@tensorflow/tfjs"));
-  function scale(x, params) {
-    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 = tf24.conv2d(x, filters, strides, padding);
-    out = tf24.add(out, bias);
-    out = scale(out, params.scale);
-    return withRelu ? tf24.relu(out) : out;
-  }
-  function conv2(x, params) {
-    return convLayer2(x, params, [1, 1], true);
-  }
-  function convNoRelu(x, params) {
-    return convLayer2(x, params, [1, 1], false);
-  }
-  function convDown(x, params) {
-    return convLayer2(x, params, [2, 2], true, "valid");
-  }
-
-  // build/src/faceRecognitionNet/extractParams.js
-  const tf25 = __toModule(require("@tensorflow/tfjs"));
-  function extractorsFactory5(extractWeights, paramMappings) {
-    function extractFilterValues(numFilterValues, numFilters, filterSize) {
-      const weights = extractWeights(numFilterValues);
-      const depth = weights.length / (numFilters * filterSize * filterSize);
-      if (isFloat(depth)) {
-        throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
-      }
-      return tf25.tidy(() => tf25.transpose(tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
-    }
-    function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-      const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-      const bias = tf25.tensor1d(extractWeights(numFilters));
-      paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
-      return {filters, bias};
-    }
-    function extractScaleLayerParams(numWeights, mappedPrefix) {
-      const weights = tf25.tensor1d(extractWeights(numWeights));
-      const biases = tf25.tensor1d(extractWeights(numWeights));
-      paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
-      return {
-        weights,
-        biases
-      };
-    }
-    function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-      const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);
-      const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);
-      return {conv: conv3, scale: scale2};
-    }
-    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) {
-      const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);
-      const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);
-      return {conv1, conv2: conv22};
-    }
-    return {
-      extractConvLayerParams,
-      extractResidualLayerParams
-    };
-  }
-  function extractParams9(weights) {
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const paramMappings = [];
-    const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory5(extractWeights, paramMappings);
-    const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down");
-    const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1");
-    const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2");
-    const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3");
-    const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true);
-    const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1");
-    const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2");
-    const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3");
-    const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true);
-    const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1");
-    const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2");
-    const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true);
-    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 = 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: ${getRemainingWeights().length}`);
-    }
-    const params = {
-      conv32_down,
-      conv32_1,
-      conv32_2,
-      conv32_3,
-      conv64_down,
-      conv64_1,
-      conv64_2,
-      conv64_3,
-      conv128_down,
-      conv128_1,
-      conv128_2,
-      conv256_down,
-      conv256_1,
-      conv256_2,
-      conv256_down_out,
-      fc
-    };
-    return {params, paramMappings};
-  }
-
-  // build/src/faceRecognitionNet/extractParamsFromWeigthMap.js
-  function extractorsFactory6(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractScaleLayerParams(prefix) {
-      const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);
-      const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);
-      return {weights, biases};
-    }
-    function extractConvLayerParams(prefix) {
-      const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);
-      const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);
-      const scale2 = extractScaleLayerParams(prefix);
-      return {conv: {filters, bias}, scale: scale2};
-    }
-    function extractResidualLayerParams(prefix) {
-      return {
-        conv1: extractConvLayerParams(`${prefix}/conv1`),
-        conv2: extractConvLayerParams(`${prefix}/conv2`)
-      };
-    }
-    return {
-      extractConvLayerParams,
-      extractResidualLayerParams
-    };
-  }
-  function extractParamsFromWeigthMap9(weightMap) {
-    const paramMappings = [];
-    const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory6(weightMap, paramMappings);
-    const conv32_down = extractConvLayerParams("conv32_down");
-    const conv32_1 = extractResidualLayerParams("conv32_1");
-    const conv32_2 = extractResidualLayerParams("conv32_2");
-    const conv32_3 = extractResidualLayerParams("conv32_3");
-    const conv64_down = extractResidualLayerParams("conv64_down");
-    const conv64_1 = extractResidualLayerParams("conv64_1");
-    const conv64_2 = extractResidualLayerParams("conv64_2");
-    const conv64_3 = extractResidualLayerParams("conv64_3");
-    const conv128_down = extractResidualLayerParams("conv128_down");
-    const conv128_1 = extractResidualLayerParams("conv128_1");
-    const conv128_2 = extractResidualLayerParams("conv128_2");
-    const conv256_down = extractResidualLayerParams("conv256_down");
-    const conv256_1 = extractResidualLayerParams("conv256_1");
-    const conv256_2 = extractResidualLayerParams("conv256_2");
-    const conv256_down_out = extractResidualLayerParams("conv256_down_out");
-    const fc = weightMap["fc"];
-    paramMappings.push({originalPath: "fc", paramPath: "fc"});
-    if (!isTensor2D(fc)) {
-      throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);
-    }
-    const params = {
-      conv32_down,
-      conv32_1,
-      conv32_2,
-      conv32_3,
-      conv64_down,
-      conv64_1,
-      conv64_2,
-      conv64_3,
-      conv128_down,
-      conv128_1,
-      conv128_2,
-      conv256_down,
-      conv256_1,
-      conv256_2,
-      conv256_down_out,
-      fc
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // 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 = 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 = 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 = tf26.zeros(padShapeX);
-      out = tf26.concat([out, zerosW], 1);
-      const padShapeY = [...out.shape];
-      padShapeY[2] = 1;
-      const zerosH = tf26.zeros(padShapeY);
-      out = tf26.concat([out, zerosH], 2);
-    }
-    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");
-    }
-    forwardInput(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("FaceRecognitionNet - load model before inference");
-      }
-      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(tf27.scalar(256));
-        let out = convDown(normalized, params.conv32_down);
-        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);
-        out = residualDown(out, params.conv64_down);
-        out = residual(out, params.conv64_1);
-        out = residual(out, params.conv64_2);
-        out = residual(out, params.conv64_3);
-        out = residualDown(out, params.conv128_down);
-        out = residual(out, params.conv128_1);
-        out = residual(out, params.conv128_2);
-        out = residualDown(out, params.conv256_down);
-        out = residual(out, params.conv256_1);
-        out = residual(out, params.conv256_2);
-        out = residualDown(out, params.conv256_down_out);
-        const globalAvg = out.mean([1, 2]);
-        const fullyConnected = tf27.matMul(globalAvg, params.fc);
-        return fullyConnected;
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    async computeFaceDescriptor(input) {
-      const netInput = await toNetInput(input);
-      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];
-    }
-    getDefaultModelName() {
-      return "face_recognition_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMap9(weightMap);
-    }
-    extractParams(weights) {
-      return extractParams9(weights);
-    }
-  }
-
-  // build/src/faceRecognitionNet/index.js
-  function createFaceRecognitionNet(weights) {
-    const net = new FaceRecognitionNet();
-    net.extractWeights(weights);
-    return net;
-  }
-
-  // build/src/factories/WithFaceDescriptor.js
-  function extendWithFaceDescriptor(sourceObj, descriptor) {
-    const extension = {descriptor};
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // build/src/factories/WithAge.js
-  function isWithAge(obj) {
-    return typeof obj["age"] === "number";
-  }
-  function extendWithAge(sourceObj, age) {
-    const extension = {age};
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // build/src/factories/WithGender.js
-  function isWithGender(obj) {
-    return (obj["gender"] === Gender.MALE || obj["gender"] === Gender.FEMALE) && isValidProbablitiy(obj["genderProbability"]);
-  }
-  function extendWithGender(sourceObj, gender, genderProbability) {
-    const extension = {gender, genderProbability};
-    return Object.assign({}, sourceObj, extension);
-  }
-
-  // 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 = 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,
-        batch_norm_scale,
-        batch_norm_offset,
-        batch_norm_mean,
-        batch_norm_variance
-      };
-    }
-    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-      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}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
-      return {filters, bias};
-    }
-    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
-      const {filters, bias} = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
-      return {
-        filters,
-        batch_norm_offset: bias
-      };
-    }
-    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
-      const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
-      const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
-      return {depthwise_conv, pointwise_conv};
-    }
-    function extractMobilenetV1Params() {
-      const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0");
-      const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1");
-      const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2");
-      const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3");
-      const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4");
-      const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5");
-      const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6");
-      const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7");
-      const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8");
-      const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9");
-      const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10");
-      const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11");
-      const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12");
-      const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13");
-      return {
-        conv_0,
-        conv_1,
-        conv_2,
-        conv_3,
-        conv_4,
-        conv_5,
-        conv_6,
-        conv_7,
-        conv_8,
-        conv_9,
-        conv_10,
-        conv_11,
-        conv_12,
-        conv_13
-      };
-    }
-    function extractPredictionLayerParams() {
-      const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0");
-      const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1");
-      const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2");
-      const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3");
-      const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4");
-      const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5");
-      const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6");
-      const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7");
-      const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor");
-      const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor");
-      const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor");
-      const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor");
-      const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor");
-      const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor");
-      const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor");
-      const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor");
-      const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor");
-      const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor");
-      const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor");
-      const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor");
-      const box_predictor_0 = {
-        box_encoding_predictor: box_encoding_0_predictor,
-        class_predictor: class_predictor_0
-      };
-      const box_predictor_1 = {
-        box_encoding_predictor: box_encoding_1_predictor,
-        class_predictor: class_predictor_1
-      };
-      const box_predictor_2 = {
-        box_encoding_predictor: box_encoding_2_predictor,
-        class_predictor: class_predictor_2
-      };
-      const box_predictor_3 = {
-        box_encoding_predictor: box_encoding_3_predictor,
-        class_predictor: class_predictor_3
-      };
-      const box_predictor_4 = {
-        box_encoding_predictor: box_encoding_4_predictor,
-        class_predictor: class_predictor_4
-      };
-      const box_predictor_5 = {
-        box_encoding_predictor: box_encoding_5_predictor,
-        class_predictor: class_predictor_5
-      };
-      return {
-        conv_0,
-        conv_1,
-        conv_2,
-        conv_3,
-        conv_4,
-        conv_5,
-        conv_6,
-        conv_7,
-        box_predictor_0,
-        box_predictor_1,
-        box_predictor_2,
-        box_predictor_3,
-        box_predictor_4,
-        box_predictor_5
-      };
-    }
-    return {
-      extractMobilenetV1Params,
-      extractPredictionLayerParams
-    };
-  }
-  function extractParams11(weights) {
-    const paramMappings = [];
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory7(extractWeights, paramMappings);
-    const mobilenetv1 = extractMobilenetV1Params();
-    const prediction_layer = extractPredictionLayerParams();
-    const extra_dim = tf28.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
-    const output_layer = {
-      extra_dim
-    };
-    paramMappings.push({paramPath: "output_layer/extra_dim"});
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {
-      params: {
-        mobilenetv1,
-        prediction_layer,
-        output_layer
-      },
-      paramMappings
-    };
-  }
-
-  // build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js
-  function extractorsFactory8(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-      const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);
-      const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);
-      return {filters, batch_norm_offset};
-    }
-    function extractConvPairParams(idx) {
-      const mappedPrefix = `mobilenetv1/conv_${idx}`;
-      const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;
-      const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;
-      const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;
-      const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);
-      const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);
-      const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);
-      const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);
-      const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);
-      return {
-        depthwise_conv: {
-          filters,
-          batch_norm_scale,
-          batch_norm_offset,
-          batch_norm_mean,
-          batch_norm_variance
-        },
-        pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv)
-      };
-    }
-    function extractMobilenetV1Params() {
-      return {
-        conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"),
-        conv_1: extractConvPairParams(1),
-        conv_2: extractConvPairParams(2),
-        conv_3: extractConvPairParams(3),
-        conv_4: extractConvPairParams(4),
-        conv_5: extractConvPairParams(5),
-        conv_6: extractConvPairParams(6),
-        conv_7: extractConvPairParams(7),
-        conv_8: extractConvPairParams(8),
-        conv_9: extractConvPairParams(9),
-        conv_10: extractConvPairParams(10),
-        conv_11: extractConvPairParams(11),
-        conv_12: extractConvPairParams(12),
-        conv_13: extractConvPairParams(13)
-      };
-    }
-    function extractConvParams(prefix, mappedPrefix) {
-      const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);
-      const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);
-      return {filters, bias};
-    }
-    function extractBoxPredictorParams(idx) {
-      const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`);
-      const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`);
-      return {box_encoding_predictor, class_predictor};
-    }
-    function extractPredictionLayerParams() {
-      return {
-        conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"),
-        conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"),
-        conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"),
-        conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"),
-        conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"),
-        conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"),
-        conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"),
-        conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"),
-        box_predictor_0: extractBoxPredictorParams(0),
-        box_predictor_1: extractBoxPredictorParams(1),
-        box_predictor_2: extractBoxPredictorParams(2),
-        box_predictor_3: extractBoxPredictorParams(3),
-        box_predictor_4: extractBoxPredictorParams(4),
-        box_predictor_5: extractBoxPredictorParams(5)
-      };
-    }
-    return {
-      extractMobilenetV1Params,
-      extractPredictionLayerParams
-    };
-  }
-  function extractParamsFromWeigthMap11(weightMap) {
-    const paramMappings = [];
-    const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory8(weightMap, paramMappings);
-    const extra_dim = weightMap["Output/extra_dim"];
-    paramMappings.push({originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim"});
-    if (!isTensor3D(extra_dim)) {
-      throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);
-    }
-    const params = {
-      mobilenetv1: extractMobilenetV1Params(),
-      prediction_layer: extractPredictionLayerParams(),
-      output_layer: {
-        extra_dim
-      }
-    };
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/ssdMobilenetv1/pointwiseConvLayer.js
-  const tf29 = __toModule(require("@tensorflow/tfjs"));
-  function pointwiseConvLayer(x, params, strides) {
-    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 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 tf30.tidy(() => {
-      let conv11;
-      let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
-      const convPairParams = [
-        params.conv_1,
-        params.conv_2,
-        params.conv_3,
-        params.conv_4,
-        params.conv_5,
-        params.conv_6,
-        params.conv_7,
-        params.conv_8,
-        params.conv_9,
-        params.conv_10,
-        params.conv_11,
-        params.conv_12,
-        params.conv_13
-      ];
-      convPairParams.forEach((param, i) => {
-        const layerIdx = i + 1;
-        const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
-        out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
-        out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
-        if (layerIdx === 11) {
-          conv11 = out;
-        }
-      });
-      if (conv11 === null) {
-        throw new Error("mobileNetV1 - output of conv layer 11 is null");
-      }
-      return {
-        out,
-        conv11
-      };
-    });
-  }
-
-  // build/src/ssdMobilenetv1/nonMaxSuppression.js
-  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);
-    const suppressFunc = (x) => x <= iouThreshold ? 1 : 0;
-    const selected = [];
-    candidates.forEach((c) => {
-      if (selected.length >= outputSize) {
-        return;
-      }
-      const originalScore = c.score;
-      for (let j = selected.length - 1; j >= 0; --j) {
-        const iou3 = IOU(boxes, c.boxIndex, selected[j]);
-        if (iou3 === 0) {
-          continue;
-        }
-        c.score *= suppressFunc(iou3);
-        if (c.score <= scoreThreshold) {
-          break;
-        }
-      }
-      if (originalScore === c.score) {
-        selected.push(c.boxIndex);
-      }
-    });
-    return selected;
-  }
-  function IOU(boxes, i, j) {
-    const boxesData = boxes.arraySync();
-    const yminI = Math.min(boxesData[i][0], boxesData[i][2]);
-    const xminI = Math.min(boxesData[i][1], boxesData[i][3]);
-    const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
-    const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
-    const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
-    const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
-    const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
-    const xmaxJ = Math.max(boxesData[j][1], boxesData[j][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);
-  }
-
-  // build/src/ssdMobilenetv1/outputLayer.js
-  const tf31 = __toModule(require("@tensorflow/tfjs"));
-  function getCenterCoordinatesAndSizesLayer(x) {
-    const vec = tf31.unstack(tf31.transpose(x, [1, 0]));
-    const sizes = [
-      tf31.sub(vec[2], vec[0]),
-      tf31.sub(vec[3], vec[1])
-    ];
-    const centers = [
-      tf31.add(vec[0], tf31.div(sizes[0], tf31.scalar(2))),
-      tf31.add(vec[1], tf31.div(sizes[1], tf31.scalar(2)))
-    ];
-    return {
-      sizes,
-      centers
-    };
-  }
-  function decodeBoxesLayer(x0, x1) {
-    const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-    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 tf31.tidy(() => {
-      const batchSize = boxPredictions.shape[0];
-      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
-      };
-    });
-  }
-
-  // build/src/ssdMobilenetv1/boxPredictionLayer.js
-  const tf32 = __toModule(require("@tensorflow/tfjs"));
-  function boxPredictionLayer(x, params) {
-    return tf32.tidy(() => {
-      const batchSize = x.shape[0];
-      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
-      };
-    });
-  }
-
-  // build/src/ssdMobilenetv1/predictionLayer.js
-  const tf33 = __toModule(require("@tensorflow/tfjs"));
-  function predictionLayer(x, conv11, params) {
-    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]);
-      const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]);
-      const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
-      const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
-      const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
-      const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
-      const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
-      const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
-      const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
-      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 = tf33.concat([
-        boxPrediction0.boxPredictionEncoding,
-        boxPrediction1.boxPredictionEncoding,
-        boxPrediction2.boxPredictionEncoding,
-        boxPrediction3.boxPredictionEncoding,
-        boxPrediction4.boxPredictionEncoding,
-        boxPrediction5.boxPredictionEncoding
-      ], 1);
-      const classPredictions = tf33.concat([
-        boxPrediction0.classPrediction,
-        boxPrediction1.classPrediction,
-        boxPrediction2.classPrediction,
-        boxPrediction3.classPrediction,
-        boxPrediction4.classPrediction,
-        boxPrediction5.classPrediction
-      ], 1);
-      return {
-        boxPredictions,
-        classPredictions
-      };
-    });
-  }
-
-  // build/src/ssdMobilenetv1/SsdMobilenetv1Options.js
-  class SsdMobilenetv1Options {
-    constructor({minConfidence, maxResults} = {}) {
-      this._name = "SsdMobilenetv1Options";
-      this._minConfidence = minConfidence || 0.5;
-      this._maxResults = maxResults || 100;
-      if (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;
-    }
-  }
-
-  // build/src/ssdMobilenetv1/SsdMobilenetv1.js
-  const tf34 = __toModule(require("@tensorflow/tfjs"));
-  class SsdMobilenetv1 extends NeuralNetwork {
-    constructor() {
-      super("SsdMobilenetv1");
-    }
-    forwardInput(input) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("SsdMobilenetv1 - load model before inference");
-      }
-      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);
-      });
-    }
-    async forward(input) {
-      return this.forwardInput(await toNetInput(input));
-    }
-    async locateFaces(input, options = {}) {
-      const {maxResults, minConfidence} = new SsdMobilenetv1Options(options);
-      const netInput = await toNetInput(input);
-      const {boxes: _boxes, scores: _scores} = this.forwardInput(netInput);
-      const boxes = _boxes[0];
-      const scores = _scores[0];
-      for (let i = 1; i < _boxes.length; i++) {
-        _boxes[i].dispose();
-        _scores[i].dispose();
-      }
-      const scoresData = Array.from(await scores.data());
-      const iouThreshold = 0.5;
-      const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-      const reshapedDims = netInput.getReshapedInputDimensions(0);
-      const inputSize = netInput.inputSize;
-      const padX = inputSize / reshapedDims.width;
-      const padY = inputSize / reshapedDims.height;
-      const boxesData = boxes.arraySync();
-      const results = indices.map((idx) => {
-        const [top, bottom] = [
-          Math.max(0, boxesData[idx][0]),
-          Math.min(1, boxesData[idx][2])
-        ].map((val) => val * padY);
-        const [left, right] = [
-          Math.max(0, boxesData[idx][1]),
-          Math.min(1, boxesData[idx][3])
-        ].map((val) => val * padX);
-        return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
-          height: netInput.getInputHeight(0),
-          width: netInput.getInputWidth(0)
-        });
-      });
-      boxes.dispose();
-      scores.dispose();
-      return results;
-    }
-    getDefaultModelName() {
-      return "ssd_mobilenetv1_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMap11(weightMap);
-    }
-    extractParams(weights) {
-      return extractParams11(weights);
-    }
-  }
-
-  // build/src/ssdMobilenetv1/index.js
-  function createSsdMobilenetv1(weights) {
-    const net = new SsdMobilenetv1();
-    net.extractWeights(weights);
-    return net;
-  }
-  function createFaceDetectionNet(weights) {
-    return createSsdMobilenetv1(weights);
-  }
-  class FaceDetectionNet extends SsdMobilenetv1 {
-  }
-
-  // build/src/tinyYolov2/const.js
-  const IOU_THRESHOLD = 0.4;
-  const BOX_ANCHORS = [
-    new Point(0.738768, 0.874946),
-    new Point(2.42204, 2.65704),
-    new Point(4.30971, 7.04493),
-    new Point(10.246, 4.59428),
-    new Point(12.6868, 11.8741)
-  ];
-  const BOX_ANCHORS_SEPARABLE = [
-    new Point(1.603231, 2.094468),
-    new Point(6.041143, 7.080126),
-    new Point(2.882459, 3.518061),
-    new Point(4.266906, 5.178857),
-    new Point(9.041765, 10.66308)
-  ];
-  const MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-  const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-  const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
-
-  // build/src/tinyYolov2/config.js
-  const isNumber = (arg) => typeof arg === "number";
-  function validateConfig(config2) {
-    if (!config2) {
-      throw new Error(`invalid config: ${config2}`);
-    }
-    if (typeof config2.withSeparableConvs !== "boolean") {
-      throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
-    }
-    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) => 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(isNumber))) {
-      throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
-    }
-  }
-
-  // build/src/tinyYolov2/leaky.js
-  const tf35 = __toModule(require("@tensorflow/tfjs"));
-  function leaky(x) {
-    return tf35.tidy(() => {
-      const min = tf35.mul(x, tf35.scalar(0.10000000149011612));
-      return tf35.add(tf35.relu(tf35.sub(x, min)), min);
-    });
-  }
-
-  // build/src/tinyYolov2/convWithBatchNorm.js
-  const tf36 = __toModule(require("@tensorflow/tfjs"));
-  function convWithBatchNorm(x, params) {
-    return tf36.tidy(() => {
-      let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-      out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid");
-      out = tf36.sub(out, params.bn.sub);
-      out = tf36.mul(out, params.bn.truediv);
-      out = tf36.add(out, params.conv.bias);
-      return leaky(out);
-    });
-  }
-
-  // build/src/tinyYolov2/depthwiseSeparableConv.js
-  const tf37 = __toModule(require("@tensorflow/tfjs"));
-  function depthwiseSeparableConv3(x, params) {
-    return tf37.tidy(() => {
-      let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-      out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-      out = tf37.add(out, params.bias);
-      return leaky(out);
-    });
-  }
-
-  // build/src/tinyYolov2/extractParams.js
-  const tf38 = __toModule(require("@tensorflow/tfjs"));
-  function extractorsFactory9(extractWeights, paramMappings) {
-    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-    function extractBatchNormParams(size, mappedPrefix) {
-      const sub6 = tf38.tensor1d(extractWeights(size));
-      const truediv = tf38.tensor1d(extractWeights(size));
-      paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-      return {sub: sub6, truediv};
-    }
-    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
-      const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
-      const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);
-      return {conv: conv3, bn};
-    }
-    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-    return {
-      extractConvParams,
-      extractConvWithBatchNormParams,
-      extractSeparableConvParams
-    };
-  }
-  function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
-    const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-    const paramMappings = [];
-    const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory9(extractWeights, paramMappings);
-    let params;
-    if (config2.withSeparableConvs) {
-      const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-      const conv0 = config2.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0");
-      const conv1 = extractSeparableConvParams(s1, s2, "conv1");
-      const conv22 = extractSeparableConvParams(s2, s3, "conv2");
-      const conv3 = extractSeparableConvParams(s3, s4, "conv3");
-      const conv4 = extractSeparableConvParams(s4, s5, "conv4");
-      const conv5 = extractSeparableConvParams(s5, s6, "conv5");
-      const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0;
-      const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0;
-      const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8");
-      params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-    } else {
-      const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-      const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0");
-      const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1");
-      const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2");
-      const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3");
-      const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4");
-      const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5");
-      const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6");
-      const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7");
-      const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8");
-      params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-    }
-    if (getRemainingWeights().length !== 0) {
-      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return {params, paramMappings};
-  }
-
-  // build/src/tinyYolov2/extractParamsFromWeigthMap.js
-  function extractorsFactory10(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractBatchNormParams(prefix) {
-      const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
-      const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-      return {sub: sub6, truediv};
-    }
-    function extractConvParams(prefix) {
-      const filters = extractWeightEntry(`${prefix}/filters`, 4);
-      const bias = extractWeightEntry(`${prefix}/bias`, 1);
-      return {filters, bias};
-    }
-    function extractConvWithBatchNormParams(prefix) {
-      const conv3 = extractConvParams(`${prefix}/conv`);
-      const bn = extractBatchNormParams(`${prefix}/bn`);
-      return {conv: conv3, bn};
-    }
-    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-    return {
-      extractConvParams,
-      extractConvWithBatchNormParams,
-      extractSeparableConvParams
-    };
-  }
-  function extractParamsFromWeigthMap13(weightMap, config2) {
-    const paramMappings = [];
-    const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory10(weightMap, paramMappings);
-    let params;
-    if (config2.withSeparableConvs) {
-      const numFilters = config2.filterSizes && config2.filterSizes.length || 9;
-      params = {
-        conv0: config2.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"),
-        conv1: extractSeparableConvParams("conv1"),
-        conv2: extractSeparableConvParams("conv2"),
-        conv3: extractSeparableConvParams("conv3"),
-        conv4: extractSeparableConvParams("conv4"),
-        conv5: extractSeparableConvParams("conv5"),
-        conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0,
-        conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0,
-        conv8: extractConvParams("conv8")
-      };
-    } else {
-      params = {
-        conv0: extractConvWithBatchNormParams("conv0"),
-        conv1: extractConvWithBatchNormParams("conv1"),
-        conv2: extractConvWithBatchNormParams("conv2"),
-        conv3: extractConvWithBatchNormParams("conv3"),
-        conv4: extractConvWithBatchNormParams("conv4"),
-        conv5: extractConvWithBatchNormParams("conv5"),
-        conv6: extractConvWithBatchNormParams("conv6"),
-        conv7: extractConvWithBatchNormParams("conv7"),
-        conv8: extractConvParams("conv8")
-      };
-    }
-    disposeUnusedWeightTensors(weightMap, paramMappings);
-    return {params, paramMappings};
-  }
-
-  // build/src/tinyYolov2/TinyYolov2Options.js
-  var TinyYolov2SizeType;
-  (function(TinyYolov2SizeType2) {
-    TinyYolov2SizeType2[TinyYolov2SizeType2["XS"] = 224] = "XS";
-    TinyYolov2SizeType2[TinyYolov2SizeType2["SM"] = 320] = "SM";
-    TinyYolov2SizeType2[TinyYolov2SizeType2["MD"] = 416] = "MD";
-    TinyYolov2SizeType2[TinyYolov2SizeType2["LG"] = 608] = "LG";
-  })(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
-  class TinyYolov2Options {
-    constructor({inputSize, scoreThreshold} = {}) {
-      this._name = "TinyYolov2Options";
-      this._inputSize = inputSize || 416;
-      this._scoreThreshold = scoreThreshold || 0.5;
-      if (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;
-    }
-  }
-
-  // build/src/tinyYolov2/TinyYolov2Base.js
-  const tf39 = __toModule(require("@tensorflow/tfjs"));
-  class TinyYolov2Base extends NeuralNetwork {
-    constructor(config2) {
-      super("TinyYolov2");
-      validateConfig(config2);
-      this._config = config2;
-    }
-    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(x, params) {
-      let out = convWithBatchNorm(x, params.conv0);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = convWithBatchNorm(out, params.conv1);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = convWithBatchNorm(out, params.conv2);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = convWithBatchNorm(out, params.conv3);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = convWithBatchNorm(out, params.conv4);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = convWithBatchNorm(out, params.conv5);
-      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 = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = depthwiseSeparableConv3(out, params.conv1);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = depthwiseSeparableConv3(out, params.conv2);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = depthwiseSeparableConv3(out, params.conv3);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = depthwiseSeparableConv3(out, params.conv4);
-      out = tf39.maxPool(out, [2, 2], [2, 2], "same");
-      out = depthwiseSeparableConv3(out, params.conv5);
-      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);
-    }
-    forwardInput(input, inputSize) {
-      const {params} = this;
-      if (!params) {
-        throw new Error("TinyYolov2 - load model before inference");
-      }
-      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(tf39.scalar(256));
-        return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
-      });
-    }
-    async forward(input, inputSize) {
-      return await this.forwardInput(await toNetInput(input), inputSize);
-    }
-    async detect(input, forwardParams = {}) {
-      const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
-      const netInput = await toNetInput(input);
-      const out = await this.forwardInput(netInput, inputSize);
-      const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims());
-      const inputDimensions = {
-        width: netInput.getInputWidth(0),
-        height: netInput.getInputHeight(0)
-      };
-      const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
-      out.dispose();
-      out0.dispose();
-      const boxes = results.map((res) => res.box);
-      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 = 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;
-    }
-    getDefaultModelName() {
-      return "";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return extractParamsFromWeigthMap13(weightMap, this.config);
-    }
-    extractParams(weights) {
-      const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;
-      const numFilters = filterSizes ? filterSizes.length : void 0;
-      if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
-        throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);
-      }
-      return extractParams13(weights, this.config, this.boxEncodingSize, filterSizes);
-    }
-    async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) {
-      const {width, height} = inputBlobDimensions;
-      const inputSize = Math.max(width, height);
-      const correctionFactorX = inputSize / width;
-      const correctionFactorY = inputSize / height;
-      const numCells = outputTensor.shape[1];
-      const numBoxes = this.config.anchors.length;
-      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 ? 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 = [];
-      const scoresData = await scoresTensor.array();
-      const boxesData = await boxesTensor.array();
-      for (let row = 0; row < numCells; row++) {
-        for (let col = 0; col < numCells; col++) {
-          for (let anchor = 0; anchor < numBoxes; anchor++) {
-            const score = sigmoid(scoresData[row][col][anchor][0]);
-            if (!scoreThreshold || score > scoreThreshold) {
-              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({
-                box: new BoundingBox(x, y, x + width2, y + height2),
-                score,
-                classScore: score * classScore,
-                label,
-                ...pos
-              });
-            }
-          }
-        }
-      }
-      boxesTensor.dispose();
-      scoresTensor.dispose();
-      classScoresTensor.dispose();
-      return results;
-    }
-    async extractPredictedClass(classesTensor, pos) {
-      const {row, col, anchor} = pos;
-      const classesData = await classesTensor.array();
-      return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
-        classScore,
-        label
-      })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
-    }
-  }
-  TinyYolov2Base.DEFAULT_FILTER_SIZES = [
-    3,
-    16,
-    32,
-    64,
-    128,
-    256,
-    512,
-    1024,
-    1024
-  ];
-
-  // build/src/tinyYolov2/TinyYolov2.js
-  class TinyYolov2 extends TinyYolov2Base {
-    constructor(withSeparableConvs = true) {
-      const config2 = Object.assign({}, {
-        withSeparableConvs,
-        iouThreshold: IOU_THRESHOLD,
-        classes: ["face"]
-      }, withSeparableConvs ? {
-        anchors: BOX_ANCHORS_SEPARABLE,
-        meanRgb: MEAN_RGB_SEPARABLE
-      } : {
-        anchors: BOX_ANCHORS,
-        withClassScores: true
-      });
-      super(config2);
-    }
-    get withSeparableConvs() {
-      return this.config.withSeparableConvs;
-    }
-    get anchors() {
-      return this.config.anchors;
-    }
-    async locateFaces(input, forwardParams) {
-      const objectDetections = await this.detect(input, forwardParams);
-      return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-    }
-    getDefaultModelName() {
-      return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return super.extractParamsFromWeigthMap(weightMap);
-    }
-  }
-
-  // build/src/tinyYolov2/types.js
-
-  // build/src/tinyYolov2/index.js
-  function createTinyYolov2(weights, withSeparableConvs = true) {
-    const net = new TinyYolov2(withSeparableConvs);
-    net.extractWeights(weights);
-    return net;
-  }
-
-  // build/src/tinyFaceDetector/TinyFaceDetectorOptions.js
-  class TinyFaceDetectorOptions extends TinyYolov2Options {
-    constructor() {
-      super(...arguments);
-      this._name = "TinyFaceDetectorOptions";
-    }
-  }
-
-  // build/src/globalApi/ComposableTask.js
-  class ComposableTask {
-    async then(onfulfilled) {
-      return onfulfilled(await this.run());
-    }
-    async run() {
-      throw new Error("ComposableTask - run is not implemented");
-    }
-  }
-
-  // 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 tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
-    const results = await computeResults(faces);
-    faces.forEach((f) => f instanceof tf40.Tensor && f.dispose());
-    return results;
-  }
-  async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
-    return extractAllFacesAndComputeResults([parentResult], input, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment);
-  }
-
-  // build/src/tinyFaceDetector/const.js
-  const IOU_THRESHOLD2 = 0.4;
-  const BOX_ANCHORS2 = [
-    new Point(1.603231, 2.094468),
-    new Point(6.041143, 7.080126),
-    new Point(2.882459, 3.518061),
-    new Point(4.266906, 5.178857),
-    new Point(9.041765, 10.66308)
-  ];
-  const MEAN_RGB = [117.001, 114.697, 97.404];
-
-  // build/src/tinyFaceDetector/TinyFaceDetector.js
-  class TinyFaceDetector extends TinyYolov2Base {
-    constructor() {
-      const config2 = {
-        withSeparableConvs: true,
-        iouThreshold: IOU_THRESHOLD2,
-        classes: ["face"],
-        anchors: BOX_ANCHORS2,
-        meanRgb: MEAN_RGB,
-        isFirstLayerConv2d: true,
-        filterSizes: [3, 16, 32, 64, 128, 256, 512]
-      };
-      super(config2);
-    }
-    get anchors() {
-      return this.config.anchors;
-    }
-    async locateFaces(input, forwardParams) {
-      const objectDetections = await this.detect(input, forwardParams);
-      return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-    }
-    getDefaultModelName() {
-      return "tiny_face_detector_model";
-    }
-    extractParamsFromWeigthMap(weightMap) {
-      return super.extractParamsFromWeigthMap(weightMap);
-    }
-  }
-
-  // build/src/globalApi/nets.js
-  const nets = {
-    ssdMobilenetv1: new SsdMobilenetv1(),
-    tinyFaceDetector: new TinyFaceDetector(),
-    tinyYolov2: new TinyYolov2(),
-    faceLandmark68Net: new FaceLandmark68Net(),
-    faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
-    faceRecognitionNet: new FaceRecognitionNet(),
-    faceExpressionNet: new FaceExpressionNet(),
-    ageGenderNet: new AgeGenderNet()
-  };
-  const ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-  const tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-  const tinyYolov23 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-  const detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-  const detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-  const computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-  const recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-  const predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-  const loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-  const loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-  const loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-  const loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-  const loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-  const loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-  const loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-  const loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-  const loadFaceDetectionModel = loadSsdMobilenetv1Model;
-  const locateFaces = ssdMobilenetv1;
-  const detectLandmarks = detectFaceLandmarks;
-
-  // build/src/globalApi/PredictFaceExpressionsTask.js
-  class PredictFaceExpressionsTaskBase extends ComposableTask {
-    constructor(parentTask, input, extractedFaces) {
-      super();
-      this.parentTask = parentTask;
-      this.input = input;
-      this.extractedFaces = extractedFaces;
-    }
-  }
-  class PredictAllFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-    async run() {
-      const parentResults = await this.parentTask;
-      const faceExpressionsByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.faceExpressionNet.predictExpressions(face))), this.extractedFaces);
-      return parentResults.map((parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]));
-    }
-    withAgeAndGender() {
-      return new PredictAllAgeAndGenderTask(this, this.input);
-    }
-  }
-  class PredictSingleFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-    async run() {
-      const parentResult = await this.parentTask;
-      if (!parentResult) {
-        return;
-      }
-      const faceExpressions = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceExpressionNet.predictExpressions(face), this.extractedFaces);
-      return extendWithFaceExpressions(parentResult, faceExpressions);
-    }
-    withAgeAndGender() {
-      return new PredictSingleAgeAndGenderTask(this, this.input);
-    }
-  }
-  class PredictAllFaceExpressionsWithFaceAlignmentTask extends PredictAllFaceExpressionsTask {
-    withAgeAndGender() {
-      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptors() {
-      return new ComputeAllFaceDescriptorsTask(this, this.input);
-    }
-  }
-  class PredictSingleFaceExpressionsWithFaceAlignmentTask extends PredictSingleFaceExpressionsTask {
-    withAgeAndGender() {
-      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptor() {
-      return new ComputeSingleFaceDescriptorTask(this, this.input);
-    }
-  }
-
-  // build/src/globalApi/PredictAgeAndGenderTask.js
-  class PredictAgeAndGenderTaskBase extends ComposableTask {
-    constructor(parentTask, input, extractedFaces) {
-      super();
-      this.parentTask = parentTask;
-      this.input = input;
-      this.extractedFaces = extractedFaces;
-    }
-  }
-  class PredictAllAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-    async run() {
-      const parentResults = await this.parentTask;
-      const ageAndGenderByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), this.extractedFaces);
-      return parentResults.map((parentResult, i) => {
-        const {age, gender, genderProbability} = ageAndGenderByFace[i];
-        return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-      });
-    }
-    withFaceExpressions() {
-      return new PredictAllFaceExpressionsTask(this, this.input);
-    }
-  }
-  class PredictSingleAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-    async run() {
-      const parentResult = await this.parentTask;
-      if (!parentResult) {
-        return;
-      }
-      const {age, gender, genderProbability} = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.ageGenderNet.predictAgeAndGender(face), this.extractedFaces);
-      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-    }
-    withFaceExpressions() {
-      return new PredictSingleFaceExpressionsTask(this, this.input);
-    }
-  }
-  class PredictAllAgeAndGenderWithFaceAlignmentTask extends PredictAllAgeAndGenderTask {
-    withFaceExpressions() {
-      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptors() {
-      return new ComputeAllFaceDescriptorsTask(this, this.input);
-    }
-  }
-  class PredictSingleAgeAndGenderWithFaceAlignmentTask extends PredictSingleAgeAndGenderTask {
-    withFaceExpressions() {
-      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptor() {
-      return new ComputeSingleFaceDescriptorTask(this, this.input);
-    }
-  }
-
-  // build/src/globalApi/ComputeFaceDescriptorsTasks.js
-  class ComputeFaceDescriptorsTaskBase extends ComposableTask {
-    constructor(parentTask, input) {
-      super();
-      this.parentTask = parentTask;
-      this.input = input;
-    }
-  }
-  class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase {
-    async run() {
-      const parentResults = await this.parentTask;
-      const descriptors = await extractAllFacesAndComputeResults(parentResults, this.input, (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), null, (parentResult) => parentResult.landmarks.align(null, {useDlibAlignment: true}));
-      return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));
-    }
-    withFaceExpressions() {
-      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withAgeAndGender() {
-      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-  }
-  class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
-    async run() {
-      const parentResult = await this.parentTask;
-      if (!parentResult) {
-        return;
-      }
-      const descriptor = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), null, (parentResult2) => parentResult2.landmarks.align(null, {useDlibAlignment: true}));
-      return extendWithFaceDescriptor(parentResult, descriptor);
-    }
-    withFaceExpressions() {
-      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withAgeAndGender() {
-      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-  }
-
-  // build/src/globalApi/DetectFaceLandmarksTasks.js
-  const tf41 = __toModule(require("@tensorflow/tfjs"));
-  class DetectFaceLandmarksTaskBase extends ComposableTask {
-    constructor(parentTask, input, useTinyLandmarkNet) {
-      super();
-      this.parentTask = parentTask;
-      this.input = input;
-      this.useTinyLandmarkNet = useTinyLandmarkNet;
-    }
-    get landmarkNet() {
-      return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net;
-    }
-  }
-  class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-    async run() {
-      const parentResults = await this.parentTask;
-      const detections = parentResults.map((res) => res.detection);
-      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 tf41.Tensor && f.dispose());
-      return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
-    }
-    withFaceExpressions() {
-      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withAgeAndGender() {
-      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptors() {
-      return new ComputeAllFaceDescriptorsTask(this, this.input);
-    }
-  }
-  class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-    async run() {
-      const parentResult = await this.parentTask;
-      if (!parentResult) {
-        return;
-      }
-      const {detection} = parentResult;
-      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 tf41.Tensor && f.dispose());
-      return extendWithFaceLandmarks(parentResult, landmarks);
-    }
-    withFaceExpressions() {
-      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withAgeAndGender() {
-      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptor() {
-      return new ComputeSingleFaceDescriptorTask(this, this.input);
-    }
-  }
-
-  // build/src/globalApi/DetectFacesTasks.js
-  class DetectFacesTaskBase extends ComposableTask {
-    constructor(input, options = new SsdMobilenetv1Options()) {
-      super();
-      this.input = input;
-      this.options = options;
-    }
-  }
-  class DetectAllFacesTask extends DetectFacesTaskBase {
-    async run() {
-      const {input, options} = this;
-      const faceDetectionFunction = options instanceof TinyFaceDetectorOptions ? (input2) => nets.tinyFaceDetector.locateFaces(input2, options) : options instanceof SsdMobilenetv1Options ? (input2) => nets.ssdMobilenetv1.locateFaces(input2, options) : options instanceof TinyYolov2Options ? (input2) => nets.tinyYolov2.locateFaces(input2, options) : null;
-      if (!faceDetectionFunction) {
-        throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");
-      }
-      return faceDetectionFunction(input);
-    }
-    runAndExtendWithFaceDetections() {
-      return new Promise(async (res) => {
-        const detections = await this.run();
-        return res(detections.map((detection) => extendWithFaceDetection({}, detection)));
-      });
-    }
-    withFaceLandmarks(useTinyLandmarkNet = false) {
-      return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
-    }
-    withFaceExpressions() {
-      return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
-    }
-    withAgeAndGender() {
-      return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);
-    }
-  }
-  class DetectSingleFaceTask extends DetectFacesTaskBase {
-    async run() {
-      const faceDetections = await new DetectAllFacesTask(this.input, this.options);
-      let faceDetectionWithHighestScore = faceDetections[0];
-      faceDetections.forEach((faceDetection) => {
-        if (faceDetection.score > faceDetectionWithHighestScore.score) {
-          faceDetectionWithHighestScore = faceDetection;
-        }
-      });
-      return faceDetectionWithHighestScore;
-    }
-    runAndExtendWithFaceDetection() {
-      return new Promise(async (res) => {
-        const detection = await this.run();
-        return res(detection ? extendWithFaceDetection({}, detection) : void 0);
-      });
-    }
-    withFaceLandmarks(useTinyLandmarkNet = false) {
-      return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
-    }
-    withFaceExpressions() {
-      return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);
-    }
-    withAgeAndGender() {
-      return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);
-    }
-  }
-
-  // build/src/globalApi/detectFaces.js
-  function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-    return new DetectSingleFaceTask(input, options);
-  }
-  function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-    return new DetectAllFacesTask(input, options);
-  }
-
-  // build/src/globalApi/allFaces.js
-  async function allFacesSsdMobilenetv1(input, minConfidence) {
-    console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead");
-    return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? {minConfidence} : {})).withFaceLandmarks().withFaceDescriptors();
-  }
-  async function allFacesTinyYolov2(input, forwardParams = {}) {
-    console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead");
-    return await detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors();
-  }
-  const allFaces = allFacesSsdMobilenetv1;
-
-  // build/src/euclideanDistance.js
-  function euclideanDistance(arr1, arr2) {
-    if (arr1.length !== arr2.length)
-      throw new Error("euclideanDistance: arr1.length !== arr2.length");
-    const desc1 = Array.from(arr1);
-    const desc2 = Array.from(arr2);
-    return Math.sqrt(desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + Math.pow(diff, 2), 0));
-  }
-
-  // build/src/globalApi/FaceMatcher.js
-  class FaceMatcher {
-    constructor(inputs, distanceThreshold = 0.6) {
-      this._distanceThreshold = distanceThreshold;
-      const inputArray = Array.isArray(inputs) ? inputs : [inputs];
-      if (!inputArray.length) {
-        throw new Error(`FaceRecognizer.constructor - expected atleast one input`);
-      }
-      let count = 1;
-      const createUniqueLabel = () => `person ${count++}`;
-      this._labeledDescriptors = inputArray.map((desc) => {
-        if (desc instanceof LabeledFaceDescriptors) {
-          return desc;
-        }
-        if (desc instanceof Float32Array) {
-          return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
-        }
-        if (desc.descriptor && desc.descriptor instanceof Float32Array) {
-          return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) {
-      return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);
-    }
-    matchDescriptor(queryDescriptor) {
-      return this.labeledDescriptors.map(({descriptors, label}) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr);
-    }
-    findBestMatch(queryDescriptor) {
-      const bestMatch = this.matchDescriptor(queryDescriptor);
-      return bestMatch.distance < this.distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance);
-    }
-    toJSON() {
-      return {
-        distanceThreshold: this.distanceThreshold,
-        labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())
-      };
-    }
-    static fromJSON(json) {
-      const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld));
-      return new FaceMatcher(labeledDescriptors, json.distanceThreshold);
-    }
-  }
-
-  // build/src/globalApi/types.js
-
-  // build/src/globalApi/index.js
-
-  // build/src/tinyFaceDetector/index.js
-  function createTinyFaceDetector(weights) {
-    const net = new TinyFaceDetector();
-    net.extractWeights(weights);
-    return net;
-  }
-
-  // build/src/resizeResults.js
-  function resizeResults(results, dimensions) {
-    const {width, height} = new Dimensions(dimensions.width, dimensions.height);
-    if (width <= 0 || height <= 0) {
-      throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width, height})}`);
-    }
-    if (Array.isArray(results)) {
-      return results.map((obj) => resizeResults(obj, {width, height}));
-    }
-    if (isWithFaceLandmarks(results)) {
-      const resizedDetection = results.detection.forSize(width, height);
-      const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
-      return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
-    }
-    if (isWithFaceDetection(results)) {
-      return extendWithFaceDetection(results, results.detection.forSize(width, height));
-    }
-    if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
-      return results.forSize(width, height);
-    }
-    return results;
-  }
-
-  // build/package.json
-  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
deleted file mode 100644
index 4faa9da..0000000
--- a/dist/face-api.iife.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["build/src/env/../../../src/env/isNodejs.ts", "build/src/../../src/index.ts", "build/src/draw/../../../src/draw/drawContour.ts", "build/src/classes/../../../src/classes/Dimensions.ts", "build/src/utils/../../../src/utils/index.ts", "build/src/classes/../../../src/classes/Point.ts", "build/src/classes/../../../src/classes/Box.ts", "build/src/classes/../../../src/classes/BoundingBox.ts", "build/src/classes/../../../src/classes/ObjectDetection.ts", "build/src/classes/../../../src/classes/FaceDetection.ts", "build/src/ops/../../../src/ops/iou.ts", "build/src/ops/../../../src/ops/minBbox.ts", "build/src/ops/../../../src/ops/nonMaxSuppression.ts", "build/src/ops/../../../src/ops/normalize.ts", "build/src/ops/../../../src/ops/padToSquare.ts", "build/src/ops/../../../src/ops/shuffleArray.ts", "build/src/ops/../../../src/ops/index.ts", "build/src/classes/../../../src/classes/Rect.ts", "build/src/classes/../../../src/classes/FaceLandmarks.ts", "build/src/classes/../../../src/classes/FaceLandmarks5.ts", "build/src/classes/../../../src/classes/FaceLandmarks68.ts", "build/src/classes/../../../src/classes/FaceMatch.ts", "build/src/classes/../../../src/classes/LabeledBox.ts", "build/src/classes/../../../src/classes/LabeledFaceDescriptors.ts", "build/src/classes/../../../src/classes/PredictedBox.ts", "build/src/factories/../../../src/factories/WithFaceDetection.ts", "build/src/env/../../../src/env/createBrowserEnv.ts", "build/src/env/../../../src/env/createFileSystem.ts", "build/src/env/../../../src/env/createNodejsEnv.ts", "build/src/env/../../../src/env/isBrowser.ts", "build/src/env/../../../src/env/index.ts", "build/src/dom/../../../src/dom/resolveInput.ts", "build/src/dom/../../../src/dom/getContext2dOrThrow.ts", "build/src/draw/../../../src/draw/DrawTextField.ts", "build/src/draw/../../../src/draw/DrawBox.ts", "build/src/draw/../../../src/draw/drawDetections.ts", "build/src/dom/../../../src/dom/isMediaLoaded.ts", "build/src/dom/../../../src/dom/awaitMediaLoaded.ts", "build/src/dom/../../../src/dom/bufferToImage.ts", "build/src/dom/../../../src/dom/getMediaDimensions.ts", "build/src/dom/../../../src/dom/createCanvas.ts", "build/src/dom/../../../src/dom/imageTensorToCanvas.ts", "build/src/dom/../../../src/dom/isMediaElement.ts", "build/src/dom/../../../src/dom/imageToSquare.ts", "build/src/dom/../../../src/dom/NetInput.ts", "build/src/dom/../../../src/dom/toNetInput.ts", "build/src/dom/../../../src/dom/extractFaces.ts", "build/src/dom/../../../src/dom/extractFaceTensors.ts", "build/src/dom/../../../src/dom/fetchOrThrow.ts", "build/src/dom/../../../src/dom/fetchImage.ts", "build/src/dom/../../../src/dom/fetchJson.ts", "build/src/dom/../../../src/dom/fetchNetWeights.ts", "build/src/common/../../../src/common/getModelUris.ts", "build/src/dom/../../../src/dom/loadWeightMap.ts", "build/src/dom/../../../src/dom/matchDimensions.ts", "build/src/../../src/NeuralNetwork.ts", "build/src/common/../../../src/common/depthwiseSeparableConv.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/denseBlock.ts", "build/src/common/../../../src/common/convLayer.ts", "build/src/common/../../../src/common/disposeUnusedWeightTensors.ts", "build/src/common/../../../src/common/extractConvParamsFactory.ts", "build/src/common/../../../src/common/extractFCParamsFactory.ts", "build/src/common/../../../src/common/types.ts", "build/src/common/../../../src/common/extractSeparableConvParamsFactory.ts", "build/src/common/../../../src/common/extractWeightEntryFactory.ts", "build/src/common/../../../src/common/extractWeightsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractorsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParams.ts", "build/src/common/../../../src/common/loadConvParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/loadParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "build/src/common/../../../src/common/fullyConnectedLayer.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParams.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParamsFromWeigthMap.ts", "build/src/faceProcessor/../../../src/faceProcessor/util.ts", "build/src/faceProcessor/../../../src/faceProcessor/FaceProcessor.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressions.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressionNet.ts", "build/src/factories/../../../src/factories/WithFaceExpressions.ts", "build/src/draw/../../../src/draw/drawFaceExpressions.ts", "build/src/factories/../../../src/factories/WithFaceLandmarks.ts", "build/src/draw/../../../src/draw/DrawFaceLandmarks.ts", "build/src/draw/../../../src/draw/index.ts", "build/src/xception/../../../src/xception/extractParams.ts", "build/src/xception/../../../src/xception/extractParamsFromWeigthMap.ts", "build/src/xception/../../../src/xception/TinyXception.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParams.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParamsFromWeigthMap.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/types.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/AgeGenderNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68Net.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/index.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/scaleLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/convLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParams.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/residualLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/FaceRecognitionNet.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/index.ts", "build/src/factories/../../../src/factories/WithFaceDescriptor.ts", "build/src/factories/../../../src/factories/WithAge.ts", "build/src/factories/../../../src/factories/WithGender.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParams.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "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", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParams.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParamsFromWeigthMap.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Options.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Base.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/index.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "build/src/globalApi/../../../src/globalApi/ComposableTask.ts", "build/src/globalApi/../../../src/globalApi/extractFacesAndComputeResults.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/const.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetector.ts", "build/src/globalApi/../../../src/globalApi/nets.ts", "build/src/globalApi/../../../src/globalApi/PredictFaceExpressionsTask.ts", "build/src/globalApi/../../../src/globalApi/PredictAgeAndGenderTask.ts", "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"],
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diff --git a/dist/face-api.js b/dist/face-api.js
new file mode 100644
index 0000000..e7dcbbf
--- /dev/null
+++ b/dist/face-api.js
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+`;return h[h.length-1]=" "+h[h.length-1]+"]"+(r?"":m),h}function Fp(e){const t=[];for(let s=0;s<e.length;s+=2)t.push([e[s],e[s+1]]);return t}class dn{constructor(e,t,s){if(this.dtype=t,this.shape=e.slice(),this.size=We(e),s!=null){const n=s.length;I(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=s||My(t,this.size),this.strides=oi(e)}set(e,...t){t.length===0&&(t=[0]),I(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);const s=this.locToIndex(t);this.values[s]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(const n of e){if(n<0||n>=this.shape[t]){const i=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(i)}t++}let s=e[e.length-1];for(let n=0;n<e.length-1;++n)s+=this.strides[n]*e[n];return this.values[s]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let s=0;s<e.length-1;++s)t+=this.strides[s]*e[s];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let s=0;s<t.length-1;++s)t[s]=Math.floor(e/this.strides[s]),e-=t[s]*this.strides[s];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return li().makeTensor(this.values,this.shape,this.dtype)}}let li=null,il=null,z_=null;function DS(e){li=e}function FS(e){il=e}function MS(e){z_=e}class me{constructor(e,t,s,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=We(e),this.strides=oi(e),this.dataId=s,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return il.buffer(this.shape,this.dtype,e)}bufferSync(){return il.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ko(this.shape,e)}arraySync(){return Ko(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=li().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(s=>Eh(s))}catch(s){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=li().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Eh(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 li().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;li().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return il.print(this,e)}clone(){return this.throwIfDisposed(),il.clone(this)}toString(e=!1){const t=this.dataSync();return _S(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),il.cast(this,e)}variable(e=!0,t,s){return this.throwIfDisposed(),li().makeVariable(this,e,t,s)}}Object.defineProperty(me,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class ci extends me{constructor(e,t,s,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=s}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(!Nt(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);li().disposeTensor(this),this.dataId=e.dataId,li().incRef(this,null)}dispose(){li().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(ci,Symbol.hasInstance,{value:e=>e instanceof me&&e.assign!=null&&e.assign instanceof Function});var By;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(By||(By={}));var jy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(jy||(jy={}));var Vy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Vy||(Vy={}));var Gy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Gy||(Gy={}));var qy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(qy||(qy={}));const P_={float32:Gy,int32:jy,bool:Vy,complex64:qy};function Ft(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return P_[e][t]}function Mp(e){return Ft(e,"int32")}const mn={};Ee(mn,{assertTypesMatch:()=>Hy,getTensorsInContainer:()=>Up,isTensorInList:()=>B_,makeTypesMatch:()=>Ce});function Ce(e,t){if(e.dtype===t.dtype)return[e,t];const s=Ft(e.dtype,t.dtype);return[e.cast(s),t.cast(s)]}function Hy(e,t){I(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function B_(e,t){return t.some(s=>s.id===e.id)}function Up(e){const t=[],s=new Set;return US(e,t,s),t}function US(e,t,s){if(e==null)return;if(e instanceof me){t.push(e);return}if(!j_(e))return;const n=e;for(const i in n){const r=n[i];s.has(r)||(s.add(r),US(r,t,s))}}function j_(e){return Array.isArray(e)||typeof e=="object"}class $S{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 $p{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new $S}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const s=e[t],n=await this.initializeBackend(s).success;if(n){await this.setBackend(s);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,s=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:s},!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:s}=this.initializeBackend(e),n=s?await t:t;if(!n)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new CS(this.backendInstance),!0}setupRegisteredKernels(){const e=Op(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Op(e);t.forEach(s=>{s.disposeFunc!=null&&s.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 s=t.factory();if(s&&!(s instanceof Eo)&&typeof s.then=="function"){const n=++this.pendingBackendInitId,i=s.then(r=>n<this.pendingBackendInitId?!1:(this.registry[e]=r,this.pendingBackendInit=null,!0)).catch(r=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(r.stack||r.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=s,{success:!0,asyncInit:!1}}catch(s){return console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 s=e[t],{success:n,asyncInit:i}=this.initializeBackend(s);if(i||n)return{name:s,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const s=this.state.tensorInfo.get(t),n=s.backend,i=this.readSync(t);n.disposeData(t),s.backend=e,e.move(t,i,s.shape,s.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let s=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");s=e}let n;return this.scopedRun(()=>this.startScope(s),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,s){e();try{const n=s();return t(),n}catch(n){throw t(),n}}nextTensorId(){return $p.nextTensorId++}nextVariableId(){return $p.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),s={x:e},n=r=>({x:()=>{const o="float32",a={x:r},l={dtype:o};return v.runKernelFunc(c=>c.cast(r,o),a,null,Ai,l)}}),i=[];return this.addTapeNode(this.state.activeScope.name,s,[t],n,i,{}),t}runKernel(e,t,s,n,i){const r=null,o=null;return this.runKernelFunc(r,t,o,e,s,n,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,s){const n=this.backend.numDataIds();let i=0;s.forEach(a=>{i+=a.dtype==="complex64"?3:1});const r=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=n-t-i-r;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e,t,s,n,i,r,o){let a,l=[];const c=this.isTapeOn();n==null&&(n=this.state.activeScope!=null?this.state.activeScope.name:"");const p=this.state.numBytes,u=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let h;const d=Rp(n,this.backendName);let m;if(d!=null)h=()=>{const g=this.backend.numDataIds();m=d.kernelFunc({inputs:t,attrs:i,backend:this.backend});const y=Array.isArray(m)?m:[m];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,g,y);const w=y.map(({dataId:x,shape:T,dtype:A})=>this.makeTensorFromDataId(x,T,A));if(c){let x=this.getTensorsForGradient(n,t,w);if(x==null){o==null&&(o=[]);const T=w.filter((A,_)=>o[_]);x=(r||[]).slice().concat(T)}l=this.saveTensorsForBackwardMode(x)}return w};else{const g=y=>{if(!c)return;l=y.map(w=>this.keep(this.clone(w)))};h=()=>{const y=this.backend.numDataIds();m=this.tidy(()=>e(this.backend,g));const w=Array.isArray(m)?m:[m];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,y,w),w}}let f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?a=h():(f=this.profiler.profileKernel(n,t,()=>h()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),a=f.outputs)}),c&&this.addTapeNode(n,t,a,s,l,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:n,bytesAdded:this.state.numBytes-p,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-u,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(g=>t[g]!=null?t[g].shape:null),outputShapes:a.map(g=>g.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(m)?a:a[0]}saveTensorsForBackwardMode(e){const t=e.map(s=>this.keep(this.clone(s)));return t}getTensorsForGradient(e,t,s){const n=Ch(e);if(n!=null){const i=n.inputsToSave||[],r=n.outputsToSave||[];let o;n.saveAllInputs?(I(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=i.map(l=>t[l]);const a=s.filter((l,c)=>r[c]);return o.concat(a)}return null}makeTensor(e,t,s,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");s=s||"float32",n=n||this.backend;let i=e;s==="string"&&Un(e[0])&&(i=e.map(a=>Rh(a)));const r=n.write(i,t,s),o=new me(t,s,r,this.nextTensorId());if(this.incRef(o,n),s==="string"){const a=this.state.tensorInfo.get(r),l=zy(i);this.state.numBytes+=l-a.bytes,a.bytes=l}return o}makeTensorFromDataId(e,t,s,n){s=s||"float32";const i=new me(t,s,e,this.nextTensorId());return this.incRef(i,n),i}makeVariable(e,t=!0,s,n){s=s||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));const i=new ci(e,t,s,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const s=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,s===0){this.state.numDataBuffers++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Wy(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.state.numBytes+=n}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof ci||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),s=t.refCount;s<=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,s=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-s;for(const n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,s,n,i,r){const o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:s,saved:i},a=Ch(e);a!=null&&(n=a.gradFunc),n!=null&&(o.gradient=l=>(l=l.map((c,p)=>{if(c==null){const u=s[p],h=ai(u.size,u.dtype);return this.makeTensor(h,u.shape,u.dtype)}return c}),n(l.length>1?l:l[0],i,r))),this.state.activeTape.push(o)}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=Up(e),s=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const r=this.state.activeScope.track[i];!r.kept&&!s.has(r.id)&&r.dispose()}const n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===n.id&&this.track(i)})}gradients(e,t,s,n=!1){if(I(t.length>0,()=>"gradients() received an empty list of xs."),s!=null&&s.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${s.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));I(i instanceof me,()=>"The result y returned by f() must be a tensor.");const r=RS(this.state.activeTape,t,i);if(!n&&r.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Please use tf.complex(real, imag).");if(!Dt(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){_p(t);const i=We(t),r=We(s);I(i===r,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${r}`);for(let o=0;o<s.length;++o){const a=s[o],l=o===s.length-1?a!==We(t.slice(o)):!0;I(s[o]===t[o]||!l,()=>`Error creating a new Tensor. 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Ct.getHandlers(e,"load",t)}static getHandlers(e,t,s){const n=[],i=t==="load"?Ct.getInstance().loadRouters:Ct.getInstance().saveRouters;return i.forEach(r=>{const o=r(e,s);o!==null&&n.push(o)}),n}}const sk=e=>Ct.registerSaveRouter(e),nk=e=>Ct.registerLoadRouter(e),ik=e=>Ct.getSaveHandlers(e),rk=(e,t)=>Ct.getLoadHandlers(e,t);const ol="://";class $n{constructor(){this.managers={}}static getInstance(){return $n.instance==null&&($n.instance=new $n),$n.instance}static registerManager(e,t){I(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(ol)&&(e=e.slice(0,e.indexOf(ol))),I(e.length>0,()=>"scheme must not be an empty string.");const s=$n.getInstance();I(s.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),s.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 Dh(e){if(e.indexOf(ol)===-1)throw new Error(`The url string provided does not contain a scheme. 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e==null?null:e.rank===0?O(e,[e.size]):e.rank===1?e:e.rank===2?O(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?O(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e}const Xs=S({batchNorm_:ED});function _D(e,t,s,n,i,r){const o=b(e,"x","batchNorm"),a=b(t,"mean","batchNorm"),l=b(s,"variance","batchNorm");let c;i!=null&&(c=b(i,"scale","batchNorm"));let p;return n!=null&&(p=b(n,"offset","batchNorm")),I(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),I(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),I(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&I(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),p!=null&&I(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),Xs(o,a,l,p,c,r)}const Yh=S({batchNorm2d_:_D});function kD(e,t,s,n,i,r){const o=b(e,"x","batchNorm"),a=b(t,"mean","batchNorm"),l=b(s,"variance","batchNorm");let c;i!=null&&(c=b(i,"scale","batchNorm"));let p;return n!=null&&(p=b(n,"offset","batchNorm")),I(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),I(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),I(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&I(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),p!=null&&I(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),Xs(o,a,l,p,c,r)}const Kh=S({batchNorm3d_:kD});function DD(e,t,s,n,i,r){const o=b(e,"x","batchNorm"),a=b(t,"mean","batchNorm"),l=b(s,"variance","batchNorm");let c;i!=null&&(c=b(i,"scale","batchNorm"));let p;return n!=null&&(p=b(n,"offset","batchNorm")),I(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),I(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),I(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&I(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),p!=null&&I(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Xs(o,a,l,p,c,r)}const Xh=S({batchNorm4d_:DD});function FD(e,t){let s=b(e,"broadcastTo","x");const n=s.shape;if(t.some(p=>!(p>0)||p%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<s.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${s.rank}.`);if(t.length>s.rank){const p=s.shape.slice();for(;p.length<t.length;)p.unshift(1);s=O(s,p)}const i=s.shape,r=Array.from(t);for(let p=t.length-1;p>=0;p--)if(i[p]===t[p])r[p]=1;else if(s.shape[p]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);const o=r.map((p,u)=>p>1?u:-1).filter(p=>p>=0);if(o.length===0)return Fs(s);const a=p=>p.tile(s,r),l={x:s},c={shape:t,inputShape:i};return v.runKernelFunc(a,l,null,Bc,c)}const ta=S({broadcastTo_:FD});function MD(e){const t=b(e,"x","ceil"),s={x:t};return v.runKernelFunc(n=>n.ceil(t),s,null,wr)}const Jh=S({ceil_:MD});function UD(e,t,s){const n=b(e,"x","clipByValue");I(t<=s,()=>`Error in clip: min (${t}) must be less than or equal to max (${s}).`);const i={x:n},r={clipValueMin:t,clipValueMax:s};return v.runKernelFunc((o,a)=>{const l=o.clip(n,t,s);return a([n]),l},i,null,xr,r)}const xt=S({clipByValue_:UD});function $D(e){return be(e,0)}const Zh=S({concat1d_:$D});function WD(e,t){return be(e,t)}const Qh=S({concat2d_:WD});function zD(e,t){return be(e,t)}const ed=S({concat3d_:zD});function PD(e,t){return be(e,t)}const td=S({concat4d_:PD});function BD(e,t,s,n,i="NHWC",r=[1,1],o){const a=b(e,"x","conv2d"),l=b(t,"filter","conv2d");let c=a,p=!1;a.rank===3&&(p=!0,c=O(a,[1,a.shape[0],a.shape[1],a.shape[2]])),I(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),I(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&I(De(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);const u=i==="NHWC"?c.shape[3]:c.shape[1];I(u===l.shape[2],()=>`Error in conv2d: depth of input (${u}) must match input depth for filter ${l.shape[2]}.`),I(tt(s,r),()=>`Error in conv2D: Either strides or dilations must be 1. 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Got strides ${s} and dilations '${r}'`),I(i==="NDHWC",()=>`Error in conv3d: got dataFormat of ${i} but only NDHWC is currently supported.`);const p=(m,f)=>{const g=Hr(l.shape,a.shape,s,r,n),y=m.conv3d(l,a,g);return f([l,a]),y},u={x:l,filter:a},h={strides:s,pad:n,dataFormat:i,dilations:r},d=v.runKernelFunc(p,u,null,qc,h);return c?O(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}const bl=S({conv3d_:qD});function HD(e,t,s,n,i){I(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let r=e,o=t,a=!1;t.rank===4&&(a=!0,o=O(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),r=[1,e[0],e[1],e[2],e[3]]);const l=r[4],c=o.shape[4];I(r.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${r.length}.`),I(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),I(s.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${s.rank}`),I(l===s.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${s.shape[3]}.`),I(c===s.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${s.shape[4]}.`);const p=m=>{const f=1,g=Hr(r,s.shape,n,f,i);return m.conv3dDerInput(o,s,g)},u={dy:o},h={pad:i},d=v.runKernelFunc(p,u,null,oy,h);return a?O(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}const sd=S({conv3DBackpropInput_:HD});function YD(e,t,s,n,i){const r=b(e,"x","conv3dTranspose"),o=b(t,"filter","conv3dTranspose");return sd(s,r,o,n,i)}const fI=S({conv3dTranspose_:YD});function KD(e){const t=b(e,"x","cos"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.cos(t);return i([t]),r},s,null,Qn)}const Kr=S({cos_:KD});function XD(e){const t=b(e,"x","cosh"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.cosh(t);return i([t]),r},s,null,Lr)}const wl=S({cosh_:XD});function JD(e,t=0,s=!1,n=!1){const i=b(e,"x","cumsum"),r=(l,c)=>{const p=dt([t],i.rank);let u=i;p!=null&&(u=se(i,p));const h=qt(1,i.rank)[0];let d=l.cumsum(u,h,s,n);if(c([i]),p!=null){const m=Jo(p);d=se(d,m)}return d},o={x:i},a={axis:t,exclusive:s,reverse:n};return v.runKernelFunc(r,o,null,Hc,a)}const xl=S({cumsum_:JD});function ZD(e,t,s="NHWC"){const n=b(e,"x","depthToSpace"),i=s==="NHWC"?n.shape[1]:n.shape[2],r=s==="NHWC"?n.shape[2]:n.shape[3],o=s==="NHWC"?n.shape[3]:n.shape[1];I(i*t>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${i} and ${t}  for depthToSpace with input shape
+    ${n.shape}`),I(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${r} and ${t} for depthToSpace with input shape
+        ${n.shape}`),I(o%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${n.shape}`);const a=p=>p.depthToSpace(n,t,s),l={x:n},c={blockSize:t,dataFormat:s};return v.runKernelFunc(a,l,null,ly,c)}const nd=S({depthToSpace_:ZD});function QD(e,t,s,n,i="NHWC",r=[1,1],o){const a=b(e,"x","depthwiseConv2d"),l=b(t,"filter","depthwiseConv2d");let c=a,p=!1;a.rank===3&&(p=!0,c=O(a,[1,a.shape[0],a.shape[1],a.shape[2]])),I(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),I(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),I(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&I(De(n),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);const u=(f,g)=>{r==null&&(r=[1,1]),I(tt(s,r),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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p={x:l,filter:a},u={strides:s,pad:n,dilations:i},h=v.runKernel(Mo,p,u);return c?O(h,[h.shape[1],h.shape[2],h.shape[3]]):h}const id=S({dilation2d_:tF});function sF(e,t){const s=e.length,n=[];for(let i=0;i<s;i++){const r=s-1-i,o=e[r]||1,a=t[t.length-1-i]||1;a>1&&o===1&&n.unshift(r)}return n}function Ke(e,t){const s=[];for(let n=0;n<t.length;n++){const i=e[e.length-n-1],r=t.length-n-1,o=t[r];(i==null||i===1&&o>1)&&s.unshift(r)}return s}function Ie(e,t){const s=[],n=Math.max(e.length,t.length);for(let i=0;i<n;i++){let r=e[e.length-i-1];r==null&&(r=1);let o=t[t.length-i-1];if(o==null&&(o=1),r===1)s.unshift(o);else if(o===1)s.unshift(r);else if(r!==o){const a=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(a)}else s.unshift(r)}return s}function nF(e,t){let s=b(e,"a","equal"),n=b(t,"b","equal");[s,n]=Ce(s,n),Ie(s.shape,n.shape);const i=o=>o.equal(s,n),r={a:s,b:n};return v.runKernelFunc(i,r,null,dy)}const as=S({equal_:nF});function iF(e,t,s){const 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o=i.selu(t);return r([t]),o},n={x:t};return v.runKernelFunc(s,n,null,Mr)}const Rl=S({selu_:MM});function UM(e,t,s,n,i,r=[1,1],o="NHWC"){const a=b(e,"x","separableConv2d"),l=b(t,"depthwiseFilter","separableConv2d"),c=b(s,"pointwiseFilter","separableConv2d");let p=a,u=!1;if(a.rank===3&&(u=!0,p=O(a,[1,a.shape[0],a.shape[1],a.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");I(p.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${p.rank}.`),I(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),I(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),I(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${c.shape[0]}.`),I(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got 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v.runKernelFunc(n=>n.sign(t),s,null,$r)}const Ld=S({sign_:WM});function zM(e){const t=b(e,"x","sin"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.sin(t);return i([t]),r},s,null,ti)}const Ol=S({sin_:zM});function PM(e){const t=b(e,"x","sinh"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.sinh(t);return i([t]),r},s,null,Ur)}const El=S({sinh_:PM});function BM(e,t,s){const n=b(e,"x","slice1d");return I(n.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${n.rank} tensor`),he(n,[t],[s])}const _l=S({slice1d_:BM});function jM(e,t,s){const n=b(e,"x","slice2d");return I(n.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${n.rank} tensor`),he(n,t,s)}const Hp=S({slice2d_:jM});function VM(e,t,s){const n=b(e,"x","slice3d");return I(n.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${n.rank} tensor`),he(n,t,s)}const to=S({slice3d_:VM});function GM(e,t,s){const n=b(e,"x","slice4d");return I(n.rank===4,()=>`slice4d expects a rank-4 tensor, but got a 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Logits was rank ${s.rank} and dim was ${t}`);const n={logits:s},i={dim:t};return v.runKernelFunc((r,o)=>{const a=r.softmax(s,t);return o([a]),a},n,null,Ip,i)}const ts=S({softmax_:qM});function HM(e){I(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);const t={input:e};return v.runKernelFunc(s=>{const n=e.shape[e.shape.length-1],i=e.size/n,r=e.as2D(i,n),o=s.fft(r);return o.reshape(e.shape)},t,null,Kc)}const so=S({fft_:HM});function YM(e){I(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);const t={input:e};return v.runKernelFunc(s=>{const n=e.shape[e.shape.length-1],i=e.size/n,r=O(e,[i,n]),o=s.ifft(r);return O(o,e.shape)},t,null,Qc)}const Wi=S({ifft_:YM});function KM(e){const t=e.shape[e.shape.length-1],s=e.size/t;let n;if(t<=2){const i=O(e,[s,t]);n=Wi(i)}else{const i=[s,2*(t-1)],r=O(Zs(e),[s,t]),o=O(yn(e),[s,t]),a=Et(he(r,[0,1],[s,t-2]),1),l=R(Et(he(o,[0,1],[s,t-2]),1),j(-1)),c=be([r,a],1),p=be([o,l],1),u=O(Gt(c,p),[i[0],i[1]]);n=Wi(u)}if(n=Zs(n),e.rank===3&&e.shape[0]!==0){const i=n,r=e.shape[0];n=O(n,[r,n.shape[0]/r,n.shape[1]]),i.dispose()}return n}const kl=S({irfft_:KM});function Sb(e,t,s=0){let n=[];if(typeof t=="number")I(e.shape[s]%t===0,()=>"Number of splits must evenly divide the axis."),n=new Array(t).fill(e.shape[s]/t);else{const i=t.reduce((o,a)=>(a===-1&&(o+=1),o),0);I(i<=1,()=>"There should be only one negative value in split array.");const r=t.indexOf(-1);if(r!==-1){const o=t.reduce((a,l)=>l>0?a+l:a);t[r]=e.shape[s]-o}I(e.shape[s]===t.reduce((o,a)=>o+a),()=>"The sum of sizes must match the size of the axis dimension."),n=t}return n}function XM(e,t,s=0){const n=b(e,"x","split"),i=(a,l)=>{const c=Ne(s,n.shape)[0],p=Sb(n,t,c);return a.split(n,p,c)},r={x:n},o={numOrSizeSplits:t,axis:s};return v.runKernelFunc(i,r,null,Sp,o)}const Bt=S({split_:XM});function JM(e,t){I(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let s=e.shape[e.shape.length-1];const n=e.size/s;let i;if(t!=null&&t<s){const m=e.shape.map(g=>0),f=e.shape.map(g=>g);f[e.shape.length-1]=t,i=he(e,m,f),s=t}else if(t!=null&&t>s){const m=e.shape.map(f=>f);m[e.shape.length-1]=t-s,i=be([e,ye(m)],e.shape.length-1),s=t}else i=e;const r=re(i),o=O(Gt(i,r),[n,s]),a=so(o),l=Math.floor(s/2)+1,c=Zs(a),p=yn(a),u=Bt(c,[l,s-l],c.shape.length-1),h=Bt(p,[l,s-l],p.shape.length-1),d=i.shape.slice();return d[i.shape.length-1]=l,O(Gt(u[0],h[0]),d)}const no=S({rfft_:JM});function ZM(e){const t=b(e,"x","sqrt"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.sqrt(t);return i([t]),r},s,null,Pr)}const Je=S({sqrt_:ZM});function QM(e,t){let s=b(e,"a","squaredDifference"),n=b(t,"b","squaredDifference");[s,n]=Ce(s,n),Ie(s.shape,n.shape);const i=(a,l)=>{const c=a.squaredDifference(s,n);return l([s,n]),c},r={a:s,b:n},o={};return v.runKernelFunc(i,r,null,si,o)}const io=S({squaredDifference_:QM});function eU(e,t){const s=b(e,"x","squeeze");return O(s,Fy(s.shape,t).newShape)}const Qs=S({squeeze_:eU});function tU(e,t=0){const s=Di(e,"tensors","stack");if(I(s.length>=1,()=>"Pass at least one tensor to tf.stack"),s.length===1)return Mt(s[0],t);const n=s[0].rank,i=s[0].shape,r=s[0].dtype;I(t<=n,()=>"Axis must be <= rank of the tensor"),s.forEach(a=>{Se(i,a.shape,"All tensors passed to stack must have matching shapes"),I(r===a.dtype,()=>"All tensors passed to stack must have matching dtypes")});const o=s.map(a=>Mt(a,t));return be(o,t)}const Ge=S({stack_:tU});function sU(e,t=0){const s=b(e,"x","step"),n={x:s},i={alpha:t};return v.runKernelFunc(r=>r.step(s,t),n,null,Vr,i)}const mi=S({step_:sU});function nU(e,t,s,n,i=0,r=0,o=0,a=0,l=0){let c=b(e,"x","stridedSlice");const p=d=>{n==null&&(n=new Array(t.length));const m=Bp(o);if(m.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&a!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");const f=c.rank-t.length,g=Bp(a),y=c.shape.slice();g.forEach(M=>{t[M]=0,s[M]=1,y.splice(M,0,1)}),c=O(c,y);const{begin:w,end:x,strides:T}=lb(c.shape,m,f,t,s,n,i,r,o);t=w,s=x,n=T;const A=Bp(l);A.forEach(M=>{s[M]=t[M]+1,n[M]=1});const _=ab(t,s,n),E=_.filter((M,P)=>A.indexOf(P)===-1),F=n.every(M=>M===1);if(F)return O(he(c,t,_),E);const D=d.stridedSlice(c,t,s,n);return O(D,E)},u={x:c},h={begin:t,end:s,strides:n,beginMask:i,endMask:r,ellipsisMask:o,newAxisMask:a,shrinkAxisMask:l};return v.runKernelFunc(p,u,null,Oy,h)}const Sd=S({stridedSlice_:nU});function iU(e){const t=b(e,"x","tan"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.tan(t);return i([t]),r},s,null,ni)}const Id=S({tan_:iU});function ls(e,t,s){if(Ys(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");const n=Qt(e,s);if(n.length!==2&&n.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return hs(e,t,n,s)}function ss(e,t,s){if(Ys(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");const n=Qt(e,s);if(n.length!==4&&n.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return hs(e,t,n,s)}function WI(e,t,s){if(Ys(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");const n=Qt(e,s);if(n.length!==5&&n.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a 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a=0;a<o.values.length;a++)o.values[a]=r.nextValue();return o.toTensor()}const ro=S({truncatedNormal_:oU});function aU(e,t=0){const s=b(e,"x","unique",null);I(s.rank>0,()=>"The input tensor must be at least 1D");const n={x:s},i={axis:t},[r,o]=v.runKernel(qo,n,i);return{values:r,indices:o}}const Yp=S({unique_:aU});function lU(e,t,s){const n=b(e,"x","unsortedSegmentSum"),i=b(t,"segmentIds","unsortedSegmentSum","int32");I(De(s),()=>"numSegments must be of dtype int");const r={x:n,segmentIds:i},o={numSegments:s},a=(l,c)=>{const p=l.unsortedSegmentSum(n,i,s);return c([i]),p};return v.runKernelFunc(a,r,null,Ap,o)}const Td=S({unsortedSegmentSum_:lU});function cU(e,t=0){const s=b(e,"x","unstack");I(t>=-s.shape.length&&t<s.shape.length,()=>`Axis = ${t} is not in [-${s.shape.length}, ${s.shape.length})`),t<0&&(t+=s.shape.length);const n={value:s},i={axis:t},r=o=>o.unstack(s,t);return v.runKernelFunc(r,n,null,Tp,i)}const qe=S({unstack_:cU});function Ad(e,t=!0,s,n){return v.makeVariable(e,t,s,n)}function Nd(e,t){const s=[];for(let r=0;r<t.length;r++)t[r]&&s.push(r);const n=ge(e,"int32"),i=ge([s.length,e.length],"int32");for(let r=0;r<s.length;r++){const o=n.indexToLoc(s[r]),a=r*e.length;i.values.set(o,a)}return i.toTensor()}async function pU(e){const t=b(e,"condition","whereAsync","bool"),s=await t.data(),n=Nd(t.shape,s);return e!==t&&t.dispose(),n}const Dl=pU;async function uU(e,t,s){const n=b(e,"tensor","boolMask"),i=b(t,"mask","boolMask","bool"),r=s??0,o=i.rank,a=n.shape;I(o>0,()=>"mask cannot be scalar"),Se(a.slice(r,r+o),i.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=r;f<r+o;f++)l*=a[f];const c=a.slice(0,r).concat([l],a.slice(r+o)),p=O(n,c),u=O(i,[-1]),h=await Dl(u),d=Qs(h,[1]),m=hi(p,d,r);return e!==n&&n.dispose(),t!==i&&i.dispose(),d.dispose(),p.dispose(),u.dispose(),h.dispose(),m}const hU=uU;function dU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","notEqualStrict"),n=b(t,"b","notEqualStrict");return Se(s.shape,n.shape,"Error in notEqualStrict: "),Js(s,n)}function mU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","lessStrict"),n=b(t,"b","lessStrict");return Se(s.shape,n.shape,"Error in lessStrict: "),Xr(s,n)}function fU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","equalStrict"),n=b(t,"b","equalStrict");return Se(s.shape,n.shape,"Error in equalStrict: "),as(s,n)}function gU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","lessEqualStrict"),n=b(t,"b","lessEqualStrict");return Se(s.shape,n.shape,"Error in lessEqualStrict: "),Ws(s,n)}function yU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","greaterStrict"),n=b(t,"b","greaterStrict");return Se(s.shape,n.shape,"Error in greaterStrict: "),Ut(s,n)}function bU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","greaterEqualStrict"),n=b(t,"b","greaterEqualStrict");return Se(s.shape,n.shape,"Error in greaterEqualStrict: "),ms(s,n)}const wU=S({equalStrict_:fU}),xU=S({greaterEqualStrict_:bU}),LU=S({greaterStrict_:yU}),SU=S({lessEqualStrict_:gU}),IU=S({lessStrict_:mU}),vU=S({notEqualStrict_:dU});function TU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","addStrict"),n=b(t,"b","addStrict");return Se(s.shape,n.shape,"Error in addStrict: "),$(s,n)}function AU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","subStrict"),n=b(t,"b","subStrict");return Se(s.shape,n.shape,"Error in subStrict: "),X(s,n)}function NU(e,t){return It("strict variants of ops have been deprecated and will be removed in future"),Se(e.shape,t.shape,"Error in powStrict: "),es(e,t)}function CU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","mul"),n=b(t,"b","mul");return Se(s.shape,n.shape,"Error in multiplyStrict: "),R(s,n)}function RU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","div"),n=b(t,"b","div");return Se(s.shape,n.shape,"Error in divideStrict: "),Z(s,n)}function OU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","modStrict"),n=b(t,"b","modStrict");return Se(s.shape,n.shape,"Error in modStrict: "),Al(s,n)}function EU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","minimumStrict"),n=b(t,"b","minimumStrict");return Se(s.shape,n.shape,"Error in minimumStrict: "),bn(s,n)}function _U(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","maximumStrict"),n=b(t,"b","maximumStrict");return Se(s.shape,n.shape,"Error in maximumStrict: "),Ht(s,n)}function kU(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","squaredDifferenceStrict"),n=b(t,"b","squaredDifferenceStrict");return Se(s.shape,n.shape,"Error in squaredDifferenceStrict: "),io(s,n)}const DU=S({addStrict_:TU}),FU=S({divStrict_:RU}),MU=S({maximumStrict_:_U}),UU=S({minimumStrict_:EU}),$U=S({modStrict_:OU}),WU=S({mulStrict_:CU}),zU=S({powStrict_:NU}),PU=S({squaredDifferenceStrict_:kU}),BU=S({subStrict_:AU});function jU(e,t="euclidean",s=null,n=!1){e=b(e,"x","norm");const i=PI(e,t,s);let r=i.shape;if(n){const o=Ne(s,e.shape);r=wt(i.shape,o)}return O(i,r)}function PI(e,t,s=null){if(e.rank===0)return et(e);if(e.rank!==1&&s===null)return PI(O(e,[-1]),t,s);if(e.rank===1||typeof s=="number"||Array.isArray(s)&&s.length===1){if(t===1)return te(et(e),s);if(t===Infinity)return Lt(et(e),s);if(t===-Infinity)return Ui(et(e),s);if(t==="euclidean"||t===2)return Je(te(es(et(e),j(2,"int32")),s));throw new Error(`Error in norm: 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l=U$(r,o);return ns(l,a,i)}const fv=S({sigmoidCrossEntropy_:$$});function W$(e,t,s=-1){if(s===-1&&(s=t.rank-1),s!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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P$={fft:so,ifft:Wi,rfft:no,irfft:kl},B$={hammingWindow:VI,hannWindow:Od,frame:Ed,stft:GI},en={flipLeftRight:HI,resizeNearestNeighbor:iv,resizeBilinear:nv,rotateWithOffset:YI,cropAndResize:qI,nonMaxSuppression:KI,nonMaxSuppressionAsync:ZI,nonMaxSuppressionWithScore:QI,nonMaxSuppressionWithScoreAsync:ev,nonMaxSuppressionPadded:tv,nonMaxSuppressionPaddedAsync:sv},Cb={bandPart:rv,gramSchmidt:ov,qr:lv},j$={absoluteDifference:cv,computeWeightedLoss:ns,cosineDistance:pv,hingeLoss:uv,huberLoss:hv,logLoss:dv,meanSquaredError:mv,sigmoidCrossEntropy:fv,softmaxCrossEntropy:gv};class gs extends cb{minimize(e,t=!1,s){const{value:n,grads:i}=this.computeGradients(e,s);if(s!=null){const r=s.map(o=>({name:o.name,tensor:i[o.name]}));this.applyGradients(r)}else this.applyGradients(i);return ce(i),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return hd(e,t)}dispose(){this.iterations_!=null&&ce(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:j(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(gs,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class ca extends gs{constructor(e,t,s=null){super();this.learningRate=e,this.rho=t,this.epsilon=s,this.accumulatedGrads=[],this.accumulatedUpdates=[],s==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${s}/accum_grad`,variable:C(()=>re(i).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${s}/accum_var`,variable:C(()=>re(i).variable(r))});const o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;const a=this.accumulatedGrads[n].variable,l=this.accumulatedUpdates[n].variable;C(()=>{const c=$(R(a,this.rho),R(xe(o),1-this.rho)),p=R(Z(Je($(l,this.epsilon)),Je($(a,this.epsilon))),o),u=$(R(l,this.rho),R(xe(p),1-this.rho));a.assign(c),l.assign(u);const h=$(R(p,-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ce(this.accumulatedGrads.map(e=>e.variable)),ce(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,s=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}ca.className="Adadelta";As(ca);class pa extends gs{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s];if(this.accumulatedGrads[n]==null){const a=!1;this.accumulatedGrads[n]={originalName:`${s}/accumulator`,variable:C(()=>Wt(i.shape,this.initialAccumulatorValue).variable(a))}}const r=Array.isArray(e)?e[n].tensor:e[s];if(r==null)return;const o=this.accumulatedGrads[n].variable;C(()=>{const a=$(o,xe(r));o.assign(a);const l=$(R(Z(r,Je($(a,v.backend.epsilon()))),-this.learningRate),i);i.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ce(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(s=>({originalName:s.name,variable:s.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}pa.className="Adagrad";As(pa);class ua extends gs{constructor(e,t,s,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=s,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],C(()=>{this.accBeta1=j(t).variable(),this.accBeta2=j(s).variable()}),n==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);C(()=>{const s=X(1,this.accBeta1),n=X(1,this.accBeta2);t.forEach((i,r)=>{const o=v.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:C(()=>re(o).variable(a))}),this.accumulatedSecondMoment[r]==null&&(this.accumulatedSecondMoment[r]={originalName:`${i}/v`,variable:C(()=>re(o).variable(a))});const l=Array.isArray(e)?e[r].tensor:e[i];if(l==null)return;const c=this.accumulatedFirstMoment[r].variable,p=this.accumulatedSecondMoment[r].variable,u=$(R(c,this.beta1),R(l,1-this.beta1)),h=$(R(p,this.beta2),R(xe(l),1-this.beta2)),d=Z(u,s),m=Z(h,n);c.assign(u),p.assign(h);const f=$(R(Z(d,$(Je(m),this.epsilon)),-this.learningRate),o);o.assign(f)}),this.accBeta1.assign(R(this.accBeta1,this.beta1)),this.accBeta2.assign(R(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ce(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ce(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),C(()=>{this.accBeta1.assign(es(this.beta1,this.iterations_+1)),this.accBeta2.assign(es(this.beta2,this.iterations_+1))});const t=e.length/2,s=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)}))}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)}}ua.className="Adam";As(ua);class ha extends gs{constructor(e,t,s,n=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=s,this.epsilon=n,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],C(()=>{this.iteration=j(0).variable(),this.accBeta1=j(t).variable()}),n==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);C(()=>{const s=X(1,this.accBeta1),n=Z(-this.learningRate,$(R(this.iteration,this.decay),1));t.forEach((i,r)=>{const o=v.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:re(o).variable(a)}),this.accumulatedWeightedInfNorm[r]==null&&(this.accumulatedWeightedInfNorm[r]={originalName:`${i}/v`,variable:re(o).variable(a)});const l=Array.isArray(e)?e[r].tensor:e[i];if(l==null)return;const c=this.accumulatedFirstMoment[r].variable,p=this.accumulatedWeightedInfNorm[r].variable,u=$(R(c,this.beta1),R(l,1-this.beta1)),h=R(p,this.beta2),d=et(l),m=Ht(h,d);c.assign(u),p.assign(m);const f=$(R(Z(n,s),Z(u,$(m,this.epsilon))),o);o.assign(f)}),this.iteration.assign($(this.iteration,1)),this.accBeta1.assign(R(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ce(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ce(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}ha.className="Adamax";As(ha);class zi extends gs{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=Array.isArray(e)?e[n].tensor:e[s];if(i==null)return;const r=v.registeredVariables[s];C(()=>{const o=$(R(this.c,i),r);r.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=pt(j(-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)}}zi.className="SGD";As(zi);class da extends zi{constructor(e,t,s=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=s,this.accumulations=[],this.m=j(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s];if(this.accumulations[n]==null){const a=!1;this.accumulations[n]={originalName:`${s}/momentum`,variable:C(()=>re(i).variable(a))}}const r=this.accumulations[n].variable,o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;C(()=>{let a;const l=$(R(this.m,r),o);this.useNesterov?a=$(R(this.c,$(o,R(l,this.m))),i):a=$(R(this.c,l),i),r.assign(l),i.assign(a)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ce(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(s=>({originalName:s.name,variable:s.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}da.className="Momentum";As(da);class ma extends gs{constructor(e,t=.9,s=0,n=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=s,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,n==null&&(this.epsilon=v.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${s}/rms`,variable:C(()=>re(i).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${s}/momentum`,variable:C(()=>re(i).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${s}/mg`,variable:C(()=>re(i).variable(r))});const o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;const a=this.accumulatedMeanSquares[n].variable,l=this.accumulatedMoments[n].variable;C(()=>{const c=$(R(a,this.decay),R(xe(o),1-this.decay));if(this.centered){const p=this.accumulatedMeanGrads[n].variable,u=$(R(p,this.decay),R(o,1-this.decay)),h=Z(R(o,this.learningRate),Je(X(c,$(xe(u),this.epsilon)))),d=$(R(l,this.momentum),h);a.assign(c),p.assign(u),l.assign(d);const m=X(i,d);i.assign(m)}else{const p=$(R(a,this.decay),R(xe(o),1-this.decay)),u=$(R(l,this.momentum),Z(R(o,this.learningRate),Je($(p,this.epsilon))));a.assign(p),l.assign(u);const h=X(i,u);i.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ce(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ce(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ce(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,s=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}ma.className="RMSProp";As(ma);class oo{static sgd(e){return new zi(e)}static momentum(e,t,s=!1){return new da(e,t,s)}static rmsprop(e,t=.9,s=0,n=null,i=!1){return new ma(e,t,s,n,i)}static adam(e=.001,t=.9,s=.999,n=null){return new ua(e,t,s,n)}static adadelta(e=.001,t=.95,s=null){return new ca(e,t,s)}static adamax(e=.002,t=.9,s=.999,n=null,i=0){return new ha(e,t,s,n,i)}static adagrad(e,t=.1){return new pa(e,t)}}da,zi,ca,pa,ma,ha,ua;const ao={sgd:oo.sgd,momentum:oo.momentum,adadelta:oo.adadelta,adagrad:oo.adagrad,rmsprop:oo.rmsprop,adamax:oo.adamax,adam:oo.adam};const V$=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Zp(){return new Promise(e=>V$(()=>e()))}function G$(e,t,s){const n=s*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[n,i]}function q$(e,t,s,n=!0){let i=[];if(n)i=i.concat(t.slice(0)),i.push(e[0]/s),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const r=t.length;for(let o=0;o<r;++o)i=i.concat([e[o+1]/t[o],t[o]]);i=i.concat(e.slice(r+1))}return i}function H$(e,t,s=!0){const n=[];if(s){n.push(t);for(let i=t+1;i<e;++i)i<=2*t?(n.push(i),n.push(i-(t+1))):n.push(i)}else{const i=[],r=[];for(let o=1;o<e;++o)o>=t*2+1||o%2===1?r.push(o):i.push(o);n.push(...i),n.push(0),n.push(...r)}return n}function Y$(e,t,s,n=!0){const i=[];n?i.push(e[0]/s):i.push(e[0]*s);for(let 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bT={kernelName:sp,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,s)=>{const[n,i]=t,{depthRadius:r,bias:o,alpha:a,beta:l}=s;return{x:()=>yT(n,i,e,r,o,a,l)}}};function Fd(e,t,s,n,i){return t.rank<s.rank&&(t=O(t,wt(t.shape,n))),e.rank<s.rank&&(e=O(e,wt(e.shape,n))),{x:()=>{const r=R(e,G(as(s,t),e.dtype));return i==null?r:se(r,i)}}}const Eb={kernelName:Ri,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,s)=>{const n=s,{reductionIndices:i}=n,[r,o]=t,a=Ne(i,r.shape),l=dt(a,r.rank),c=Fd(e,o,r,a,l);return{x:()=>{let p=c.x();return l!=null&&(p=se(p)),p}}}};const wT={kernelName:np,inputsToSave:["a","b"],gradFunc:(e,t)=>{const[s,n]=t,i=()=>R(e,G(ms(s,n),"float32")),r=()=>R(e,G(Xr(s,n),"float32"));return{a:i,b:r}}};function xW(e,t,s,n,i,r=[1,1,1],o,a){const l=b(e,"dy","maxPool3dBackprop"),c=b(t,"input","maxPool3dBackprop"),p=b(s,"output","maxPool3dBackprop");let 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Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,_W(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,kW(this.distribution),this.seed=e.seed}apply(e,t){const s=DW(e),n=s[0],i=s[1];let r=this.scale;if(this.mode==="fanIn"?r/=Math.max(1,n):this.mode==="fanOut"?r/=Math.max(1,i):r/=Math.max(1,(n+i)/2),this.distribution==="normal"){const o=Math.sqrt(r);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ae(`${this.getClassName()} does not support dType ${t}.`);return ro(e,0,o,t,this.seed)}else{const o=Math.sqrt(3*r);return wn(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}ws.className="VarianceScaling";V.registerClass(ws);class nu extends ws{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ws.className}}nu.className="GlorotUniform";V.registerClass(nu);class iu extends ws{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ws.className}}iu.className="GlorotNormal";V.registerClass(iu);class ru extends ws{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ws.className}}ru.className="HeNormal";V.registerClass(ru);class ou extends ws{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ws.className}}ou.className="HeUniform";V.registerClass(ou);class au extends ws{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ws.className}}au.className="LeCunNormal";V.registerClass(au);class lu extends ws{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ws.className}}lu.className="LeCunNormal";V.registerClass(lu);class Qd extends zs{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ae("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return C(()=>{if(e.length<2)throw new ae("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const s=e[0]>e[1]?[e[1],e[0]]:e,n=Wl(s,0,1,"float32");let i=Cb.gramSchmidt(n);return e[0]>e[1]&&(i=i.transpose()),R(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}Qd.className="Orthogonal";V.registerClass(Qd);const EA={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function _A(e,t={}){return fi(e,V.SerializationMap.getMap().classNameMap,t,"initializer")}function Ze(e){return Ml(e)}function je(e){if(typeof e=="string"){const t=e in EA?EA[e]:e;if(t==="GlorotNormal")return new iu;if(t==="GlorotUniform")return new nu;if(t==="HeNormal")return new ru;if(t==="HeUniform")return new ou;if(t==="LeCunNormal")return new au;if(t==="LeCunUniform")return new lu;{const s={};return s.className=t,s.config={},_A(s)}}else return e instanceof zs?e:_A(e)}const Gb={};Ee(Gb,{constant:()=>UW,glorotNormal:()=>VW,glorotUniform:()=>jW,heNormal:()=>GW,heUniform:()=>qW,identity:()=>PW,leCunNormal:()=>HW,leCunUniform:()=>YW,ones:()=>MW,orthogonal:()=>KW,randomNormal:()=>WW,randomUniform:()=>$W,truncatedNormal:()=>zW,varianceScaling:()=>BW,zeros:()=>FW});function FW(){return new Hd}function MW(){return new zl}function UW(e){return new Yd(e)}function $W(e){return new Kd(e)}function WW(e){return new Xd(e)}function zW(e){return new Jd(e)}function PW(e){return new Zd(e)}function BW(e){return new ws(e)}function jW(e){return new nu(e)}function VW(e){return new iu(e)}function GW(e){return new ru(e)}function qW(e){return new ou(e)}function HW(e){return new au(e)}function YW(e){return new lu(e)}function KW(e){return new Qd(e)}let XW=0;function em(){return XW++}const tm={};function co(e=""){return e in tm||(tm[e]=0),tm[e]+=1,e+tm[e].toString()}function sm(e){return Array.isArray(e)&&Array.isArray(e[0])}function Pl(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function we(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new k(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function Ue(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new k(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Bl(e){let t=0;for(const s of e)s.shape.length===0?t+=1:t+=s.shape.reduce((n,i)=>n*i);return t}const kA="Variable";class nm{constructor(e,t="float32",s=kA,n=!0,i=null){this.dtype=t??"float32",this.shape=e.shape,this.id=em(),s=s??kA,this.originalName=Bd(s),this.name=jd(this.originalName),this.trainable_=n,this.constraint=i,this.val=Ad(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),JW(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function JW(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function cu(e){return e.map(t=>t.read())}function jl(e){e.forEach(t=>{const s=t[0];s.write(t[1])})}class st{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class xs{constructor(e,t,s,n,i,r,o){this.dtype=e,this.shape=t,this.sourceLayer=s,this.inputs=n,this.callArgs=i,this.outputTensorIndex=o,this.id=em(),r!=null&&(this.originalName=Bd(r),this.name=jd(this.originalName)),this.rank=t.length}}let ZW=0;class ya{constructor(e,t){this.callArgs=t,this.id=ZW++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const s of e.inboundLayers)s!=null&&s.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let QW=0;class Le extends V.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=QW++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const s=this.getClassName();t=vn(s)+"_"+co(s)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let s;if(e.batchInputShape!=null)s=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),s=[i].concat(e.inputShape)}this.batchInputShape=s;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Ns(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new k(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return jt(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return jt(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Sn(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new Sn(`Layer ${this.name} is not connected, no input to return.`);return jt(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Sn(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Sn(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return jt(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=He(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=He(this.inputSpec);if(e.length!==t.length)throw new k(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let s=0;s<e.length;s++){const n=e[s],i=t[s];if(i==null)continue;const r=n.rank;if(i.ndim!=null&&r!==i.ndim)throw new k(`Input ${s} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${r}`);if(i.maxNDim!=null&&r>i.maxNDim)throw new k(`Input ${s} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${r}`);if(i.minNDim!=null&&r<i.minNDim)throw new k(`Input ${s} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${r}.`);if(i.dtype!=null&&n.dtype!==i.dtype)throw new k(`Input ${s} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${n.dtype}.`);if(i.axes){const o=n.shape;for(const a in i.axes){const l=Number(a),c=i.axes[a],p=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(p)===-1)throw new k(`Input ${s} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(i.shape!=null)for(let o=0;o<i.shape.length;++o){const a=i.shape[o],l=n.shape[o];if(a!=null&&l!=null&&a!==l)throw new k(`Input ${s} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const s=He(e);let n=!0;for(const r of s)if(!(r instanceof xs)){n=!1;break}let i=!0;for(const r of s)if(r instanceof xs){i=!1;break}if(n===i)throw new k("Arguments to apply() must be all SymbolicTensors or all Tensors");return zn(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const r=[];for(const o of He(e))r.push(o.shape);this.build(jt(r)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let r=this.call(e,t);const o=He(r),a=[];for(let l of o)s.indexOf(l)!==-1&&(l=l.clone()),a.push(l);if(r=jt(a),this.activityRegularizer!=null)throw new ae("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}else{const r=ez(e),o=this.computeOutputShape(r);let a;const l=tz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?r[0]:r),o!=null&&o.length>0&&Array.isArray(o[0])?a=o.map((c,p)=>new xs(l,c,this,He(e),t,this.name,p)):a=new xs(l,o,this,He(e),t,this.name),this.addInboundNode(e,a,null,null,r,o,t),this._refCount++,this.activityRegularizer!=null)throw new ae("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((s,n)=>{s!=null&&e[n]!=null&&e[n]!==s&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Sn(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const s=JSON.stringify(t.outputShapes);e.indexOf(s)===-1&&e.push(s)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Sn(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Ns(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Bl(this.weights)}build(e){this.built=!0}getWeights(e=!1){return cu(e?this.trainableWeights:this.weights)}setWeights(e){C(()=>{const t=this.weights;if(t.length!==e.length)throw new k(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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The following previous layers were accessed without issue: ${f}`);for(const T of w.outputTensors)m.push(T);f.push(x.name)}}this.nodesByDepth=u;const g=this.layers.map(y=>y.name);for(const y of g){const w=g.filter(x=>x===y).length;if(w!==1)throw new Ns(`The name "${y}" is used ${w} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new ya({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(s=>s.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new k("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const s of this.layers)t.push(...s.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const s={};let n=0;for(const r of this.layers)for(const o of r.weights){if(s[o.originalName]!=null)throw new k(`Duplicate weight name: ${o.originalName}`);s[o.originalName]=o,n++}const i=[];for(const r in e){let o=r;if(s[r]==null){const a=r.split("/"),l=a.slice(0,-2).concat([a[a.length-1]]);o=l.join("/")}if(s[o]!=null)i.push([s[o],e[r]]);else if(t)throw new k(`Provided weight data has no target variable: ${r}`);delete s[o]}if(t){const r=[];for(const o in s)r.push(o);if(r.length>0)throw new k(`${r.length} of ${n} weights are not set: ${r}`)}jl(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uo}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const s=dm(this.updatedConfig());return t?JSON.stringify(s):s}call(e,t){return C(()=>{e=He(e);const s=new Hi;for(let n=0;n<this.inputs.length;++n)s.add(this.inputs[n],e[n]);return La(this.outputs,s,t)})}computeMask(e,t){return C(()=>{e=He(e);let s;return t==null?s=In(null,e.length):s=He(t),this.runInternalGraph(e,s)[1]})}computeOutputShape(e){const t=Pl(e);if(t.length!==this.inputLayers.length)throw new k(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const s={};for(let o=0;o<t.length;o++){const a=this.inputLayers[o],l=t[o],c=a.name+"_0_0";s[c]=l}const n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Qp);if(n.length>1)for(const o of n){const a=this.nodesByDepth[o];for(const l of a){const c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;const p=[];for(let m=0;m<l.inboundLayers.length;m++){const f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],w=`${f.name}_${g}_${y}`,x=s[w];p.push(x)}const u=c.computeOutputShape(jt(p)),h=Pl(u),d=c.inboundNodes.indexOf(l);for(let m=0;m<h.length;m++){const f=`${c.name}_${d}_${m}`;s[f]=h[m]}}}const i=[],r=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],p=`${a.name}_${l}_${c}`;r.push(p)}for(let o=0;o<r.length;o++){const a=r[o];tn(a in s),i.push(s[a])}return jt(i)}runInternalGraph(e,t){t==null&&(t=In(null,e.length));const s={};for(let a=0;a<this.inputs.length;++a){const l=this.inputs[a],c=e[a],p=t[a];s[l.id]=[c,p]}const n=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Qp);for(const a of n){const l=this.nodesByDepth[a];for(const c of l){const p=c.outboundLayer,u=c.inputTensors,h=c.outputTensors,d=new Array;for(const m of u)m.id in s&&d.push(s[m.id]);if(d.length===u.length){let m={},f,g,y,w;if(c.callArgs!=null&&(m=c.callArgs),d.length===1){const[x,T]=d[0];m.mask==null&&(m.mask=T),y=He(p.call(x,m)),w=He(p.computeMask(x,T)),f=[x],g=[T]}else f=d.map(x=>x[0]),g=d.map(x=>x[1]),m.mask==null&&(m.mask=g),y=He(p.call(f,m)),w=He(p.computeMask(f,g));if(p.activityRegularizer)throw new ae("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<h.length;++x){const T=h[x],A=y[x],_=w[x];s[T.id]=[A,_]}}}}const i=[],r=[],o=[];for(const a of this.outputs){tn(a.id in s,`Could not compute output ${a.name} : ${a.id}`);const[l,c]=s[a.id];o.push(l.shape),i.push(l),r.push(c)}return[i,r,o]}buildNodeConversionMap(e){const t={};let s;for(const n of this.layers){s=n instanceof Bn?1:0;for(let i=0;i<n.inboundNodes.length;i++){const r=Bn.nodeKey(n,i);this.containerNodes.has(r)&&(t[r]=s,s+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new k(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new k("Provide either a layer name or layer index");for(const s of this.layers)if(s.name===e)return s;throw new k(`No such layer: ${e}`)}calculateLosses(){return C(()=>{const e=[];for(const t of this.layers)for(let s=0;s<t.inboundNodes.length;++s){const n=Bn.nodeKey(t,s);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),s=[];for(const r of this.layers){const o=r.getClassName(),a=r.getConfig(),l=[];for(let p=0;p<r.inboundNodes.length;p++){const u=r.inboundNodes[p],h=Bn.nodeKey(r,p);let d={};if(this.containerNodes.has(h)){if(u.callArgs)try{JSON.stringify(u.callArgs),d=u.callArgs}catch(m){console.warn(`Layer ${r.name} was passed non-serializable keyword arguments: ${u.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(u.inboundLayers.length>0){const m=[];for(let f=0;f<u.inboundLayers.length;f++){const g=u.inboundLayers[f],y=u.nodeIndices[f],w=u.tensorIndices[f],x=Bn.nodeKey(g,y);let T=t[x];T==null&&(T=0),m.push([g.name,T,w,d])}l.push(m)}}}const c={};c.name=r.name,c.className=o,c.config=a,c.inboundNodes=l,s.push(c)}e.layers=s;const n=[];for(let r=0;r<this.inputLayers.length;r++){const o=this.inputLayers[r],a=this.inputLayersNodeIndices[r],l=Bn.nodeKey(o,a);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);const p=this.inputLayersTensorIndices[r];n.push([o.name,c,p])}e.inputLayers=n;const i=[];for(let r=0;r<this.outputLayers.length;r++){const o=this.outputLayers[r],a=this.outputLayersNodeIndices[r],l=Bn.nodeKey(o,a);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);const p=this.outputLayersTensorIndices[r];i.push([o.name,c,p])}return e.outputLayers=i,e}static fromConfig(e,t,s={},n=!1){const i={},r={};function o(f,g){f.name in r?r[f.name].push(g):r[f.name]=[g]}function a(f,g){const y=[];let w;for(const x of g){const T=x[0],A=x[1],_=x[2];if(w=x[3]==null?{}:x[3],!(T in i)){o(f,g);return}const E=i[T];if(E.inboundNodes.length<=A){o(f,g);return}const F=E.inboundNodes[A];y.push(F.outputTensors[_])}y.length>0&&f.apply(jt(y),w)}function l(f){const g=f.name,y=Ls(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),i[g]=y;const w=f.inboundNodes;w.forEach(x=>{if(!(x instanceof Array))throw new k(`Corrupted configuration, expected array for nodeData: ${x}`);o(y,x)})}const c=t.name,p=t.layers;for(const f of p)l(f);for(;!pA(r);)for(const f of p){const g=i[f.name];if(g.name in r){const y=r[g.name];delete r[g.name];for(const w of y)a(g,w)}}const u=[],h=[],d=t.inputLayers;for(const f of d){const g=f[0],y=f[1],w=f[2];tn(g in i);const x=i[g],T=x.inboundNodes[y].outputTensors;u.push(T[w])}const m=t.outputLayers;for(const f of m){const g=f[0],y=f[1],w=f[2];tn(g in i);const x=i[g],T=x.inboundNodes[y].outputTensors;h.push(T[w])}return new e({inputs:u,outputs:h,name:c})}get stateful(){if(this._stateful)throw new k("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){C(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Rz(e,t,s){const n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${s} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(r=>{r in e?i.push(e[r]):i.push(null)}),i}else throw new Error(`The model has multiple (${n}) outputs, so ${s} must be either an array with ${n} elements or an object with ${t} keys. Provided ${s} not understood: ${JSON.stringify(e)}`)}function mm(e,t){return Rz(e,t,"classWeight")}async function fm(e,t,s,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(s!=null){const i=C(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const a=1;return e.argMax(a)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),r=Array.from(await i.data());ce(i);const o=[];return r.forEach(a=>{if(s[a]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${a} exists in the data but not in classWeight`);o.push(s[a])}),Oe(o,"float32")}else return null}function VA(e,t){return R(e,t)}const Oz=32;function qA(e,t){let s,n;const i=t;s=i.xs,n=i.ys,N.assert(s!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const r=GA("input",e.inputNames,s),o=GA("output",e.outputNames,n),a=r[0].shape[0];N.assert(r.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${r.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),N.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<r.length;l++)N.assert(r[l].shape[0]===a,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${r[l].shape[0]}; expected  ${a} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)N.assert(o[l].shape[0]===a,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected  ${a} based on input ${e.inputNames[0]}.`);return{xs:r,ys:o}}function GA(e,t,s){if(s instanceof me)return[s];if(Array.isArray(s))return N.assert(s.length===t.length,()=>`Received an array of ${s.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),s;{const n=[];for(const i of t){if(s[i]==null)throw new k(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);n.push(s[i])}return n}}function Ez(e){if(e.length===3)throw new ae("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function YA(e,t,s){const n=s.batchesPerEpoch!=null;if(N.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),N.assert(s!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),N.assert(s.epochs!=null&&s.epochs>0&&Number.isInteger(s.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${s.epochs}`),N.assert(!n||s.batchesPerEpoch>0&&Number.isInteger(s.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${s.batchesPerEpoch}`),N.assert(s.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=s.validationData!=null;let r,o;if(i)if(HA(s.validationData))N.assert(s.validationBatches==null||s.validationBatches>0&&Number.isInteger(s.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${s.validationBatches}`);else{const g=Ez(s.validationData);r=g.xs,o=g.ys}const a=e.makeTrainFunction(),l=e.getDedupedMetricsNames();let c;i?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();const p=om(s.callbacks,s.yieldEvery),u=s.verbose==null?1:s.verbose,{callbackList:h,history:d}=am(p,u,s.epochs,null,null,_z(t,s),null,i,c);h.setModel(e),e.history=d,await h.onTrainBegin(),e.stopTraining_=!1;let m=s.initialEpoch==null?0:s.initialEpoch,f=await t.iterator();for(;m<s.epochs;){const g={};await h.onEpochBegin(m);let y=0,w=0;for(n||(f=await t.iterator());n?y<s.batchesPerEpoch:!0;){const x=await f.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${s.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${s.batchesPerEpoch*s.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){const{xs:T,ys:A}=qA(e,x.value),_={};_.batch=w,_.size=T[0].shape[0],await h.onBatchBegin(w,_);const E=[];if(s.classWeight!=null){const M=mm(s.classWeight,e.outputNames);for(let P=0;P<M.length;++P)E.push(await fm(A[P],null,M[P]))}const F=T.concat(A).concat(E),D=a(F);ce(F);for(let M=0;M<l.length;++M){const P=l[M],B=D[M];_[P]=B,pt(B)}await h.onBatchEnd(w,_),rm(_),w++,y++}if(n?y>=s.batchesPerEpoch:x.done){if(i){let T;HA(s.validationData)?T=He(await e.evaluateDataset(s.validationData,{batches:s.validationBatches})):T=He(e.evaluate(r,o,{batchSize:s.validationBatchSize==null?Oz:s.validationBatchSize,verbose:0}));for(let A=0;A<e.metricsNames.length;++A)g[`val_${e.metricsNames[A]}`]=T[A]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(m,g),m++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function _z(e,t){let s=null;return t.batchesPerEpoch!=null?s=t.batchesPerEpoch:Number.isFinite(e.size)&&(s=e.size),s}function HA(e){return typeof e.iterator=="function"}function kz(e){return typeof e.next=="function"}async function KA(e,t,s){s=s||{};const n=s.batches!=null,i=e.testFunction;let r=[];if(s.verbose>0)throw new ae("Verbose mode is not implemented yet.");N.assert(!n||s.batches>0&&Number.isInteger(s.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(s.batches)}`);const o=kz(t)?t:await t.iterator();let a=0,l=0;for(;n?l<s.batches:!0;){const c=await o.next();if(r=C(()=>{if(c.value){const{xs:p,ys:u}=qA(e,c.value),h=p.concat(u),d=C(()=>i(h));if(ce(h),l===0)for(let f=0;f<d.length;++f)r.push(j(0));const m=h[0].shape[0];for(let f=0;f<d.length;++f){const g=d[f],y=r[f];r[f]=C(()=>$(r[f],R(m,g))),l>0&&ce(y)}ce(d),a+=m,++l}return r}),c.done){n&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). 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Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,s,n,i=!0,r){const[o,a]=this.standardizeUserDataXY(e,t,i,r);if(s!=null)throw new Error("sample weight is not supported yet.");let l=null;if(n!=null){const c=mm(n,this.outputNames);l=[];for(let p=0;p<c.length;++p)l.push(await fm(a[p],null,c[p]))}return[o,a,l]}testLoop(e,t,s,n=0,i){return C(()=>{const r=this.checkNumSamples(t,s,i,"steps"),o=[];if(n>0)throw new ae("Verbose mode is not implemented yet.");if(i!=null)throw new ae("steps mode in testLoop() is not implemented yet");{const a=bm(r,s),l=Oe(cs(0,r));for(let c=0;c<a.length;++c){const p=a[c][0],u=a[c][1],h=Gi(l,p,u-p),d=ym(t,h),m=e(d);if(c===0)for(let f=0;f<m.length;++f)o.push(j(0));for(let f=0;f<m.length;++f){const g=m[f];o[f]=$(o[f],R(u-p,g))}}for(let c=0;c<o.length;++c)o[c]=Z(o[c],r)}return o})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let s=0;s<e.length;++s){const n=e[s];let i=n;if(Ub(e,n)>1){const r=Ub(e.slice(0,s),n);i+=`_${r}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],s=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),r=[],o=()=>{const p=[];for(let m=0;m<this.inputs.length;++m)p.push({key:this.inputs[m],value:s[m]});const u=new Hi(p),h=La(this.outputs,u,{training:!0});let d;for(let m=0;m<this.lossFunctions.length;++m){const f=this.lossFunctions[m];let g=f(n[m],h[m]);i[m]!=null&&(g=VA(g,i[m]));const y=Xe(g);t.push(y),m===0?d=g:d=$(d,g)}for(let m=0;m<this.metricsTensors.length;++m){let f;if(this.outputs.length>1&&m<this.outputs.length)f=t[m];else{const g=this.metricsTensors[m][0],y=this.metricsTensors[m][1];f=Xe(g(n[y],h[y]))}pt(f),r.push(f)}return d=Xe(d),this.calculateLosses().forEach(m=>{d=$(d,m)}),d},a=this.collectedTrainableWeights.map(p=>p.read()),l=!0,c=this.optimizer_.minimize(o,l,a);return[c].concat(r)}}makeTestFunction(){this.testFunction=e=>C(()=>{const t=[];let s;const n=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=[];for(let l=0;l<this.inputs.length;++l)r.push({key:this.inputs[l],value:n[l]});const o=new Hi(r),a=La(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){const c=this.lossFunctions[l],p=Xe(c(i[l],a[l]));l===0?s=p:s=$(s,p),t.push(s)}for(let l=0;l<this.metricsTensors.length;++l){const c=this.metricsTensors[l][0],p=this.metricsTensors[l][1],u=Xe(c(i[p],a[p]));t.push(u)}return t})}async fit(e,t,s={}){return XA(this,e,t,s)}async fitDataset(e,t){return YA(this,e,t)}async trainOnBatch(e,t){const s=await this.standardizeUserData(e,t),n=s[0],i=s[1],r=this.makeTrainFunction(),o=r(n.concat(i)),a=[];for(const l of o){const c=await l.data();a.push(c[0])}return ce(o),jt(a)}getNamedWeights(e){const t=[],s=e!=null&&e.trainableOnly,n=s?this.trainableWeights:this.weights,i=this.getWeights(s);for(let r=0;r<n.length;++r){if(s&&!n[r].trainable)continue;t.push({name:n[r].originalName,tensor:i[r]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=pl().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-pl().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=vn(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>vn(t))}else{const t=Object.keys(this.loss);e={};const s=this.loss;for(const n of t)if(typeof s[n]=="string")e[n]=vn(s[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[vn(gu(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>vn(gu(e)));{const e={};for(const t in this.metrics)e[t]=vn(gu(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=xa(e.optimizer_config),s=Ls(t);let n;if(typeof e.loss=="string")n=Pi(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(r=>Pi(r));else if(e.loss!=null){n={};for(const r in e.loss)n[r]=Pi(e.loss[r])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(r=>Pi(r));else if(e.metrics!=null){i={};for(const r in e.metrics)i[r]=Pi(e.metrics[r])}this.compile({loss:n,metrics:i,optimizer:s})}async save(e,t){if(typeof e=="string"){const l=Rt.getSaveHandlers(e);if(l.length===0)throw new k(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new k(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new k("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const s=await Rt.encodeWeights(this.getNamedWeights(t)),n=!1,i=null,r=this.toJSON(i,n),o={modelTopology:r,format:Wz,generatedBy:`TensorFlow.js tfjs-layers v${uo}`,convertedBy:null},a=t==null?!1:t.includeOptimizer;if(a&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();const l="optimizer",{data:c,specs:p}=await Rt.encodeWeights(await this.optimizer.getWeights(),l);s.specs.push(...p),s.data=Rt.concatenateArrayBuffers([s.data,c])}if(this.userDefinedMetadata!=null){const l=!0;Qb(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=s.data,o.weightSpecs=s.specs,e.save(o)}setUserDefinedMetadata(e){Qb(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}nn.className="Model";V.registerClass(nn);class eN extends nn{}eN.className="Functional";V.registerClass(eN);async function tN(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let s=e.modelTopology;s.model_config!=null&&(s=s.model_config);const n=xa(s),i=Ls(n,t);if(e.weightsManifest!=null){const r=await Rt.loadWeights(e.weightsManifest,e.pathPrefix,i.weights.map(a=>a.originalName)),o={};for(const a of i.weights)o[a.originalName]=r[a.originalName];i.loadWeights(o),ce(r)}return i}async function sN(e,t){if(t==null&&(t={}),typeof e=="string"){const s=Rt.getLoadHandlers(e,t);if(s.length===0)s.push(Rt.browserHTTPRequest(e,t));else if(s.length>1)throw new k(`Found more than one (${s.length}) load handlers for URL '${e}'`);e=s[0]}return zz(e,void 0,t)}async function zz(e,t,s){if(s==null&&(s={}),e.load==null)throw new k("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const n=await e.load();let i=n.modelTopology;i.model_config!=null&&(i=i.model_config);const r=s.strict==null?!0:s.strict,o=n.weightData!=null&&n.weightSpecs!=null&&r,a=Ls(xa(i),t,o),l=n.trainingConfig;if(l!=null&&a.loadTrainingConfig(l),n.userDefinedMetadata!=null&&a.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new k("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:c,optimizerWeights:p}=Pz(n.weightData,n.weightSpecs);a.loadWeights(c,r),a.optimizer!=null&&p.length>0&&await a.optimizer.setWeights(p),ce(c),ce(p.map(u=>u.tensor))}return a}function Pz(e,t){const s=Rt.decodeWeights(e,t),n={},i=[];return t.forEach(r=>{r.group==="optimizer"?i.push({name:r.name,tensor:s[r.name]}):n[r.name]=s[r.name]}),{modelWeights:n,optimizerWeights:i}}class mo extends nn{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:co("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(s=>s<0))throw new k(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof mo||e instanceof nn;let s;if(t){if(s=e,s.outputs.length!==1)throw new k("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(s.inputs.length!==1)throw new k("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new k("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const n=im({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=s.outputs,this.inputs=s.inputs;else{if(e.inboundNodes.length!==1)throw new k(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new k("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=qb(this.outputs[0])}this.inboundNodes=[],new ya({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:In(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{const n=e.apply(this.outputs[0]);if(Array.isArray(n))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(Ue(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new nn({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,s=console.log){this.built||this.build(),super.summary(e,t,s)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,s={}){if(!this.built)throw new Ns("The model needs to be compiled before being used.");return this.model.evaluate(e,t,s)}async evaluateDataset(e,t){if(!this.built)throw new Ns("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,s={}){if(!this.built)throw new Ns("The model needs to be compiled before being used.");return this.model.fit(e,t,s)}async fitDataset(e,t){if(!this.built)throw new Ns("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,s={},n=!1){let i,r={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new k("Legacy serialization format not supported yet.");i=t}else N.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,r=t;const o=new e(r);if(!(o instanceof mo))throw new ae(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(const a of i){const l=void 0,c=Ls(a,l,n);n&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new k("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new k("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const s={};s.className=t.getClassName(),s.config=t.getConfig(),e.push(s)}return{name:this.name,layers:e}}}mo.className="Sequential";V.registerClass(mo);function nN(e){return new nn(e)}function iN(e){return new mo(e)}function rN(e,t){return t==null&&(t={}),sN(e,t)}function wm(e){return im(e)}function oN(e,t){sn.registerCallbackConstructor(e,t)}class Ps extends V.Serializable{getConfig(){return{}}}class aN extends Ps{apply(e,t=1){return AA(e,t)}}aN.className="elu";V.registerClass(aN);class lN extends Ps{apply(e){return Rl(e)}}lN.className="selu";V.registerClass(lN);class cN extends Ps{apply(e){return Fe(e)}}cN.className="relu";V.registerClass(cN);class pN extends Ps{apply(e){return C(()=>bn(6,Fe(e)))}}pN.className="relu6";V.registerClass(pN);class uN extends Ps{apply(e){return e}}uN.className="linear";V.registerClass(uN);class hN extends Ps{apply(e){return os(e)}}hN.className="sigmoid";V.registerClass(hN);class dN extends Ps{apply(e){return CA(e)}}dN.className="hardSigmoid";V.registerClass(dN);class mN extends Ps{apply(e){return di(e)}}mN.className="softplus";V.registerClass(mN);class fN extends Ps{apply(e){return NA(e)}}fN.className="softsign";V.registerClass(fN);class gN extends Ps{apply(e){return Mi(e)}}gN.className="tanh";V.registerClass(gN);class xm extends Ps{apply(e,t=-1){return ts(e,t)}}xm.className="softmax";V.registerClass(xm);class yN extends Ps{apply(e,t=-1){return Il(e,t)}}yN.className="logSoftmax";V.registerClass(yN);class bN extends Ps{apply(e,t=1){return C(()=>os(e.mul(t)).mul(e))}}bN.className="swish";V.registerClass(bN);function jn(e){return e.getClassName()}function nw(e,t={}){return fi(e,V.SerializationMap.getMap().classNameMap,t,"activation")}function Vn(e){if(e==null){const t={};return t.className="linear",t.config={},nw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},nw(t)}else return e instanceof Ps?e:nw(e)}function iw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class wN extends V.Serializable{}class Yl extends wN{constructor(e){super();iw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return C(()=>{let t=ye([1]);return this.hasL1&&(t=$(t,te(R(this.l1,et(e))))),this.hasL2&&(t=$(t,te(R(this.l2,ga(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Yl.className="L1L2";V.registerClass(Yl);function xN(e){return iw(e),new Yl({l1:e!=null?e.l1:null,l2:0})}function LN(e){return iw(e),new Yl({l2:e!=null?e.l2:null,l1:0})}const SN={l1l2:"L1L2"};function Pe(e){return Ml(e)}function IN(e,t={}){return fi(e,V.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ye(e){if(e==null)return null;if(typeof e=="string"){const t=e in SN?SN[e]:e,s={className:t,config:{}};return IN(s)}else return e instanceof wN?e:IN(e)}class Lm extends Le{constructor(e){super(e??{});this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=we(e);let s=Fe(e);return this.maxValue!=null&&(s=xt(s,0,this.maxValue)),s}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Lm.className="ReLU";V.registerClass(Lm);class Sm extends Le{constructor(e){super(e??{});this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const s=we(e);return Ll(s,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Sm.className="LeakyReLU";V.registerClass(Sm);class Im extends Le{constructor(e){super(e??{});if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=je(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ye(e.alphaRegularizer),this.alphaConstraint=at(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new k(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ue(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const s={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)s[n]=e[n];this.inputSpec=[new st({ndim:e.length,axes:s})],this.built=!0}call(e,t){return e=we(e),Qr(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Ze(this.alphaInitializer),alphaRegularizer:Pe(this.alphaRegularizer),alphaConstraint:ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Im.className="PReLU";V.registerClass(Im);class vm extends Le{constructor(e){super(e??{});if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ae(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const s=we(e);return gn(s)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}vm.className="ELU";V.registerClass(vm);class Tm extends Le{constructor(e){super(e??{});this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const s=we(e);return s.mul(ji(s.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Tm.className="ThresholdedReLU";V.registerClass(Tm);class Am extends Le{constructor(e){super(e??{});this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new xm().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const s=we(e);return this.softmax(s,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Am.className="Softmax";V.registerClass(Am);function fo(e,t,s){if(typeof e=="number")return In(e,t);if(e.length!==t)throw new k(`The ${s} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){const i=e[n];if(!LA(i))throw new k(`The ${s} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function Rs(e,t,s,n,i=1){if(e==null)return e;const r=t+(t-1)*(i-1);let o;return s==="same"?o=e:o=e-r+1,Math.floor((o+n-1)/n)}function yu(e,t,s,n){if(e==null)return null;if(n==="valid")e=e*t+Pn([s-t,0]);else if(n==="same")e=e*t;else throw new k(`Unsupport padding mode: ${n}.`);return e}function bu(e,t){return C(()=>(lt(t),t==="channelsFirst"?se(e,[0,2,3,1]):e))}function rw(e,t){return C(()=>(lt(t),t==="channelsFirst"?se(e,[0,2,3,4,1]):e))}function Bz(e,t,s,n=1,i="valid",r,o=1){return C(()=>{if(r==null&&(r=ys()),lt(r),e.shape.length!==3)throw new k(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new k(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(s!=null&&s.shape.length!==1)throw new k(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(r==="channelsFirst"&&(e=se(e,[0,2,1])),i==="causal")throw new ae("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=fl(e,t,n,i==="same"?"same":"valid","NWC",o);return s!=null&&(a=Cs(a,s)),a})}function vN(e,t,s,n=[1,1],i="valid",r,o,a=null){return C(()=>{if(r==null&&(r=ys()),lt(r),e.rank!==3&&e.rank!==4)throw new k(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new k(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=bu(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=xn.conv2d({x:l,filter:t,strides:n,pad:i==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:s,activation:a}),r==="channelsFirst"&&(l=se(l,[0,3,1,2])),l})}function jz(e,t,s,n=[1,1,1],i="valid",r,o){return C(()=>{if(r==null&&(r=ys()),lt(r),e.rank!==4&&e.rank!==5)throw new k(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new k(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let a=rw(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=bl(a,t,n,i==="same"?"same":"valid","NDHWC",o),s!=null&&(a=Cs(a,s)),r==="channelsFirst"&&(a=se(a,[0,4,1,2,3])),a})}class Nm extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Nm.verifyArgs(t),this.rank=e,yt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ae(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=fo(t.kernelSize,e,"kernelSize"),this.strides=fo(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,bs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,lt(this.dataFormat),this.activation=Vn(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=at(t.biasConstraint),this.biasRegularizer=Ye(t.biasRegularizer),this.activityRegularizer=Ye(t.activityRegularizer),this.dilationRate=fo(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new k(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new k(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new k(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(tn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Md(e.kernelSize,"number",1,3))throw new k(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:jn(this.activation),useBias:this.useBias,biasInitializer:Ze(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class wu extends Nm{constructor(e,t){super(e,t);this.kernel=null,wu.verifyArgs(t),this.filters=t.filters,yt(this.filters,"filters"),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=at(t.kernelConstraint),this.kernelRegularizer=Ye(t.kernelRegularizer)}build(e){e=Ue(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new k(`The channel dimension of the input should be defined. Found ${e[t]}`);const s=e[t],n=this.kernelSize.concat([s,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:s}}],this.built=!0}call(e,t){return C(()=>{e=we(e);let s;const n=this.bias==null?null:this.bias.read(),i=Ud(this.activation.getClassName());if(i!=null&&this.rank===2)s=vN(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)s=Bz(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)s=vN(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)s=jz(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ae("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(s=this.activation.apply(s))}return s})}computeOutputShape(e){e=Ue(e);const t=[],s=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<s.length;++i){const r=Rs(s[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(r)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){const e={filters:this.filters,kernelInitializer:Ze(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new k(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Kl extends wu{constructor(e){super(2,e);Kl.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Md(e.kernelSize,"number",1,2))throw new k(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Kl.className="Conv2D";V.registerClass(Kl);class xu extends wu{constructor(e){super(3,e);xu.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new k(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}xu.className="Conv3D";V.registerClass(xu);class Cm extends Kl{constructor(e){super(e);if(this.inputSpec=[new st({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new k(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ue(e),e.length!==4)throw new k("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new k("The channel dimension of the inputs should be defined. Found `None`.");const s=e[t],n=this.kernelSize.concat([this.filters,s]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new st({ndim:4,axes:{[t]:s}})],this.built=!0}call(e,t){return C(()=>{let s=we(e);if(s.shape.length!==4)throw new k(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${s.shape.length}`);const n=s.shape,i=n[0];let r,o;this.dataFormat==="channelsFirst"?(r=2,o=3):(r=1,o=2);const a=n[r],l=n[o],c=this.kernelSize[0],p=this.kernelSize[1],u=this.strides[0],h=this.strides[1],d=yu(a,u,c,this.padding),m=yu(l,h,p,this.padding),f=[i,d,m,this.filters];this.dataFormat!=="channelsLast"&&(s=se(s,[0,2,3,1]));let g=yl(s,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=se(g,[0,3,1,2])),this.bias!=null&&(g=Cs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Ue(e);const t=e.slice();let s,n,i;this.dataFormat==="channelsFirst"?(s=1,n=2,i=3):(s=3,n=1,i=2);const r=this.kernelSize[0],o=this.kernelSize[1],a=this.strides[0],l=this.strides[1];return t[s]=this.filters,t[n]=yu(t[n],a,r,this.padding),t[i]=yu(t[i],l,o,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Cm.className="Conv2DTranspose";V.registerClass(Cm);class TN extends wu{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new k("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new k("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new k(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ye(t.depthwiseRegularizer),this.depthwiseConstraint=at(t.depthwiseConstraint),this.pointwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ye(t.pointwiseRegularizer),this.pointwiseConstraint=at(t.pointwiseConstraint)}build(e){if(e=Ue(e),e.length<this.rank+2)throw new k(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new k(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const s=e[t],n=this.kernelSize.concat([s,this.depthMultiplier]),i=[];for(let o=0;o<this.rank;++o)i.push(1);i.push(s*this.depthMultiplier,this.filters);const r=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,r,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,r,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,r,this.biasConstraint):this.bias=null,this.inputSpec=[new st({ndim:this.rank+2,axes:{[t]:s}})],this.built=!0}call(e,t){return C(()=>{e=we(e);let s;if(this.rank===1)throw new ae("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=se(e,[0,2,3,1])),s=eo(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(s=Cs(s,this.bias.read(),this.dataFormat)),this.activation!=null&&(s=this.activation.apply(s)),this.dataFormat==="channelsFirst"&&(s=se(s,[0,3,1,2])),s})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ze(this.depthwiseInitializer),e.pointwiseInitializer=Ze(this.pointwiseInitializer),e.depthwiseRegularizer=Pe(this.depthwiseRegularizer),e.pointwiseRegularizer=Pe(this.pointwiseRegularizer),e.depthwiseConstraint=ot(this.depthwiseConstraint),e.pointwiseConstraint=ot(this.pointwiseConstraint),e}}TN.className="SeparableConv";class Rm extends TN{constructor(e){super(2,e)}}Rm.className="SeparableConv2D";V.registerClass(Rm);class Lu extends wu{constructor(e){super(1,e);Lu.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Md(e.kernelSize,"number",1,1))throw new k(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Lu.className="Conv1D";V.registerClass(Lu);class Om extends Le{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return C(()=>{if(e=we(e),this.dataFormat==="channelsLast"){const s=su(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return su(s,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const s=su(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return su(s,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Om.className="Cropping2D";V.registerClass(Om);class Em extends Le{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],s=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,s]}else{const t=e[1]==null?null:this.size[0]*e[1],s=e[2]==null?null:this.size[1]*e[2];return[e[0],t,s,e[3]]}}call(e,t){return C(()=>{let s=we(e);const n=s.shape;if(this.dataFormat==="channelsFirst"){s=se(s,[0,2,3,1]);const i=this.size[0]*n[2],r=this.size[1]*n[3],o=s.resizeNearestNeighbor([i,r]);return se(o,[0,3,1,2])}else{const i=this.size[0]*n[1],r=this.size[1]*n[2];return s.resizeNearestNeighbor([i,r])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Em.className="UpSampling2D";V.registerClass(Em);function Vz(e,t,s=[1,1],n="valid",i,r){return C(()=>{i==null&&(i=ys()),lt(i);let o=bu(e,i);if(e.rank!==4)throw new k(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new k(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=fn(o,t,s,n==="same"?"same":"valid","NHWC",r),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}class _m extends Nm{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=at(e.depthwiseConstraint),this.depthwiseRegularizer=Ye(e.depthwiseRegularizer)}build(e){if(e=Ue(e),e.length<4)throw new k(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new k(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const s=e[t],n=[this.kernelSize[0],this.kernelSize[1],s,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[s*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return C(()=>{e=we(e);let s=Vz(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(s=Cs(s,this.bias.read(),this.dataFormat)),this.activation!=null&&(s=this.activation.apply(s)),s})}computeOutputShape(e){e=Ue(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=Rs(t,this.kernelSize[0],this.padding,this.strides[0]),r=Rs(s,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,i,r]:[e[0],i,r,n]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ze(this.depthwiseInitializer),e.depthwiseRegularizer=Pe(this.depthwiseRegularizer),e.depthwiseConstraint=ot(this.depthwiseRegularizer),e}}_m.className="DepthwiseConv2D";V.registerClass(_m);function ow(e,t,s,n){if(Array.isArray(e)){if(t!=null||s!=null)throw new k("When inputs is an array, neither initialState or constants should be provided");n!=null&&(s=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(r){return r==null||Array.isArray(r)?r:[r]}return t=i(t),s=i(s),{inputs:e,initialState:t,constants:s}}function aw(e,t,s,n=!1,i,r,o=!1,a=!1){return C(()=>{const l=t.shape.length;if(l<3)throw new k(`Input should be at least 3D, but is ${l}D.`);const c=[1,0].concat(cs(2,l));if(t=se(t,c),r!=null)throw new ae("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===l-1&&(i=Mt(i,-1)),i=se(i,c)),n&&(t=Et(t,0),i!=null&&(i=Et(i,0)));const p=[];let u,h=s;const d=t.shape[0],m=qe(t);let f;i!=null&&(f=qe(i));for(let y=0;y<d;++y){const w=m[y],x=C(()=>e(w,h));if(i==null)u=x[0],h=x[1];else{const T=C(()=>{const A=f[y],_=Ot(A).sub(A),E=x[0].mul(A).add(h[0].mul(_)),F=h.map((D,M)=>x[1][M].mul(A).add(D.mul(_)));return{output:E,newStates:F}});u=T.output,h=T.newStates}a&&p.push(u)}let g;if(a){const y=1;g=Ge(p,y)}return[u,g,h]})}class rn extends Le{constructor(e){super(e);let t;if(e.cell==null)throw new k("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Su({cells:e.cell}):t=e.cell,t.stateSize==null)throw new k("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new st({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return cs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){sm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const s=t[0];let n;if(this.returnSequences?n=[e[0],e[1],s]:n=[e[0],s],this.returnState){const i=[];for(const r of t)i.push([e[0],r]);return[n].concat(i)}else return n}computeMask(e,t){return C(()=>{Array.isArray(t)&&(t=t[0]);const s=this.returnSequences?t:null;if(this.returnState){const n=this.states.map(i=>null);return[s].concat(n)}else return s})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let s=0;s<e;++s)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ae("Constants support is not implemented in RNN yet.");sm(e)&&(e=e[0]),e=e;const s=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new st({shape:[s,null,...n]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ae("Constants support is not implemented in RNN yet.");this.cell.build(i);let r;if(Array.isArray(this.cell.stateSize)?r=this.cell.stateSize:r=[this.cell.stateSize],this.stateSpec!=null){if(!N.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),r))throw new k(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(o=>new st({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){C(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");const s=this.inputSpec[0].shape[0];if(s==null)throw new k("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ye([s,n])):this.states_=[ye([s,this.cell.stateSize])];else if(e==null)ce(this.states_),this.keptStates!=null&&(ce(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ye([s,n])):this.states_[0]=ye([s,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new k(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ce(this.states_);for(let n=0;n<this.states_.length;++n){const i=e[n],r=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,o=[s,r];if(!N.arraysEqual(i.shape,o))throw new k(`State ${n} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[n]=i}}this.states_=this.states_.map(n=>pt(n.clone()))})}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=ow(e,s,n,this.numConstants);e=i.inputs,s=i.initialState,n=i.constants;let r=[],o=[];if(s!=null){t.initialState=s,r=r.concat(s),this.stateSpec=[];for(const l of s)this.stateSpec.push(new st({shape:l.shape}));o=o.concat(this.stateSpec)}n!=null&&(t.constants=n,r=r.concat(n),this.numConstants=n.length);const a=r[0]instanceof xs;if(a){const l=[e].concat(r),c=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=c;const u=super.apply(l,t);return this.inputSpec=p,u}else return super.apply(e,t)}call(e,t){return C(()=>{const s=t==null?null:t.mask,n=t==null?null:t.training;let i=t==null?null:t.initialState;e=we(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const r=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==r)throw new k(`RNN Layer has ${r} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const o={training:n},a=(d,m)=>{const f=this.cell.call([d].concat(m),o);return[f[0],f.slice(1)]},l=aw(a,e,i,this.goBackwards,s,null,this.unroll,this.returnSequences),c=l[0],p=l[1],u=l[2];this.stateful&&this.resetStates(u,n);const h=this.returnSequences?p:c;return this.returnState?[h].concat(u):h})}getInitialState(e){return C(()=>{let t=ye(e.shape);return t=te(t,[1,2]),t=Vi(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(s=>s>1?Vd(t,[1,s]):t):this.cell.stateSize>1?[Vd(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const s=this.cell.getConfig();return this.getClassName()===rn.className&&(t.cell={className:this.cell.getClassName(),config:s}),Object.assign({},s,e,t)}static fromConfig(e,t,s={}){const n=t.cell,i=Ls(n,s);return new e(Object.assign(t,{cell:i}))}}rn.className="RNN";V.registerClass(rn);class go extends Le{}class Iu extends go{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,yt(this.units,"units"),this.activation=Vn(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=at(e.kernelConstraint),this.recurrentConstraint=at(e.recurrentConstraint),this.biasConstraint=at(e.biasConstraint),this.dropout=fa([1,Pn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fa([1,Pn([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ue(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return C(()=>{if(e=e,e.length!==2)throw new k(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let s=e[1];e=e[0];const n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yi({ones:()=>Ot(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yi({ones:()=>Ot(s),rate:this.recurrentDropout,training:n}));let i;const r=this.dropoutMask,o=this.recurrentDropoutMask;r!=null?i=Cn(R(e,r),this.kernel.read()):i=Cn(e,this.kernel.read()),this.bias!=null&&(i=Cs(i,this.bias.read())),o!=null&&(s=R(s,o));let a=$(i,Cn(s,this.recurrentKernel.read()));return this.activation!=null&&(a=this.activation.apply(a)),[a,a]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jn(this.activation),useBias:this.useBias,kernelInitializer:Ze(this.kernelInitializer),recurrentInitializer:Ze(this.recurrentInitializer),biasInitializer:Ze(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:ot(this.kernelConstraint),recurrentConstraint:ot(this.recurrentConstraint),biasConstraint:ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Iu.className="SimpleRNNCell";V.registerClass(Iu);class km extends rn{constructor(e){e.cell=new Iu(e),super(e)}call(e,t){return C(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}static fromConfig(e,t){return new e(t)}}km.className="SimpleRNN";V.registerClass(km);class vu extends go{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new k("GRUCell does not support reset_after parameter set to true.");this.units=e.units,yt(this.units,"units"),this.activation=Vn(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vn(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=at(e.kernelConstraint),this.recurrentConstraint=at(e.recurrentConstraint),this.biasConstraint=at(e.biasConstraint),this.dropout=fa([1,Pn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fa([1,Pn([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ue(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return C(()=>{if(e=e,e.length!==2)throw new k(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const s=t.training==null?!1:t.training;let n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yi({ones:()=>Ot(e),rate:this.dropout,training:s,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yi({ones:()=>Ot(n),rate:this.recurrentDropout,training:s,count:3}));const i=this.dropoutMask,r=this.recurrentDropoutMask;let o,a,l;0<this.dropout&&this.dropout<1&&(e=R(e,i[0]));let c=Cn(e,this.kernel.read());this.useBias&&(c=Cs(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=R(n,r[0]));const p=this.recurrentKernel.read(),[u,h]=Bt(p,[2*this.units,this.units],p.rank-1),d=Cn(n,u),[m,f,g]=Bt(c,3,c.rank-1),[y,w]=Bt(d,2,d.rank-1);o=this.recurrentActivation.apply($(m,y)),a=this.recurrentActivation.apply($(f,w));const x=Cn(R(a,n),h);l=this.activation.apply($(g,x));const T=$(R(o,n),R($(1,ke(o)),l));return[T,T]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jn(this.activation),recurrentActivation:jn(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ze(this.kernelInitializer),recurrentInitializer:Ze(this.recurrentInitializer),biasInitializer:Ze(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:ot(this.kernelConstraint),recurrentConstraint:ot(this.recurrentConstraint),biasConstraint:ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}vu.className="GRUCell";V.registerClass(vu);class Dm extends rn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new vu(e),super(e)}call(e,t){return C(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Dm.className="GRU";V.registerClass(Dm);class Sa extends go{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,yt(this.units,"units"),this.activation=Vn(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vn(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=at(e.kernelConstraint),this.recurrentConstraint=at(e.recurrentConstraint),this.biasConstraint=at(e.biasConstraint),this.dropout=fa([1,Pn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fa([1,Pn([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Ue(e);const s=e[e.length-1];this.kernel=this.addWeight("kernel",[s,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,r=this.units;n=new(t=class extends zs{apply(a,l){const c=i.apply([r]),p=new zl().apply([r]),u=i.apply([r*2]);return jb(jb(c,p),u)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return C(()=>{const s=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new k(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yi({ones:()=>Ot(e),rate:this.dropout,training:s,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yi({ones:()=>Ot(n),rate:this.recurrentDropout,training:s,count:4}));const r=this.dropoutMask,o=this.recurrentDropoutMask;let a,l,c,p;0<this.dropout&&this.dropout<1&&(e=R(e,r[0]));let u=Cn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=R(n,o[0])),u=$(u,Cn(n,this.recurrentKernel.read())),this.useBias&&(u=Cs(u,this.bias.read()));const[h,d,m,f]=Bt(u,4,u.rank-1);a=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(d),c=$(R(l,i),R(a,this.activation.apply(m))),p=this.recurrentActivation.apply(f);const g=R(p,this.activation.apply(c));return[g,g,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jn(this.activation),recurrentActivation:jn(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ze(this.kernelInitializer),recurrentInitializer:Ze(this.recurrentInitializer),biasInitializer:Ze(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:ot(this.kernelConstraint),recurrentConstraint:ot(this.recurrentConstraint),biasConstraint:ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Sa.className="LSTMCell";V.registerClass(Sa);class Fm extends rn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Sa(e),super(e)}call(e,t){return C(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Fm.className="LSTM";V.registerClass(Fm);class Su extends go{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return C(()=>{e=e;let s=e.slice(1);const n=[];for(const o of this.cells.slice().reverse())Array.isArray(o.stateSize)?n.push(s.splice(0,o.stateSize.length)):n.push(s.splice(0,1));n.reverse();const i=[];let r;for(let o=0;o<this.cells.length;++o){const a=this.cells[o];s=n[o],o===0?r=[e[0]].concat(s):r=[r[0]].concat(s),r=a.call(r,t),i.push(r.slice(1))}s=[];for(const o of i.slice().reverse())s.push(...o);return[r[0]].concat(s)})}build(e){sm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((s,n)=>{zn(`RNNCell_${n}`,()=>{s.build(e),Array.isArray(s.stateSize)?t=s.stateSize[0]:t=s.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),s=this.cells.map(t),n={cells:s};return Object.assign({},e,n)}static fromConfig(e,t,s={}){const n=[];for(const i of t.cells)n.push(Ls(i,s));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const s of this.cells)t.push(...s.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return cu(e)}setWeights(e){const t=[];for(const s of this.cells){const n=s.weights.length,i=e.splice(n);for(let r=0;r<s.weights.length;++r)t.push([s.weights[r],i[r]])}jl(t)}}Su.className="StackedRNNCells";V.registerClass(Su);function Yi(e){const{ones:t,rate:s,training:n=!1,count:i=1}=e,r=()=>qd(t(),s),o=()=>lo(r,t,n);if(!i||i<=1)return pt(o().clone());const a=Array(i).fill(void 0).map(o);return a.map(l=>pt(l.clone()))}var Gz=function(e,t){var s={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&t.indexOf(n)<0&&(s[n]=e[n]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,n=Object.getOwnPropertySymbols(e);i<n.length;i++)t.indexOf(n[i])<0&&Object.prototype.propertyIsEnumerable.call(e,n[i])&&(s[n[i]]=e[n[i]]);return s};class bMe extends go{}class AN extends rn{constructor(e){if(e.unroll)throw new ae("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ae("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new st({ndim:5})]}call(e,t){return C(()=>{if(this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new k("ConvRNN2D cell does not support constants");const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return C(()=>{const{stateSize:t}=this.cell,s=e.shape,n=this.computeSingleOutputShape(s),i=[n[0],...n.slice(2)],r=ye(i);return Array.isArray(t)?Array(t.length).fill(r):[r]})}resetStates(e,t=!1){C(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");const s=this.inputSpec[0].shape,n=this.computeSingleOutputShape(s),i=[n[0],...n.slice(2)],r=s[0];if(r==null)throw new k("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ye(i)):this.states_=[ye(i)];else if(e==null)ce(this.states_),this.keptStates!=null&&(ce(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ye(i)):this.states_[0]=ye(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new k(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ce(this.states_);for(let o=0;o<this.states_.length;++o){const a=e[o],l=i;if(!N.arraysEqual(a.shape,l))throw new k(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${a.shape}`);this.states_[o]=a}}this.states_=this.states_.map(o=>pt(o.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:s,kernelSize:n,padding:i,strides:r,dilationRate:o}=this.cell,a=t==="channelsFirst",l=e[a?3:2],c=e[a?4:3],p=Rs(l,n[0],i,r[0],o[0]),u=Rs(c,n[1],i,r[1],o[1]),h=[...e.slice(0,2),...a?[s,p,u]:[p,u,s]];return h}}AN.className="ConvRNN2D";class Tu extends Sa{constructor(e){const{filters:t,kernelSize:s,strides:n,padding:i,dataFormat:r,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,yt(this.filters,"filters"),this.kernelSize=fo(s,2,"kernelSize"),this.kernelSize.forEach(a=>yt(a,"kernelSize")),this.strides=fo(n||1,2,"strides"),this.strides.forEach(a=>yt(a,"strides")),this.padding=i||"valid",bs(this.padding),this.dataFormat=r||"channelsLast",lt(this.dataFormat),this.dilationRate=fo(o||1,2,"dilationRate"),this.dilationRate.forEach(a=>yt(a,"dilationRate"))}build(e){var t;e=Ue(e);const s=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[s]==null)throw new k(`The channel dimension of the input should be defined. Found ${e[s]}`);const n=e[s],i=4,r=this.kernelSize.concat([n,this.filters*i]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const o=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let a;if(this.unitForgetBias){const l=this.biasInitializer,c=this.filters;a=new(t=class extends zs{apply(u,h){const d=l.apply([c]),m=Kt([c]),f=l.apply([c*2]);return $l([d,m,f])}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,a,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return C(()=>{if(e.length!==3)throw new k(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const s=t.training||!1,n=e[0],i=e[1],r=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yi({ones:()=>Ot(n),rate:this.dropout,training:s,count:o}));const a=this.dropoutMask,l=(ie,ne,le)=>!ne||!ne[le]?ie:R(ne[le],ie);let c=l(n,a,0),p=l(n,a,1),u=l(n,a,2),h=l(n,a,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yi({ones:()=>Ot(i),rate:this.recurrentDropout,training:s,count:o}));const d=this.recurrentDropoutMask;let m=l(i,d,0),f=l(i,d,1),g=l(i,d,2),y=l(i,d,3);const w=3,[x,T,A,_]=Bt(this.kernel.read(),o,w),[E,F,D,M]=this.useBias?Bt(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,T,F,this.padding),u=this.inputConv(u,A,D,this.padding),h=this.inputConv(h,_,M,this.padding);const[P,B,Y,q]=Bt(this.recurrentKernel.read(),o,w);m=this.recurrentConv(m,P),f=this.recurrentConv(f,B),g=this.recurrentConv(g,Y),y=this.recurrentConv(y,q);const K=this.recurrentActivation.apply($(c,m)),H=this.recurrentActivation.apply($(p,f)),Q=$(R(H,r),R(K,this.activation.apply($(u,g)))),J=R(this.recurrentActivation.apply($(h,y)),this.activation.apply(Q));return[J,J,Q]})}getConfig(){const e=super.getConfig(),{units:t}=e,s=Gz(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},s,n)}inputConv(e,t,s,n){const i=rt(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return s?Cs(i,s,this.dataFormat):i}recurrentConv(e,t){const s=1;return rt(e,t,s,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Tu.className="ConvLSTM2DCell";V.registerClass(Tu);class Mm extends AN{constructor(e){const t=new Tu(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Mm.className="ConvLSTM2D";V.registerClass(Mm);class Au extends Le{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,s=[];for(let n=0;n<this.noiseShape.length;++n)s.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return s}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e);if(0<this.rate&&this.rate<1){const n=t.training==null?!1:t.training,i=this.getNoiseShape(s),r=lo(()=>qd(s,this.rate,i,this.seed),()=>s,n);return r}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Au.className="Dropout";V.registerClass(Au);class Um extends Au{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Um.className="SpatialDropout1D";V.registerClass(Um);class $m extends Le{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,yt(this.units,"units"),this.activation=Vn(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=at(e.kernelConstraint),this.biasConstraint=at(e.biasConstraint),this.kernelRegularizer=Ye(e.kernelRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.activityRegularizer=Ye(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ue(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Ue(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e),n=Ud(this.activation.getClassName());let i;return n!=null?i=Cn(s,this.kernel.read(),n,this.bias?this.bias.read():null):(i=Cn(s,this.kernel.read()),this.bias!=null&&(i=Cs(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:jn(this.activation),useBias:this.useBias,kernelInitializer:Ze(this.kernelInitializer),biasInitializer:Ze(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:ot(this.kernelConstraint),biasConstraint:ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}$m.className="Dense";V.registerClass($m);class Wm extends Le{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ue(e);for(const t of e.slice(1))if(t==null)throw new k(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Nn(e,1)]}call(e,t){return C(()=>{this.invokeCallHook(e,t);let s=we(e);if(this.dataFormat==="channelsFirst"&&s.rank>1){const n=[0];for(let i=2;i<s.rank;++i)n.push(i);n.push(1),s=s.transpose(n)}return TA(s)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}Wm.className="Flatten";V.registerClass(Wm);class zm extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vn(e.activation)}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e);return this.activation.apply(s)})}getConfig(){const e={activation:jn(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}zm.className="Activation";V.registerClass(zm);class Pm extends Le{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return C(()=>(e=we(e),IA(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Pm.className="RepeatVector";V.registerClass(Pm);class Bm extends Le{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const s="Total size of new array must be unchanged.",n=t.slice();let i=1,r=null;for(let a=0;a<n.length;++a){const l=n[a];if(this.isUnknown(l))if(r===null)r=a;else throw new k("Can only specifiy one unknown dimension.");else i*=l}const o=Nn(e);if(r!==null){if(i===0||o%i!==0)throw new k(s);n[r]=o/i}else if(o!==i)throw new k(s);return n}computeOutputShape(e){let t=!1;for(let s=0;s<e.length;++s)if(this.isUnknown(e[s])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e),n=s.shape,i=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return s.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}Bm.className="Reshape";V.registerClass(Bm);class jm extends Le{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=cs(1,e.dims.length+1);if(!N.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new st({ndim:this.dims.length+1})]}computeOutputShape(e){e=Ue(e);const t=e.slice();return this.dims.forEach((s,n)=>{t[n+1]=e[s]}),t}call(e,t){return se(we(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}jm.className="Permute";V.registerClass(jm);class Vm extends Le{constructor(e){super(e??{});this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const s=we(e),n=-1;return Zo(Js(s,this.maskValue),n)}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e),n=-1,i=!0,r=Zo(Js(s,this.maskValue),n,i),o=s.mul(r.asType(s.dtype));return o})}}Vm.className="Masking";V.registerClass(Vm);class Gm extends Le{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(He(e.inputLength))}this.inputDim=e.inputDim,yt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,yt(this.outputDim,"outputDim"),this.embeddingsInitializer=je(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ye(e.embeddingsRegularizer),this.activityRegularizer=Ye(e.activityRegularizer),this.embeddingsConstraint=at(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return C(()=>this.maskZero?(e=we(e),Js(e,re(e))):null)}computeOutputShape(e){if(e=Ue(e),this.inputLength==null)return[...e,this.outputDim];const t=He(this.inputLength);if(t.length!==e.length-1)throw new k(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let s=0;for(let n=0;n<t.length;++n){const i=t[n],r=e[n+1];if(i!=null&&r!=null&&i!==r)throw new k(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[s]=r),s++}}return[e[0],...t,this.outputDim]}call(e,t){return C(()=>{this.invokeCallHook(e,t);let s=we(e);s.dtype!=="int32"&&(s=ji(s,"int32"));const n=Gd(this.embeddings.read(),s.as1D());return n.reshape(Ue(this.computeOutputShape(s.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ze(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Gm.className="Embedding";V.registerClass(Gm);class Ia extends Le{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ae}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const s=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){const i=e[e.length-t.length+n],r=t[n];if(i==null||r==null||i<0||r<0)s.push(null);else if(i===1)s.push(r);else if(r===1)s.push(i);else{if(i!==r)throw new k("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));s.push(i)}}return s}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Ue(e)]),e=e,e.length<2)throw new k(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=An(t),t.length>1)throw new k(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let s=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const r=e[i]==null?null:e[i].slice(1);s=this.computeElementwiseOpOutputShape(s,r)}const n=e.map(i=>i.length);e.indexOf(null)===-1&&An(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return C(()=>{if(e=e,this.reshapeRequired){const s=[],n=e.map(i=>i.rank);if(n.indexOf(null)===-1){const i=Pn(n);for(let r of e){const o=r.rank;for(let a=0;a<i-o;++a)r=Vi(r,1);s.push(r)}return this.mergeFunction(s)}else{let i=!1;for(const a of e){const l=a.rank;if(l==null){const c=a.shape,p=c[0],u=c.slice(1).concat([p]);let h=a.reshape([p].concat(Nn(c.slice(1))));h=se(h,[1,0]),h=h.reshape(u),s.push(h),i=!0}else if(l>1){const c=cs(1,l).concat([0]);s.push(se(a,c)),i=!0}else s.push(a)}let r=this.mergeFunction(s);const o=r.rank;if(i){if(o==null){const a=r.shape,l=a.length,c=a[l-1],p=[c].concat(a.slice(0,a.length-1));r=se(r.reshape([-1,c]),[1,0]).reshape(p)}else if(o>1){const a=[o-1].concat(cs(0,o-1));r=se(r,a)}}return r}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){const i=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let s=[];for(const n of e)n!=null&&n[0]!==null&&s.push(n[0]);return s=An(s),s.length===1?t=s.concat(t):t=[null].concat(t),t}computeMask(e,t){return C(()=>{if(t==null)return null;if(!Array.isArray(t))throw new k("`mask` should be an Array");if(!Array.isArray(e))throw new k("`inputs` should be an Array");if(t.length!==e.length)throw new k(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Mt(n,0));let s=t[0];for(let n=1;n<t.length-1;++n)s=Yt(s,t[n]);return s})}}class qm extends Ia{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=$(t,e[s]);return t})}}qm.className="Add";V.registerClass(qm);class Hm extends Ia{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=R(t,e[s]);return t})}}Hm.className="Multiply";V.registerClass(Hm);class Ym extends Ia{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=$(t,e[s]);return R(1/e.length,t)})}}Ym.className="Average";V.registerClass(Ym);class Km extends Ia{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=Ht(t,e[s]);return t})}}Km.className="Maximum";V.registerClass(Km);class Xm extends Ia{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=bn(t,e[s]);return t})}}Xm.className="Minimum";V.registerClass(Xm);class Jm extends Ia{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new k("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const n of e)if(n!=null){t=!1;break}if(t)return;const s=[];for(let n=0;n<e.length;++n){const i=e[n].slice();i.splice(this.axis,1);let r=!1;for(const o of s)if(N.arraysEqual(o,i)){r=!0;break}r||s.push(i)}if(s.length>1)throw new k("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return C(()=>$l(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new k("A `Concatenate` layer should be called on a list of inputs.");const t=e,s=t[0].slice(),n=this.axis<0?s.length+this.axis:this.axis;for(const i of t.slice(1)){if(s[n]==null||i[n]==null){s[n]=null;break}s[n]+=i[n]}return s}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new k("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new k("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new k(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return C(()=>{let s=!0;if(t.forEach(r=>{if(r!=null){s=!1;return}}),s)return null;const n=[];for(let r=0;r<e.length;++r)t[r]==null?n.push(Ot(e[r]).asType("bool")):t[r].rank<e[r].rank?n.push(Mt(t[r],-1)):n.push(t[r]);const i=be(n,this.axis);return hl(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Jm.className="Concatenate";V.registerClass(Jm);function Nu(e,t){for(;e<0;)e+=t;return e}function qz(e,t,s){if(e.shape.length>3||t.shape.length>3)throw new ae("batchDot is not implemented for tensors of 4D or higher rank yet");if(N.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),N.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof s=="number"&&(s=[s,s]),e.dtype==="complex64"||t.dtype==="complex64")throw new ae("batchDot is not implemented for complex64-type Tensors yet.");const n=e.shape.length,i=t.shape.length;s==null&&(s=[n-1,i-2]);const r=s;return C(()=>{let o;if(n>i){o=n-i;const l=[];for(let c=0;c<o;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(i>n){o=i-n;const l=[];for(let c=0;c<o;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else o=0;let a;if(e.shape.length===2&&t.shape.length===2)r[0]===r[1]?a=e.mul(t).sum(r[0]):a=e.transpose([1,0]).mul(t).sum(r[1]);else{const l=r[0]!==e.shape.length-1,c=r[1]===t.shape.length-1;a=e.matMul(t,l,c)}if(o>0){let l;n>i?l=n+i-3:l=n-1;const c=[];for(let p=l;p<l+o;++p)c.push(p);a=a.squeeze(c)}return a.shape.length===1&&(a=a.expandDims(1)),a})}class Zm extends Ia{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){N.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],s=e[1];if(t.length>3||s.length>3)throw new ae("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(t,s);if(t[n[0]]!==s[n[1]])throw new k(`Dimension incompatibility: ${t[n[0]]} !== ${s[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new k(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],s=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((i,r)=>Nu(i,e[r].shape.length)):n=[Nu(this.axes,t.shape.length),Nu(this.axes,s.shape.length)],this.normalize&&(t=pu(t,n[0]),s=pu(s,n[1])),qz(t,s,n)}interpretAxes(e,t){let s;return Array.isArray(this.axes)?s=this.axes:s=[Nu(this.axes,e.length),Nu(this.axes,t.length)],s}computeOutputShape(e){N.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),s=e[1].slice();if(t.length>3||s.length>3)throw new ae("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(t,s);t.splice(n[0],1),s.splice(n[1],1),s.splice(0,1);const i=t.concat(s);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}Zm.className="Dot";V.registerClass(Zm);class Qm extends Le{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e),n=()=>Wl(s.shape,0,this.stddev).add(s),i=lo(n,()=>s,t.training||!1);return i})}}Qm.className="GaussianNoise";V.registerClass(Qm);class ef extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e);if(this.rate>0&&this.rate<1){const n=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return s.mul(Wl(s.shape,1,i))};return lo(n,()=>s,t.training||!1)}return s})}}ef.className="GaussianDropout";V.registerClass(ef);class tf extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||we(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return C(()=>{if(this.rate<1&&this.rate>0){const s=this._getNoiseShape(e),n=()=>{const i=we(e),r=1.6732632423543772,o=1.0507009873554805,a=-r*o;let l=ms(wn(s),this.rate);l=ji(l,"float32");const c=((1-this.rate)*(1+this.rate*a**2))**-.5,p=-c*a*this.rate,u=i.mul(l).add(l.add(-1).mul(a));return u.mul(c).add(p)};return lo(n,()=>we(e),t.training||!1)}return e})}}tf.className="AlphaDropout";V.registerClass(tf);function Cu(e,t,s,n,i,r=.001){let o;if(e.rank===2)o=Yh(e,t,s,n,i,r);else if(e.rank===3)o=Kh(e,t,s,n,i,r);else if(e.rank===4)o=Xh(e,t,s,n,i,r);else throw new ae(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function Hz(e,t,s,n,i=.001){return C(()=>{const r=na(e,n),o=r.mean,a=r.variance,l=Cu(e,o,a,s,t,i);return[l,o,a]})}function Yz(e,t,s,n,i=.001){return C(()=>{const r=na(e,n),o=r.mean,a=r.variance,l=[];for(const m of cs(0,e.rank))n.indexOf(m)!==-1?l.push(1):l.push(e.shape[m]);const c=o.reshape(l),p=a.reshape(l),u=t==null?null:t.reshape(l),h=s==null?null:s.reshape(l),d=Cu(e,c,p,h,u,i);return[d,o,a]})}function Kz(e,t,s,n,i=.001){return N.arraysEqual(n.slice().sort(),cs(0,e.rank-1))?Hz(e,t,s,n,i):Yz(e,t,s,n,i)}class sf extends Le{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=je(e.betaInitializer||"zeros"),this.gammaInitializer=je(e.gammaInitializer||"ones"),this.movingMeanInitializer=je(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(e.movingVarianceInitializer||"ones"),this.betaConstraint=at(e.betaConstraint),this.gammaConstraint=at(e.gammaConstraint),this.betaRegularizer=Ye(e.betaRegularizer),this.gammaRegularizer=Ye(e.gammaRegularizer)}build(e){e=Ue(e);const t=this.axis>=0?this.axis:this.axis+e.length,s=e[t];if(s==null)throw new k(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new st({ndim:e.length,axes:{[t]:s}})];const n=[s];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return C(()=>{const s=t.training==null?!1:t.training,n=we(e),i=n.shape,r=i.length,o=cs(0,r),a=this.axis>=0?this.axis:this.axis+r;o.splice(a,1);const l=In(1,r);l[a]=i[a];const c=o.slice();c.sort();const p=!N.arraysEqual(c,cs(0,r).slice(0,r-1)),u=()=>{if(p){const y=this.movingMean.read().reshape(l),w=this.movingVariance.read().reshape(l),x=this.center?this.beta.read().reshape(l):null,T=this.scale?this.gamma.read().reshape(l):null;return Cu(n,y,w,x,T,this.epsilon)}else return Cu(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!s)return u();const[h,d,m]=Kz(n,this.gamma.read(),this.beta.read(),o,this.epsilon),f=(y,w,x)=>{C(()=>{const T=1-x,A=y.read(),_=A.sub(w).mul(T);y.write(A.sub(_))})},g=()=>{f(this.movingMean,d,this.momentum),f(this.movingVariance,m,this.momentum)};return g(),h})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ze(this.betaInitializer),gammaInitializer:Ze(this.gammaInitializer),movingMeanInitializer:Ze(this.movingMeanInitializer),movingVarianceInitializer:Ze(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:ot(this.betaConstraint),gammaConstraint:ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}sf.className="BatchNormalization";V.registerClass(sf);class nf extends Le{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=je(e.betaInitializer||"zeros"),this.gammaInitializer=je(e.gammaInitializer||"ones"),this.betaRegularizer=Ye(e.betaRegularizer),this.gammaRegularizer=Ye(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Ue(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==An(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const s=this.axis.map(i=>e[i]),n=!0;this.scale?this.gamma=this.addWeight("gamma",s,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",s,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){const s=we(e),n=s.shape,i=n.length;return C(()=>{const r=!0;let{mean:o,variance:a}=na(s,this.axis,r);const l=In(1,i);for(const m of this.axis)l[m]=n[m];const c=m=>m!=null&&m.shape.length!==i&&this.axis!==[i-1]?m.reshape(l):m;let p=c(this.gamma.read()),u=c(this.beta.read());const h=[],d=[];for(let m=0;m<i;++m)this.axis.indexOf(m)!==-1?(h.push(n[m]),d.push(1)):(h.push(1),d.push(n[m]));return o=o.tile(h),a=a.tile(h),p=p.tile(d),u=u.tile(d),Cu(s,o,a,u,p,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ze(this.betaInitializer),gammaInitializer:Ze(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}nf.className="LayerNormalization";V.registerClass(nf);function Xz(e,t,s){return C(()=>{if(e.rank!==4)throw new k(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new k("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(s==null&&(s=ys()),s!=="channelsLast"&&s!=="channelsFirst")throw new k(`Unknown data format: ${s}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return s==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Pt(e,n)})}class rf extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ys():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new k(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,s;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],s=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new k(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new k(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);s=e.padding[1]}this.padding=[t,s]}this.inputSpec=[new st({ndim:4})]}computeOutputShape(e){e=Ue(e);let t,s;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?s=e[3]+this.padding[1][0]+this.padding[1][1]:s=null,[e[0],e[1],t,s]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?s=e[2]+this.padding[1][0]+this.padding[1][1]:s=null,[e[0],t,s,e[3]])}call(e,t){return C(()=>Xz(we(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}rf.className="ZeroPadding2D";V.registerClass(rf);function of(e,t,s,n,i,r){return C(()=>{lt(i),Pb(r),bs(n),s==null&&(s=[1,1]),n==null&&(n="valid"),i==null&&(i=ys()),r==null&&(r="max"),e=bu(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=ft(e,t,s,a):o=ds(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}function NN(e,t,s,n,i,r){return C(()=>{lt(i),Pb(r),bs(n),s==null&&(s=[1,1,1]),n==null&&(n="valid"),i==null&&(i=ys()),r==null&&(r="max"),e=rw(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=Tl(e,t,s,a):o=ml(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,4,1,2,3])),o})}class CN extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new k(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(yt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new k(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,bs(this.padding),this.inputSpec=[new st({ndim:3})]}computeOutputShape(e){e=Ue(e);const t=Rs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return C(()=>{this.invokeCallHook(e,t),e=Vi(we(e),2);const s=this.poolingFunction(we(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Qs(s,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class af extends CN{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),of(e,t,s,n,i,"max")}}af.className="MaxPooling1D";V.registerClass(af);class lf extends CN{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),of(e,t,s,n,i,"avg")}}lf.className="AveragePooling1D";V.registerClass(lf);class RN extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new k(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];yt(this.poolSize,"poolSize"),yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,lt(this.dataFormat),bs(this.padding),this.inputSpec=[new st({ndim:4})]}computeOutputShape(e){e=Ue(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Rs(t,this.poolSize[0],this.padding,this.strides[0]),s=Rs(s,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,s]:[e[0],t,s,e[3]]}call(e,t){return C(()=>(this.invokeCallHook(e,t),this.poolingFunction(we(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class cf extends RN{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),of(e,t,s,n,i,"max")}}cf.className="MaxPooling2D";V.registerClass(cf);class pf extends RN{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),of(e,t,s,n,i,"avg")}}pf.className="AveragePooling2D";V.registerClass(pf);class ON extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new k(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];yt(this.poolSize,"poolSize"),yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,lt(this.dataFormat),bs(this.padding),this.inputSpec=[new st({ndim:5})]}computeOutputShape(e){e=Ue(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Rs(t,this.poolSize[0],this.padding,this.strides[0]),s=Rs(s,this.poolSize[1],this.padding,this.strides[1]),n=Rs(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,s,n]:[e[0],t,s,n,e[4]]}call(e,t){return C(()=>(this.invokeCallHook(e,t),this.poolingFunction(we(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class uf extends ON{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),NN(e,t,s,n,i,"max")}}uf.className="MaxPooling3D";V.registerClass(uf);class hf extends ON{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return lt(i),bs(n),NN(e,t,s,n,i,"avg")}}hf.className="AveragePooling3D";V.registerClass(hf);class EN extends Le{constructor(e){super(e);this.inputSpec=[new st({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ae}}class df extends EN{constructor(e){super(e||{})}call(e,t){return C(()=>{const s=we(e);return Xe(s,1)})}}df.className="GlobalAveragePooling1D";V.registerClass(df);class mf extends EN{constructor(e){super(e||{})}call(e,t){return C(()=>{const s=we(e);return Lt(s,1)})}}mf.className="GlobalMaxPooling1D";V.registerClass(mf);class _N extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,lt(this.dataFormat),this.inputSpec=[new st({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ae}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class ff extends _N{call(e,t){return C(()=>{const s=we(e);return this.dataFormat==="channelsLast"?Xe(s,[1,2]):Xe(s,[2,3])})}}ff.className="GlobalAveragePooling2D";V.registerClass(ff);class gf extends _N{call(e,t){return C(()=>{const s=we(e);return this.dataFormat==="channelsLast"?Lt(s,[1,2]):Lt(s,[2,3])})}}gf.className="GlobalMaxPooling2D";V.registerClass(gf);class kN extends Le{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,s={}){const n=t.layer,i=Ls(n,s);delete t.layer;const r={layer:i};return Object.assign(r,t),new e(r)}}class yf extends kN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Ue(e),e.length<3)throw new k(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Ue(e);const t=[e[0]].concat(e.slice(2)),s=this.layer.computeOutputShape(t),n=e[1];return[s[0],n].concat(s.slice(1))}call(e,t){return C(()=>{e=we(e);const s=(r,o)=>{const a=we(this.layer.call(r,t));return[a,[]]},n=aw(s,e,[],!1,null,null,!1,!0),i=n[1];return i})}}yf.className="TimeDistributed";V.registerClass(yf);function Jz(e){Bi(bA,"BidirectionalMergeMode",e)}const Zz="concat";class bf extends kN{constructor(e){super(e);const t=e.layer.getConfig(),s={};s.className=e.layer.getClassName(),s.config=t,this.forwardLayer=Ls(s),t.goBackwards=!(t.goBackwards===!0);const n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Ls(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Zz:e.mergeMode,Jz(this.mergeMode),e.weights)throw new ae("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,s=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,s)),this.backwardLayer.setWeights(e.slice(s))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let s,n,i;return this.returnState&&(i=t.slice(1)),s=t[0],s=s,this.mergeMode==="concat"?(s[s.length-1]*=2,n=[s]):this.mergeMode==null?n=[s,s.slice()]:n=[s],this.returnState?this.mergeMode==null?n.concat(i).concat(i.slice()):[s].concat(i).concat(i.slice()):jt(n)}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=ow(e,s,n,this.numConstants);if(e=i.inputs,s=i.initialState,n=i.constants,Array.isArray(e)&&(s=e.slice(1),e=e[0]),(s==null||s.length===0)&&n==null)return super.apply(e,t);const r=[],o=[];if(s!=null){const l=s.length;if(l%2>0)throw new k("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=s,r.push(...s);const c=s.map(p=>new st({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(n!=null)throw new ae("Support for constants in Bidirectional layers is not implemented yet.");const a=r[0]instanceof xs;for(const l of r)if(l instanceof xs!==a)throw new k("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(a){const l=[e].concat(r),c=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=c;const u=super.apply(l,t);return this.inputSpec=p,u}else return super.apply(e,t)}call(e,t){return C(()=>{const s=t.initialState;let n,i;if(s==null)n=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const a=s.slice(0,s.length/2),l=s.slice(s.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:a})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let r;this.returnState&&(Array.isArray(n)&&(r=n.slice(1).concat(i.slice(1))),n=n[0],i=i[0]),this.returnSequences&&(i=Et(i,1));let o;return this.mergeMode==="concat"?o=$l([n,i]):this.mergeMode==="sum"?o=$(n,i):this.mergeMode==="ave"?o=R(.5,$(n,i)):this.mergeMode==="mul"?o=R(n,i):this.mergeMode==null&&(o=[n,i]),this.returnState?this.mergeMode==null?o.concat(r):[o].concat(r):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){zn(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),zn(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let s;if(this.returnSequences?this.mergeMode==null?s=[t,t]:s=t:this.mergeMode==null?s=[null,null]:s=null,this.returnState){const n=this.forwardLayer.states,i=n.map(r=>null);return Array.isArray(s)?s.concat(i).concat(i):[s].concat(i).concat(i)}else return s}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const s=Ls(t.layer);if(delete t.layer,t.numConstants!=null)throw new ae("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const n=t;return n.layer=s,new e(n)}}bf.className="Bidirectional";V.registerClass(bf);const lw={};Ee(lw,{Layer:()=>Le,RNN:()=>rn,RNNCell:()=>go,activation:()=>mP,add:()=>IP,alphaDropout:()=>oB,average:()=>vP,averagePooling1d:()=>cw,averagePooling2d:()=>pw,averagePooling3d:()=>uw,avgPool1d:()=>kP,avgPool2d:()=>FP,avgPool3d:()=>UP,avgPooling1d:()=>DP,avgPooling2d:()=>MP,avgPooling3d:()=>$P,batchNormalization:()=>OP,bidirectional:()=>ZP,concatenate:()=>TP,conv1d:()=>oP,conv2d:()=>aP,conv2dTranspose:()=>lP,conv3d:()=>cP,convLstm2d:()=>YP,convLstm2dCell:()=>KP,cropping2D:()=>uP,dense:()=>fP,depthwiseConv2d:()=>dP,dot:()=>RP,dropout:()=>gP,elu:()=>eP,embedding:()=>SP,flatten:()=>bP,gaussianDropout:()=>rB,gaussianNoise:()=>iB,globalAveragePooling1d:()=>WP,globalAveragePooling2d:()=>zP,globalMaxPool1d:()=>eB,globalMaxPool2d:()=>tB,globalMaxPooling1d:()=>DN,globalMaxPooling2d:()=>FN,gru:()=>BP,gruCell:()=>jP,input:()=>wm,inputLayer:()=>Qz,layerNormalization:()=>EP,leakyReLU:()=>sP,lstm:()=>VP,lstmCell:()=>GP,masking:()=>aB,maxPool1d:()=>sB,maxPool2d:()=>nB,maxPooling1d:()=>MN,maxPooling2d:()=>UN,maxPooling3d:()=>PP,maximum:()=>AP,minimum:()=>NP,multiply:()=>CP,permute:()=>LP,prelu:()=>nP,reLU:()=>tP,repeatVector:()=>wP,reshape:()=>xP,rnn:()=>XP,separableConv2d:()=>pP,simpleRNN:()=>qP,simpleRNNCell:()=>HP,softmax:()=>iP,spatialDropout1d:()=>yP,stackedRNNCells:()=>JP,thresholdedReLU:()=>rP,timeDistributed:()=>QP,upSampling2d:()=>hP,zeroPadding2d:()=>_P});function 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xw={};Ee(xw,{json:()=>RB});const RB=[{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListSetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListReserve",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListFromTensor",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListStack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"},{tfName:"num_elements",name:"numElements",type:"dtype"}]},{tfOpName:"TensorListSplit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"},{start:2,name:"lengths",type:"number[]"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListConcat",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}],attrs:[{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPopBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPushBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]}];const Lw={};Ee(Lw,{json:()=>OB});const OB=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"FusedDepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]}]},{tfOpName:"Conv3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"Dilation2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"rates",name:"dilations",type:"number[]"},{tfName:"padding",name:"pad",type:"string"}]}];const Sw={};Ee(Sw,{json:()=>EB});const EB=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}];const Iw={};Ee(Iw,{json:()=>_B});const _B=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const vw={};Ee(vw,{json:()=>kB});const kB=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}];const Tw={};Ee(Tw,{json:()=>DB});const DB=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];const Aw={};Ee(Aw,{json:()=>FB});const FB=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];const Nw={};Ee(Nw,{json:()=>MB});const MB=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const Cw={};Ee(Cw,{json:()=>UB});const UB=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const Rw={};Ee(Rw,{json:()=>$B});const $B=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];const Ow={};Ee(Ow,{json:()=>WB});const WB=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];const Ew={};Ee(Ew,{json:()=>zB});const zB=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];const _w={};Ee(_w,{json:()=>PB});const PB=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];const kw={};Ee(kw,{json:()=>BB});const BB=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];class Fw{static get Instance(){return this._instance||(this._instance=new this)}constructor(){const e=[bw,ww,xw,Lw,Sw,Iw,vw,Nw,Aw,Tw,Cw,Rw,Ow,Ew,_w,kw],t=[].concat(...e.map(s=>s.json));this.opMappers=t.reduce((s,n)=>(s[n.tfOpName]=n,s),{})}transformGraph(e,t={}){const s=e.node,n=[],i=[],r=[],o=s.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op.startsWith("Placeholder")?n.push(m[f.name]):f.op==="Const"?i.push(m[f.name]):(f.input==null||f.input.length===0)&&r.push(m[f.name]),m),{});let a=[];const l=[];let c={},p={};t!=null&&(c=this.mapSignatureEntries(t.inputs),p=this.mapSignatureEntries(t.outputs));const u=Object.keys(o);u.forEach(m=>{const f=o[m];f.inputNames.forEach(g=>{const[y]=Gn(g);f.inputs.push(o[y]),o[y].children.push(f)})}),Object.keys(p).length===0?u.forEach(m=>{const f=o[m];f.children.length===0&&l.push(f)}):Object.keys(p).forEach(m=>{const[f]=Gn(m),g=o[f];g!=null&&(g.signatureKey=p[m],l.push(g))}),Object.keys(c).length>0?Object.keys(c).forEach(m=>{const[f]=Gn(m),g=o[f];g&&(g.signatureKey=c[m],a.push(g))}):a=n;let h={};e.library!=null&&e.library.function!=null&&(h=e.library.function.reduce((m,f)=>(m[f.signature.name]=this.mapFunction(f),m),{}));const d={nodes:o,inputs:a,outputs:l,weights:i,placeholders:n,signature:t,functions:h};return r.length>0&&(d.initNodes=r),d}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,s)=>(t[e[s].name]=s,t),{})}mapNode(e){const t=xf(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});const s={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(n=>n.startsWith("^")?n.substr(1):n),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(s.inputParams=t.inputs.reduce((n,i)=>(n[i.name]={type:i.type,inputIndexStart:i.start,inputIndexEnd:i.end},n),{})),t.attrs!=null&&(s.attrParams=t.attrs.reduce((n,i)=>{const r=i.type;let o;switch(i.type){case"string":o=Sf(e.attr,i.tfName,i.defaultValue),o===void 0&&!!i.tfDeprecatedName&&(o=Sf(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"string[]":o=Rf(e.attr,i.tfName,i.defaultValue),o===void 0&&!!i.tfDeprecatedName&&(o=Rf(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number":o=vf(e.attr,i.tfName,i.defaultValue||0),o===void 0&&!!i.tfDeprecatedName&&(o=vf(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number[]":o=Cf(e.attr,i.tfName,i.defaultValue),o===void 0&&!!i.tfDeprecatedName&&(o=Cf(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool":o=If(e.attr,i.tfName,i.defaultValue),o===void 0&&!!i.tfDeprecatedName&&(o=If(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool[]":o=Ef(e.attr,i.tfName,i.defaultValue),o===void 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i={};t!=null&&(i=t.reduce((p,u)=>(p[u.name]=this.mapNode(u),u.op==="Const"&&n.push(p[u.name]),p),{}));const r=[],o=[];e.signature.inputArg.forEach(p=>{const[u]=Gn(p.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Dw(p.type),type:"dtype"}},children:[]};h.signatureKey=p.name,r.push(h),i[u]=h});const a=Object.keys(i);a.forEach(p=>{const u=i[p];u.inputNames.forEach(h=>{const[d]=Gn(h);u.inputs.push(i[d]),i[d].children.push(u)})});const l=e.ret;e.signature.outputArg.forEach(p=>{const[u,h]=Gn(l[p.name]),d=i[u];d!=null&&(d.defaultOutput=h,o.push(d))});const c=this.mapArgsToSignature(e);return{nodes:i,inputs:r,outputs:o,weights:n,placeholders:s,signature:c}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,s)=>(t[s.name]=this.mapArgToTensorInfo(s),t),{}),outputs:e.signature.outputArg.reduce((t,s)=>(t[s.name]=this.mapArgToTensorInfo(s,e.ret),t),{})}}mapArgToTensorInfo(e,t){let 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+          tensor.shape[0], but sum of lengths is
+        ${s}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=s===0?0:t.size/s,r=[];C(()=>{t=O(t,[1,s,i]);for(let a=0;a<e.length;++a){const l=a===0?0:n[a-1],c=[0,l,0],p=[1,e[a],i];r[a]=O(he(t,c,p),this.elementShape)}return r});const o=[];for(let a=0;a<e.length;a++)o[a]=a;this.writeMany(o,r)}}class Ou{constructor(e,t,s,n=-1){this.tensors=e,this.elementShape=t,this.elementDtype=s,e!=null&&e.forEach(i=>{if(s!==i.dtype)throw new Error(`Invalid data types; op elements ${s}, but list elements ${i.dtype}`);Bs(t,i.shape,"TensorList shape mismatch: "),pt(i)}),this.idTensor=j(0),this.maxNumElements=n,pt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ou([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,s=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(s!==-1&&this.tensors.length!==s)throw new Error(`Operation expected a list with ${s} elements but got a list with ${this.tensors.length} elements.`);return Bs(e,this.elementShape,"TensorList shape mismatch: "),C(()=>{const n=this.tensors.map(i=>O(i,e));return Ge(n,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const s=this.tensors.pop();return Bs(s.shape,e,"TensorList shape mismatch: "),O(s,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Bs(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");pt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,s){if(s!==this.elementDtype)throw new Error(`Invalid data types; op elements ${s}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Bs(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Bs(this.elementShape,t.shape,"TensorList shape mismatch: "),pt(t),this.tensors[e]=t}gather(e,t,s){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Bs(this.elementShape,s,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?ze([],[0].concat(this.elementShape)):C(()=>{const n=e.map(i=>O(this.tensors[i],s));return Ge(n,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Bs(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?ze([],[0].concat(this.elementShape)):C(()=>{const s=this.tensors.map(n=>O(n,t));return be(s,0)})}}function JN(e,t,s){const n=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==s)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${s}`);const i=e.shape.slice(1);Bs(i,t,"TensorList shape mismatch: ");const r=qe(e);return new Ou(r,t,n)}function ZN(e,t,s){return new Ou([],e,t,s)}function QN(e,t,s,n){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(n!=null&&n!==-1&&i>=n)throw new Error(`Max index must be < array size (${i}  vs. ${n})`);const r=new Ou([],s,e.dtype,n),o=qe(e,0);return t.forEach((a,l)=>{r.setItem(a,o[l])}),r}function eC(e,t,s){let n=0;const i=t.map(l=>(n+=l,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${n}, and tensor's shape is: ${e.shape}`);const r=n===0?0:e.size/n,o=C(()=>{const l=[];e=O(e,[1,n,r]);for(let c=0;c<t.length;++c){const p=c===0?0:i[c-1],u=[0,p,0],h=[1,t[c],r];l[c]=O(he(e,u,h),s)}return e.dispose(),l}),a=new Ou([],s,e.dtype,t.length);for(let l=0;l<o.length;l++)a.setItem(l,o[l]);return a}const tC=async(e,t,s)=>{switch(e.op){case"If":case"StatelessIf":{const n=L("thenBranch",e,t,s),i=L("elseBranch",e,t,s),r=L("cond",e,t,s),o=L("args",e,t,s),a=await r.data();return a[0]?s.functionMap[n].executeFunctionAsync(o,s.tensorArrayMap,s.tensorListMap):s.functionMap[i].executeFunctionAsync(o,s.tensorArrayMap,s.tensorListMap)}case"While":case"StatelessWhile":{const n=L("body",e,t,s),i=L("cond",e,t,s),r=L("args",e,t,s),o=await s.functionMap[i].executeFunctionAsync(r,s.tensorArrayMap,s.tensorListMap),a=r.map(p=>p.id);let l=await o[0].data();o.forEach(p=>{!p.kept&&a.indexOf(p.id)===-1&&p.dispose()});let c=r;for(;l[0];){const p=c;c=await s.functionMap[n].executeFunctionAsync(c,s.tensorArrayMap,s.tensorListMap);const u=c.map(d=>d.id);p.forEach(d=>{!d.kept&&a.indexOf(d.id)===-1&&u.indexOf(d.id)===-1&&d.dispose()});const h=await s.functionMap[i].executeFunctionAsync(c,s.tensorArrayMap,s.tensorListMap);l=await h[0].data(),h.forEach(d=>{!d.kept&&a.indexOf(d.id)===-1&&u.indexOf(d.id)===-1&&d.dispose()})}return c}case"LoopCond":{const n=L("pred",e,t,s);return[qn(n)]}case"Switch":{const n=L("pred",e,t,s);let i=L("data",e,t,s);return i.kept||(i=qn(i)),(await n.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const n=e.inputNames.find(i=>Vt(i,t,s)!==void 0);if(n){const i=Vt(n,t,s);return[qn(i)]}return}case"Enter":{const n=L("frameName",e,t,s),i=L("tensor",e,t,s);return s.enterFrame(n),[qn(i)]}case"Exit":{const n=L("tensor",e,t,s);return s.exitFrame(),[qn(n)]}case"NextIteration":{const n=L("tensor",e,t,s);return s.nextIteration(),[qn(n)]}case"TensorArrayV3":{const n=L("size",e,t,s),i=L("dtype",e,t,s),r=L("elementShape",e,t,s),o=L("dynamicSize",e,t,s),a=L("clearAfterRead",e,t,s),l=L("identicalElementShapes",e,t,s),c=L("name",e,t,s),p=new XN(c,i,n,r,l,o,a);return s.addTensorArray(p),[p.idTensor,j(1)]}case"TensorArrayWriteV3":{const n=L("tensorArrayId",e,t,s),i=L("index",e,t,s),r=L("tensor",e,t,s),o=s.getTensorArray(n.id);return o.write(i,r),[o.idTensor]}case"TensorArrayReadV3":{const n=L("tensorArrayId",e,t,s),i=L("index",e,t,s),r=s.getTensorArray(n.id);return[r.read(i)]}case"TensorArrayGatherV3":{const n=L("tensorArrayId",e,t,s),i=L("indices",e,t,s),r=L("dtype",e,t,s),o=s.getTensorArray(n.id);return[o.gather(i,r)]}case"TensorArrayScatterV3":{const n=L("tensorArrayId",e,t,s),i=L("indices",e,t,s),r=L("tensor",e,t,s),o=s.getTensorArray(n.id);return o.scatter(i,r),[o.idTensor]}case"TensorArrayConcatV3":{const n=L("tensorArrayId",e,t,s),i=s.getTensorArray(n.id),r=L("dtype",e,t,s);return[i.concat(r)]}case"TensorArraySplitV3":{const 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n=L("elementShape",e,t,s),i=L("elementDType",e,t,s),r=L("numElements",e,t,s),o=ZN(n,i,r);return s.addTensorList(o),[o.idTensor]}case"TensorListGather":{const n=L("tensorListId",e,t,s),i=L("indices",e,t,s),r=L("elementShape",e,t,s),o=L("elementDType",e,t,s),a=s.getTensorList(n.id);return[a.gather(i,o,r)]}case"TensorListStack":{const n=L("tensorListId",e,t,s),i=L("elementShape",e,t,s),r=L("elementDType",e,t,s),o=L("numElements",e,t,s),a=s.getTensorList(n.id);return[a.stack(i,r,o)]}case"TensorListFromTensor":{const n=L("tensor",e,t,s),i=L("elementShape",e,t,s),r=L("elementDType",e,t,s),o=JN(n,i,r);return s.addTensorList(o),[o.idTensor]}case"TensorListConcat":{const n=L("tensorListId",e,t,s),i=s.getTensorList(n.id),r=L("dtype",e,t,s),o=L("elementShape",e,t,s);return[i.concat(r,o)]}case"TensorListPushBack":{const n=L("tensorListId",e,t,s),i=L("tensor",e,t,s),r=s.getTensorList(n.id);return r.pushBack(i),[r.idTensor]}case"TensorListPopBack":{const 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n=L("strides",e,t,s),i=L("pad",e,t,s),r=L("kernelSize",e,t,s);return[ml(L("x",e,t,s),[r[1],r[2],r[3]],[n[1],n[2],n[3]],i)]}case"MaxPool3D":{const n=L("strides",e,t,s),i=L("pad",e,t,s),r=L("kernelSize",e,t,s);return[Tl(L("x",e,t,s),[r[1],r[2],r[3]],[n[1],n[2],n[3]],i)]}case"Dilation2D":{const n=L("strides",e,t,s),i=L("pad",e,t,s),r=L("dilations",e,t,s),o=n[1],a=n[2],l=r[1],c=r[2];return[id(L("x",e,t,s),L("filter",e,t,s),[o,a],i,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const iC=(e,t,s)=>{switch(e.op){case"Fill":{const n=L("shape",e,t,s),i=L("dtype",e,t,s),r=L("value",e,t,s);return[Wt(n,r,i)]}case"LinSpace":{const n=L("start",e,t,s),i=L("stop",e,t,s),r=L("num",e,t,s);return[cd(n,i,r)]}case"Multinomial":{const n=L("logits",e,t,s),i=L("numSamples",e,t,s),r=L("seed",e,t,s);return[fd(n,i,r)]}case"OneHot":{const 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cC=(e,t,s)=>{switch(e.op){case"Equal":return[as(L("a",e,t,s),L("b",e,t,s))];case"NotEqual":return[Js(L("a",e,t,s),L("b",e,t,s))];case"Greater":return[Ut(L("a",e,t,s),L("b",e,t,s))];case"GreaterEqual":return[ms(L("a",e,t,s),L("b",e,t,s))];case"Less":return[Xr(L("a",e,t,s),L("b",e,t,s))];case"LessEqual":return[Ws(L("a",e,t,s),L("b",e,t,s))];case"LogicalAnd":return[Yt(L("a",e,t,s),L("b",e,t,s))];case"LogicalNot":return[Jr(L("a",e,t,s))];case"LogicalOr":return[vl(L("a",e,t,s),L("b",e,t,s))];case"Select":case"SelectV2":return[mt(L("condition",e,t,s),L("a",e,t,s),L("b",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const pC=(e,t,s)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Te(L("a",e,t,s),L("b",e,t,s),L("transposeA",e,t,s),L("transposeB",e,t,s))];case"Transpose":return[se(L("x",e,t,s),L("perm",e,t,s))];case"_FusedMatMul":const[n,i]=L("fusedOps",e,t,s),r=n==="biasadd",o=i==="prelu",a=L("numArgs",e,t,s);if(r){if(o&&a!==2)throw new 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uC=(e,t,s)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Xs(L("x",e,t,s),L("mean",e,t,s),L("variance",e,t,s),L("offset",e,t,s),L("scale",e,t,s),L("epsilon",e,t,s))];case"FusedBatchNormV3":return[Xs(L("x",e,t,s),L("mean",e,t,s),L("variance",e,t,s),L("offset",e,t,s),L("scale",e,t,s),L("epsilon",e,t,s))];case"LRN":return[pd(L("x",e,t,s),L("radius",e,t,s),L("bias",e,t,s),L("alpha",e,t,s),L("beta",e,t,s))];case"Softmax":return[ts(L("x",e,t,s))];case"LogSoftmax":return[Il(L("x",e,t,s))];case"SparseToDense":return[Xp(L("sparseIndices",e,t,s),L("outputShape",e,t,s),L("sparseValues",e,t,s),L("defaultValue",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const hC=(e,t,s)=>{switch(e.op){case"Max":{const n=L("axis",e,t,s),i=L("keepDims",e,t,s);return[Lt(L("x",e,t,s),n,i)]}case"Mean":{const n=L("axis",e,t,s),i=L("keepDims",e,t,s);return[Xe(L("x",e,t,s),n,i)]}case"Min":{const 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n=L("axis",e,t,s);return[Qs(L("x",e,t,s),n)]}case"Reshape":return[O(L("x",e,t,s),L("shape",e,t,s))];case"PadV2":case"Pad":return[Pt(L("x",e,t,s),L("padding",e,t,s),L("constantValue",e,t,s))];case"SpaceToBatchND":{const n=L("blockShape",e,t,s),i=L("paddings",e,t,s);return[Zr(L("x",e,t,s),n,i)]}case"BatchToSpaceND":{const n=L("blockShape",e,t,s),i=L("crops",e,t,s);return[Yr(L("x",e,t,s),n,i)]}case"DepthToSpace":{const n=L("blockSize",e,t,s),i=L("dataFormat",e,t,s).toUpperCase();return[nd(L("x",e,t,s),n,i)]}case"BroadcastTo":return[ta(L("x",e,t,s),L("shape",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Uw(e,t,s){const n=((i,r,o)=>{switch(i.category){case"arithmetic":return C(()=>YN(i,r,o));case"basic_math":return C(()=>KN(i,r,o));case"control":return tC(i,r,o);case"convolution":return C(()=>nC(i,r,o));case"creation":return C(()=>iC(i,r,o));case"dynamic":return rC(i,r,o);case"evaluation":return C(()=>oC(i,r,o));case"image":return C(()=>lC(i,r,o));case"graph":return C(()=>aC(i,r,o));case"logical":return C(()=>cC(i,r,o));case"matrices":return C(()=>pC(i,r,o));case"normalization":return C(()=>uC(i,r,o));case"reduction":return C(()=>hC(i,r,o));case"slice_join":return C(()=>dC(i,r,o));case"spectral":return C(()=>mC(i,r,o));case"transformation":return C(()=>fC(i,r,o));case"custom":const a=xf(i.op);if(a&&a.customExecutor)return a.customExecutor(new HN(i,r,o));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,s);return n instanceof Promise?n.then(i=>[].concat(i)):[].concat(n)}class $w{constructor(e={},t={},s={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=s,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const s=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(s))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function zw(e,t,s,n){const i=new Set,r=[];let o=null,a=null;const l=new Set,c=Object.keys(e).map(h=>Ss(h)[0]);let p=[];n!=null&&(p=n.map(h=>Ss(h.name)[0]));const u=[...t];for(;u.length>0;){const h=u.pop();if((Ww(h)||GB(h))&&(o==null&&(o=h,a=o.children.map(d=>d.name).filter(d=>i.has(d)))),i.add(h.name),s[h.name]!=null)continue;if(c.indexOf(h.name)!==-1)continue;if(p.indexOf(h.name)!==-1)continue;if(h.inputs.length===0){r.push(h.name);continue}h.inputs.forEach(d=>{if(l.has(d.name))return;l.add(d.name),u.push(d)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:r,dynamicNode:o,syncInputs:a}}function gC(e,t,s){const{usedNodes:n,inputs:i}=s,r=[],o=Object.keys(i).map(p=>Ss(p)[0]).map(p=>e.nodes[p]),a=e.initNodes;o.forEach(p=>{n.has(p.name)&&r.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&r.push(p)}),a!=null&&a.forEach(p=>{n.has(p.name)&&r.push(p)});const l=new Set,c=[];for(;r.length>0;){const p=r.pop();l.add(p.name),t[p.name]||c.push(p),p.children.forEach(u=>{!l.has(u.name)&&n.has(u.name)&&u.inputs.every(h=>l.has(h.name))&&r.push(u)})}return c}const qB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],HB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function Ww(e){return qB.indexOf(e.op)>=0}function GB(e){return HB.indexOf(e.op)>=0}class _f{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(s=>{this._functionExecutorMap[s]=new _f(e.functions[s],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(s=>e[s].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const s=e.map(i=>i.name).sort(),n=t.map(i=>i.name).sort();return s.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){const s=zw(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:i,syncInputs:r}=s;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${r}]`);if(n.length>0){const o=t.map(l=>l.name),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${a}]. Missing the following inputs: [${n}]`)}return gC(this.graph,this.weightMap,s)}execute(e,t){e=this.mapInputs(e);const s=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const n=s.map(p=>this.graph.nodes[Ss(p)[0]]),i=t.map(p=>Ss(p)[0]);let r=i.map(p=>this.graph.nodes[p]);r.length===0&&(r=this._outputs);const o=this.getCompilationKey(n,r);let a=this.compiledMap.get(o);a==null&&(a=this.compile(e,r),this.compiledMap.set(o,a));const l={},c={};return C(()=>{const p=new $w(this.weightMap,l,c,this.functionExecutorMap),u=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{const[f,g]=Ss(m),y=[];y[g]=e[m],u[f]=y});const h=this.getFrozenTensorIds(u),d={};for(let m=0;m<a.length;m++){const f=a[m];if(!u[f.name]){const g=Uw(f,u,p);if(g instanceof Promise)throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);u[f.name]=g,this.checkTensorForDisposal(f.name,f,u,p,h,i,d)}}return this.parent==null&&p.dispose(h),t.map(m=>Vt(m,u,p))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(s=>e[s]).map(s=>s.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,s,n,i,r,o){if(t.category==="control"||r.indexOf(e)!==-1)return;s[e].forEach(a=>{a!=null&&(o[a.id]=(o[a.id]||0)+t.children.length)}),t.inputs.forEach(a=>{if(a.category!=="control"){const l=jN(a.name,s,n);l!=null&&l.forEach(c=>{if(c&&!i.has(c.id)){const p=o[c.id];p===1?(c.dispose(),delete o[c.id]):p!=null&&o[c.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,s=!1,n={},i={}){s||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const r=new $w(this.weightMap,n,i,this.functionExecutorMap),o=await this.executeWithControlFlow(e,r,t,s),a=t.map(u=>Vt(u,o,r)),l=a.map(u=>u.id),c=Object.keys(e).map(u=>e[u].id),p=new Set([...l,...c,...this.weightIds]);return Object.keys(o).forEach(u=>{const h=o[u];h.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&r.dispose(p),a}async executeFunctionAsync(e,t,s){const n=e.reduce((i,r,o)=>(i[this.inputs[o].name]=r,i),{});return this._executeAsync(n,this.outputNodes,!0,t,s)}async executeWithControlFlow(e,t,s,n){const i=Object.keys(e),r=i.map(w=>this.graph.nodes[Ss(w)[0]]),o=s.map(w=>Ss(w)[0]),a=o.map(w=>this.graph.nodes[w]),{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:u}=zw(e,a,this.weightMap),h=[...r,...this.graph.weights].map(w=>({node:w,contexts:t.currentContext})),d=Object.assign({},this.weightMap);Object.keys(e).forEach(w=>{const[x,T]=Ss(w),A=[];A[T]=e[w],d[x]=A});const m={},f=this.getFrozenTensorIds(d),g={};for(;h.length>0;){const w=this.processStack(r,h,t,d,g,f,o,m,l);await Promise.all(w)}p==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=a.filter(w=>!Ww(w)&&!Vt(w.name,d,t)).map(w=>w.name);if(y.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${u}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${i}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(e,t,s,n,i,r,o,a,l){const c=[];for(;t.length>0;){const p=t.pop();s.currentContext=p.contexts;let u="";if(p.node.op==="Enter"&&L("isConstant",p.node,n,s)&&([u]=Gn(p.node.name,s)),e.indexOf(p.node)===-1){const h=Uw(p.node,n,s);u||([u]=Gn(p.node.name,s));const d=s.currentContext;h instanceof Promise?c.push(h.then(m=>(n[u]=m,s.currentContext=d,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l),m))):(n[u]=h,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l))}else this.processChildNodes(p.node,t,s,n,i,l)}return c}processChildNodes(e,t,s,n,i,r){e.children.forEach(o=>{const[a]=Gn(o.name,s);if(i[a]||!r.has(o.name))return;o.op==="Merge"?o.inputNames.some(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o})):o.inputNames.every(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const s=e[t],[n]=Ss(t),i=this.graph.nodes[n];if(i.attrParams.shape&&i.attrParams.shape.value){const r=i.attrParams.shape.value,o=r.length===s.shape.length&&s.shape.every((a,l)=>r[l]===-1||r[l]===a);N.assert(o,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${r}], but was [${s.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&N.assert(s.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${s.dtype}`)})}mapInputs(e){const t={};for(const s in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[s]!=null){const n=this._signature.inputs[s];t[n.name]=e[s]}else t[s]=e[s];return t}checkInputs(e){const t=Object.keys(e).filter(s=>{const[n]=Ss(s);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const s=this._signature.outputs[t];return s.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[s]=Ss(t);if(!this.graph.nodes[s])throw new Error(`The output '${t}' is not found in the graph`)})}}const YB="?tfjs-format=file",KB="model.json";class Pw{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Rt.browserHTTPRequest(e,this.loadOptions);else{const t=Rt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Rt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let s={};this.artifacts.userDefinedMetadata!=null&&(s=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const n=Rt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new _f(Fw.Instance.transformGraph(t,s)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),e.modelInitializer!=null){const i=Fw.Instance.transformGraph(e.modelInitializer);this.initializer=new _f(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const s=Rt.getSaveHandlers(e);if(s.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(s.length>1)throw new Error(`Found more than one (${s.length}) save handlers for URL '${e}'`);e=s[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof me)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,s,n)=>(t[s]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=this.executor.execute(e,t);return s.length>1?s:s[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=await this.executor.executeAsync(e,t);return s.length>1?s:s[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,s)=>(t[s]=[e[s]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function yC(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${KB}${YB}`));const s=new Pw(e,t);return await s.load(),s}const kf="2.6.0";function bC(e,t){return Df(e,t)}function Df(e,t,s=new Map,n=new Set){if(e==null)return null;if(n.has(e))throw new Error("Circular references are not supported.");if(s.has(e))return s.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(yo(e)){const r=Array.isArray(e)?[]:{};n.add(e);for(const o in e){const a=e[o],l=Df(a,t,s,n);r[o]=l}return n.delete(e),r}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return s.set(e,i.value),i.value}function xC(e,t=Bw){return wC(e,t)}function wC(e,t,s=new Set){const n=e[0];if(s.has(n))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(yo(n)){const r=Array.isArray(n)?[]:{};s.add(n);for(const o in n){const a=e.map(c=>c[o]),l=wC(a,t,s);r[o]=l}return s.delete(n),r}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return i.value}function Bw(e){return e===null?null:yo(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Ff(e,t){const s=new Map;Df(e,t,s);for(const i of Array.from(s.keys())){const r=s.get(i);if(r instanceof Promise){const o=await r;s.set(i,o)}}const n=Df(e,t,s);return n}function yo(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof me))}function LC(e){return e==null||XB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof me||N.isTypedArray(e)}function XB(e){return e===null||typeof e!="object"&&typeof e!="function"}function SC(e){return bC(e,JB)}function JB(e){return e instanceof me?{value:e.clone(),recurse:!1}:yo(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class Mf{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),s=this.get(t);return this.set(t,this.pop()),s}}class Uf extends Mf{constructor(){super(Uf.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),s=this.length();for(let n=0;n<s;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=s}}Uf.INITIAL_CAPACITY=32;const IC=vc(Tc());function jw(e){return new ZB(e)}function Eu(e){return new QB(e)}function TC(e,t){return new vC(e,t)}function AC(e,t=Ki.FAIL){return new ej(e,t)}class At{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let s=await e.next();for(;!s.done;)t.push(s.value),s=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),s=e(t.value);for(;!t.done&&s;)t=await this.next(),s=e(t.value)}handleErrors(e){return new aj(this,e)}filter(e){return new rj(this,e)}map(e){return new oj(this,e)}mapAsync(e){return new NC(this,e)}serialMapAsync(e){return new NC(this,e).serial()}flatmap(e){return new lj(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new ij(this,e,t)}columnMajorBatch(e,t=!0,s=Bw){const n=this.rowMajorBatch(e,t);return n.map(i=>xC(i,s))}concatenate(e,t){return new vC(jw([this,e]),t)}take(e){return e<0||e==null?this:new nj(this,e)}skip(e){return e<0||e==null?this:new sj(this,e)}prefetch(e){return new CC(this,e)}shuffle(e,t){return new cj(this,e,t)}serial(){return new tj(this)}}class ZB extends At{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:SC(e),done:!1}}}class QB extends At{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class tj extends At{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class sj extends At{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;ce(e.value)}return this.upstream.next()}}class nj extends At{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class ij extends At{constructor(e,t,s=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=s,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class rj extends At{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ce(e.value)}}}class oj extends At{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mn.getTensorsInContainer(e.value),s=this.transform(e.value),n=mn.getTensorsInContainer(s);for(const i of t)mn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class aj extends At{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class NC extends At{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mn.getTensorsInContainer(e.value),s=await this.transform(e.value),n=mn.getTensorsInContainer(s);for(const i of t)mn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class _u extends At{constructor(){super();this.outputQueue=new Uf,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class lj extends _u{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=mn.getTensorsInContainer(e.value),s=this.transform(e.value),n=mn.getTensorsInContainer(s);this.outputQueue.pushAll(s);for(const i of t)mn.isTensorInList(i,n)||i.dispose();return!0}}class vC extends At{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const s=await this.moreIterators.next();if(s.done)return{value:null,done:!0};this.iterator=s.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Ki;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Ki||(Ki={}));class ej extends At{constructor(e,t=Ki.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,s=0;function n(r){if(r instanceof At){const o=r.next();return{value:o.then(a=>(t++,a.done&&s++,a.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await Ff(this.iterators,n);if(t===s)return{value:null,done:!0};if(s>0)switch(this.mismatchMode){case Ki.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ki.SHORTEST:return{value:null,done:!0};case Ki.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class CC extends At{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Mf(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class cj extends CC{constructor(e,t,s){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=IC.alea(s||N.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}const RC=vc(Tc());class Xi{constructor(){this.size=null}batch(e,t=!0){const s=this;N.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),Os(async()=>(await s.iterator()).columnMajorBatch(e,t,pj),n)}concatenate(e){const t=this;let s;return this.size===Infinity||e.size===Infinity?s=Infinity:this.size!=null&&e.size!=null?s=this.size+e.size:s=null,Os(async()=>(await t.iterator()).concatenate(await e.iterator()),s)}filter(e){const t=this;let s;return this.size===Infinity?s=Infinity:s=null,Os(async()=>(await t.iterator()).filter(n=>C(()=>e(n))),s)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return Os(async()=>(await t.iterator()).map(s=>C(()=>e(s))),this.size)}mapAsync(e){const t=this;return Os(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return Os(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let s;return this.size!=null&&e>0?s=this.size*e:e===0?s=0:this.size!=null&&(e===void 0||e<0)?s=Infinity:s=null,Os(async()=>{const n=Eu(async()=>({value:await t.iterator(),done:!1}));return TC(n.take(e))},s)}skip(e){const t=this;let s;return this.size!=null&&e>=0&&this.size>=e?s=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?s=0:s=null,Os(async()=>(await t.iterator()).skip(e),s)}shuffle(e,t,s=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const n=this,i=RC.alea(t||N.now().toString());return Os(async()=>{let r=i.int32();return s&&(r+=i.int32()),(await n.iterator()).shuffle(e,r.toString())},this.size)}take(e){const t=this;let s;return this.size!=null&&this.size>e?s=e:this.size!=null&&this.size<=e?s=this.size:s=null,Os(async()=>(await t.iterator()).take(e),s)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Xi.MAX_BUFFER_SIZE=1e4;function Os(e,t=null){return new class extends Xi{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function OC(e){return Os(async()=>jw(e),e.length)}function EC(e){if(!yo(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let s=0;s<e.length;s++)t=t==null?e[s].size:Math.min(t,e[s].size);else if(e instanceof Object)for(const s in e)t=t==null?e[s].size:Math.min(t,e[s].size);return Os(async()=>{const s=await Ff(e,n=>{if(n instanceof Xi)return{value:n.iterator(),recurse:!1};if(yo(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return AC(s,Ki.SHORTEST)},t)}function pj(e){if(e===null)return null;const t=e[0];if(LC(t)){const s=uj(e);return{value:s,recurse:!1}}return{value:null,recurse:!0}}function uj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof me?Ge(e):ze(e)}class $f extends Xi{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),s=t.split(`
+`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n));return s}}const Wf='"',ku=Symbol("out"),_C=Symbol("field"),zf=Symbol("quote"),Vw=Symbol("quoteafterquote"),kC=Symbol("quoteinquote");class Pf extends Xi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new $f(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(N.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&N.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((n,i)=>(n[i]=n[i]+1||1,n),{}),s=Object.keys(t).filter(n=>t[n]>1);if(N.assert(s.length===0,()=>"Duplicate column names found: "+s.toString()),this.columnConfigs)for(const n of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(n);if(i===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const s=t.value,n=this.parseRow(s,!1);return n}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),s={},n={};for(let i=0;i<this.fullColumnNames.length;i++){const r=this.fullColumnNames[i],o=this.columnConfigs?this.columnConfigs[r]:null;if(this.configuredColumnsOnly&&!o)continue;{const a=t[i];let l=null;if(a==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${r} is empty in this line: ${e}`);l=void 0}else{const c=Number(a);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(a):l=a;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(a);break;default:l=c}}o&&o.isLabel?n[r]=l:s[r]=l}}return Object.keys(n).length===0?s:{xs:s,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const s=[];let n=0;const i=e.length;let r=ku;for(let o=0;o<i;o++)switch(r){case ku:switch(e.charAt(o)){case Wf:n=o+1,r=zf;break;case this.delimiter:if(n=o+1,this.delimiter===" "&&this.delimWhitespace)break;s.push(""),r=ku;break;default:r=_C,n=o;break}break;case _C:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o)),r=ku,n=o+1;break;default:}break;case zf:switch(e.charAt(o)){case Wf:r=Vw;break;default:}break;case Vw:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o-1)),r=ku,n=o+1;break;case Wf:r=zf;break;default:r=kC;break}break;case kC:switch(e.charAt(o)){case Wf:r=zf;break;default:}break;default:}if(r===Vw?s.push(e.substring(n,i-1)):s.push(e.substring(n)),t&&s.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${s}`);return s}}class Gw extends At{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new Gw(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(s){throw new Error(`Error thrown while initializing video stream: ${s.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const s=await this.getAudioData();if(this.includeSpectrogram){const n=this.flattenQueue(s.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const n=this.flattenQueue(s.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let s=0;return new Promise(n=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++s===this.numFrames&&(clearInterval(i),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,s=new Float32Array(e.length*t);return e.forEach((n,i)=>s.set(n,i*t)),s}getTensorFromAudioDataArray(e,t){const s=new Float32Array(N.sizeFromShape(t));return s.set(e,s.length-e.length),ze(s,t)}}class qw extends At{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){const s=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-s)/2,r=(1-n)/2,o=i+s,a=n+r;this.cropBox=ls([r,i,a,o],[1,4])}else this.cropBox=ls([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const s=new qw(e,t);return await s.start(),s}async start(){this.webcamConfig.facingMode&&N.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gr.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return C(()=>{const t=e.toFloat().expandDims(0);let s;s=en.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const n=s.shape;return s.reshape(n.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class Bf{}class Hw extends At{split(e){return new hj(this,e)}}class hj extends Hw{constructor(e,t){super();this.upstream=e,this.impl=new dj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class dj extends _u{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const s of t.slice(0,-1))this.outputQueue.push(s);return this.carryover=t[t.length-1],!0}}class DC extends At{decodeUTF8(){return new mj(this)}}class mj extends Hw{constructor(e){super();this.upstream=e,this.impl=new fj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class fj extends _u{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=kL();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let s;return W().get("IS_BROWSER")?s=this.decoder.decode(t,{stream:!0}):s=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(s),!0}}class jf extends DC{constructor(e,t={}){super();this.file=e,this.options=t,N.assert(e instanceof Uint8Array||(W().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,s)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{const i=new FileReader;i.onload=o=>{let a=i.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return s(new TypeError("FileReader returned unknown type."));t(a)},i.onabort=o=>s(new Error("Aborted")),i.onerror=o=>s(new Error(o.type));const r=this.file.slice(this.offset,n);i.readAsArrayBuffer(r)}this.offset=n});return{value:await e,done:!1}}}async function FC(e,t={}){let s,n;typeof e=="string"?s=e:(s=e.url,n=gj(e));const i=await N.fetch(s,n);if(i.ok){const r=new Uint8Array(await i.arrayBuffer());return new jf(r,t)}else throw new Error(i.statusText)}const gj=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function Vf(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class Gf extends Bf{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Vf(this.input)&&W().get("IS_NODE")){const e=DL();this.input=e.readFileSync(this.input.substr(7))}return new jf(this.input,this.options)}}class qf extends Bf{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Vf(this.url)?new Gf(this.url,this.fileOptions).iterator():FC(this.url,this.fileOptions)}}function MC(e,t={}){return new Pf(new qf(e),t)}function UC(e){const t=Eu(e);return Os(async()=>t)}function $C(e){return Os(async()=>{const t=await e();return Eu(()=>t.next())})}async function WC(e,t){return qw.create(e,t)}async function zC(e){return Gw.create(e)}const Hf="2.6.0";const Yw={};Ee(Yw,{CSVDataset:()=>Pf,Dataset:()=>Xi,FileDataSource:()=>Gf,TextLineDataset:()=>$f,URLDataSource:()=>qf,array:()=>OC,csv:()=>MC,func:()=>UC,generator:()=>$C,microphone:()=>zC,version_data:()=>Hf,webcam:()=>WC,zip:()=>EC});function ee(e,t){Array.isArray(e)||(e=[e]),e.forEach(s=>{s!=null&&N.assert(s.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const PC=vc(Tc());const yj=vt.nonMaxSuppressionV3Impl,bj=vt.split,wj=vt.tile,xj=vt.topkImpl,Lj=vt.whereImpl;function Kw(e,t,s,n){if(s==="linear")return e.linear(t);if(s==="relu")return e.relu(t);if(s==="elu")return gn(t);if(s==="relu6")return e.relu6(t);if(s==="prelu")return e.prelu(t,n);throw new Error(`Activation ${s} has not been implemented for the CPU backend.`)}class Xw extends Eo{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Fc(this,Us())}write(e,t,s){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&U.warn(`
+============================
+Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
+============================`));const n={};return this.data.set(n,{values:e,dtype:s,refCount:1}),n}makeTensorInfo(e,t,s){const n=this.write(s,e,t);return{dataId:n,shape:e,dtype:t}}incRef(e){const t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){const t=this.data.get(e);t.refCount--}}move(e,t,s,n){this.data.set(e,{values:t,dtype:n,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:s}=this.data.get(e);if(t==="complex64"){const n=this.readSync(s.real.dataId),i=this.readSync(s.imag.dataId);return U.mergeRealAndImagArrays(n,i)}return this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let s=t;if(e.dtype==="string")try{s=t.map(n=>N.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ge(e.shape,e.dtype,s)}makeOutput(e,t,s){const n=this.write(e,t,s);return Us().makeTensorFromDataId(n,t,s,this)}disposeData(e){if(this.data.has(e)){const{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.data.has(t)){const s=this.data.get(t);s.refCount--,s.refCount<1&&this.disposeData(t)}}async time(e){const t=N.now();e();const s=N.now()-t;return{kernelMs:s}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}stridedSlice(e,t,s,n){ee(e,"stridedSlice");const i=Ms.computeOutShape(t,s,n);if(i.some(a=>a===0))return ze([],i);const r=ge(i,e.dtype),o=this.bufferSync(e);for(let a=0;a<r.size;a++){const l=r.indexToLoc(a),c=new Array(l.length);for(let p=0;p<c.length;p++)c[p]=l[p]*n[p]+t[p];r.set(o.get(...c),...l)}return r.toTensor()}diag(e){const t=this.readSync(e.dataId),s=ge([e.size,e.size],e.dtype),n=s.values;for(let i=0;i<t.length;i++)n[i*e.size+i]=t[i];return s.toTensor()}unstack(e,t){const s=e.shape[t],n=new Array(e.rank-1);let i=0;for(let l=0;l<e.rank;l++)l!==t&&(n[i++]=e.shape[l]);const r=new Array(e.rank).fill(0),o=e.shape.slice();o[t]=1;const a=new Array(s);for(let l=0;l<a.length;l++)r[t]=l,a[l]=he(e,r,o).reshape(n);return a}reverse(e,t){ee(e,"reverse");const s=ge(e.shape,e.dtype),n=this.bufferSync(e);for(let i=0;i<s.size;i++){const r=s.indexToLoc(i),o=r.slice();t.forEach(a=>o[a]=e.shape[a]-1-o[a]),s.set(n.get(...o),...r)}return s.toTensor()}neg(e){return ee(e,"neg"),R(j(-1),e)}addN(e){ee(e,"addN");const t=e.map(i=>this.readSync(i.dataId)),s=ge(e[0].shape,e[0].dtype),n=s.values;for(let i=0;i<e.length;i++){const r=t[i];for(let o=0;o<n.length;o++)n[o]+=r[o]}return s.toTensor()}softmax(e,t){const s=N.parseAxisParam([t],e.shape),n=Lt(e,s),i=U.expandShapeToKeepDim(n.shape,s),r=X(e,n.reshape(i)),o=ut(r),a=this.sum(o,s).reshape(i);return Z(o,a)}pow(e,t){return ee([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.pow(s,n))}batchMatMul(e,t,s,n){ee([e,t],"matMul");const i=s?e.shape[1]:e.shape[2],r=s?e.shape[2]:e.shape[1],o=n?t.shape[1]:t.shape[2],a=e.shape[0],l=this.readSync(e.dataId),c=this.readSync(t.dataId),[p,u,h]=s?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],[d,m,f]=n?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],g=r*o,y=ge([a,r,o],e.dtype),w=y.values,x=this.blockSize;for(let T=0;T<a;T++)for(let A=0;A<r;A+=x)for(let _=0;_<o;_+=x)for(let E=0;E<i;E+=x){const F=Math.min(A+x,r),D=Math.min(_+x,o),M=Math.min(E+x,i);for(let P=A;P<F;P++)for(let B=_;B<D;B++){let Y=0;for(let q=E;q<M;q++)Y+=l[T*p+P*u+q*h]*c[q*d+B*m+T*f];w[T*g+(P*o+B)]+=Y}}return y.toTensor()}fusedBatchMatMul({a:e,b:t,transposeA:s,transposeB:n,bias:i,activation:r,preluActivationWeights:o}){let a=this.batchMatMul(e,t,s,n);return i&&(a=$(a,i)),r&&(a=Kw(this,a,r,o)),a}floorDiv(e,t){ee([e,t],"floorDiv");const s=(i,r)=>Math.floor(i/r),n="int32";return this.broadcastedBinaryOp(e,t,n,s)}sum(e,t){ee(e,"sum"),U.assertAxesAreInnerMostDims("sum",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ft(e.dtype,"int32"),r=ye(s,i),o=N.sizeFromShape(n),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=0;for(let h=0;h<o;++h)u+=l[p+h];a[c]=u}return r}prod(e,t){ee(e,"sum");const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ft(e.dtype,"int32"),r=ye(s,i),o=N.sizeFromShape(n),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=1;for(let h=0;h<o;++h)u*=l[p+h];a[c]=u}return r}unsortedSegmentSum(e,t,s){ee(e,"unsortedSegmentSum");const n=[],i=e.rank-t.rank;for(let r=0;r<i;++r)t=t.expandDims(r+1);for(let r=0;r<s;++r){const o=j(r,"int32"),a=as(o,t).asType("float32"),l=a.mul(e).sum(0);n.push(l)}return Ge(n)}argMin(e,t){ee(e,"argMin");const s=[t];U.assertAxesAreInnerMostDims("argMin",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=ye(n,"int32"),o=N.sizeFromShape(i),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=l[p],h=0;for(let d=0;d<o;++d){const m=l[p+d];m<u&&(u=m,h=d)}a[c]=h}return r}argMax(e,t){ee(e,"argMax");const s=[t];U.assertAxesAreInnerMostDims("argMax",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=ye(n,"int32"),o=N.sizeFromShape(i),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=l[p],h=0;for(let d=0;d<o;++d){const m=l[p+d];m>u&&(u=m,h=d)}a[c]=h}return r}cumsum(e,t,s,n){if(ee(e,"cumsum"),t!==e.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=Ft(e.dtype,"int32"),r=ye(e.shape,i),o=this.readSync(r.dataId),a=this.readSync(e.dataId),l=e.shape[e.rank-1],c=n?(p,u)=>p+l-u-1:(p,u)=>p+u;for(let p=0;p<a.length;p+=l)for(let u=0;u<l;u++){const h=c(p,u);if(u===0)o[h]=s?0:a[h];else{const d=c(p,u-1);o[h]=s?a[d]+o[d]:a[h]+o[d]}}return r}equal(e,t){return ee([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s===n?1:0)}notEqual(e,t){return ee([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s!==n?1:0)}less(e,t){return ee([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<n?1:0)}lessEqual(e,t){return ee([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<=n?1:0)}greater(e,t){return ee([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>n?1:0)}greaterEqual(e,t){return ee([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>=n?1:0)}logicalAnd(e,t){return ee([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s&&n)}logicalOr(e,t){return ee([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s||n)}select(e,t,s){ee([e,t,s],"select");const n=this.readSync(e.dataId),i=this.readSync(t.dataId),r=this.readSync(s.dataId),o=ye(t.shape,Ft(t.dtype,s.dtype)),a=this.readSync(o.dataId);let l=0;const c=e.rank===0||e.rank>1||t.rank===1?1:N.sizeFromShape(t.shape.slice(1));for(let p=0;p<n.length;p++)for(let u=0;u<c;u++)n[p]===1?a[l++]=i[p]:a[l++]=r[p];return o}where(e){ee([e],"where");const t=this.readSync(e.dataId);return Lj(e.shape,t)}topk(e,t,s){ee(e,"topk");const n=this.readSync(e.dataId);return xj(n,e.shape,e.dtype,t,s)}min(e,t){ee(e,"min"),U.assertAxesAreInnerMostDims("min",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];h<p&&(p=h)}o[l]=p}return i}minimum(e,t){return ee([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.min(s,n))}mod(e,t){return ee([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>{const i=s%n;return s<0&&n<0||s>=0&&n>=0?i:(i+n)%n})}maximum(e,t){return ee([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.max(s,n))}all(e,t){ee(e,"all"),U.assertAxesAreInnerMostDims("all",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];p=p&&h}o[l]=p}return i}any(e,t){ee(e,"any"),U.assertAxesAreInnerMostDims("any",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];p=p||h}o[l]=p}return i}squaredDifference(e,t){return ee([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>{const i=s-n;return i*i})}linear(e){return e}relu(e){ee(e,"relu");const t=ye(e.shape,e.dtype),s=this.readSync(t.dataId),n=this.readSync(e.dataId);for(let i=0;i<n.length;++i)s[i]=Math.max(0,n[i]);return t}relu6(e){ee(e,"relu");const t=ye(e.shape,e.dtype),s=this.readSync(t.dataId),n=this.readSync(e.dataId);for(let i=0;i<n.length;++i)s[i]=Math.min(Math.max(0,n[i]),6);return t}prelu(e,t){return ee([e,t],"prelu"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>s<0?n*s:s)}eluDer(e,t){ee([e,t],"eluDer");const s=new Float32Array(t.size),n=this.readSync(t.dataId),i=this.readSync(e.dataId);for(let r=0;r<n.length;++r){const o=n[r];o>=1?s[r]=i[r]:s[r]=i[r]*(o+1)}return this.makeOutput(s,t.shape,"float32")}atan2(e,t){return ee([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.atan2(s,n))}fusedConv2d({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){let o=this.conv2d(e,t,s);return n&&(o=$(o,n)),i&&(o=Kw(this,o,i,r)),o}conv2d(e,t,s){ee([e,t],"conv2d");const n=s.filterHeight,i=s.filterWidth,r=s.dilationHeight,o=s.dilationWidth,a=s.padInfo.left,l=s.padInfo.top,c=s.dataFormat==="channelsLast",p=ge(s.outShape,e.dtype),u=e.strides[0],h=c?e.strides[1]:e.strides[2],d=c?e.strides[2]:1,m=c?1:e.strides[1],f=p.strides[0],g=c?p.strides[1]:p.strides[2],y=c?p.strides[2]:1,w=c?1:p.strides[1],x=this.readSync(e.dataId),T=this.readSync(t.dataId),A=p.values;for(let _=0;_<s.batchSize;++_){const E=_*u,F=_*f;for(let D=0;D<s.outHeight;++D){const M=F+D*g,P=D*s.strideHeight-l;for(let B=0;B<n;B++){const Y=P+B*r;if(Y<0||Y>=s.inHeight)continue;const q=B*t.strides[0],K=E+Y*h;for(let H=0;H<s.outWidth;++H){const Q=M+H*y,J=H*s.strideWidth-a;for(let ie=0;ie<i;ie++){const ne=J+ie*o;if(ne<0||ne>=s.inWidth)continue;const le=q+ie*t.strides[1],ue=K+ne*d;let oe=le;for(let de=0;de<s.inChannels;++de){const Ae=x[ue+de*m];for(let Me=0;Me<s.outChannels;++Me)A[Q+Me*w]+=Ae*T[oe+Me];oe+=s.outChannels}}}}}}return p.toTensor()}conv3d(e,t,s){const n=s.filterDepth,i=s.filterHeight,r=s.filterWidth,o=s.dilationDepth,a=s.dilationHeight,l=s.dilationWidth,c=s.padInfo.front,p=s.padInfo.left,u=s.padInfo.top,h=ge(s.outShape,e.dtype),d=this.readSync(e.dataId),m=this.readSync(t.dataId),f=h.values;for(let g=0;g<s.batchSize;++g){const y=g*e.strides[0],w=g*h.strides[0];for(let x=0;x<s.outDepth;++x){const T=w+x*h.strides[1],A=x*s.strideDepth-c;for(let _=0;_<n;_++){const E=A+_*o;if(E<0||E>=s.inDepth)continue;const F=_*t.strides[0],D=y+E*e.strides[1];for(let M=0;M<s.outHeight;++M){const P=T+M*h.strides[2],B=M*s.strideHeight-u;for(let Y=0;Y<i;Y++){const q=B+Y*a;if(q<0||q>=s.inHeight)continue;const K=F+Y*t.strides[1],H=D+q*e.strides[2];for(let Q=0;Q<s.outWidth;++Q){const J=P+Q*s.outChannels,ie=Q*s.strideWidth-p;for(let ne=0;ne<r;ne++){const le=ie+ne*l;if(le<0||le>=s.inWidth)continue;const ue=K+ne*t.strides[2],oe=H+le*s.inChannels;let de=ue;for(let Ae=0;Ae<s.inChannels;++Ae){const Me=d[oe+Ae];for(let Qe=0;Qe<s.outChannels;++Qe)f[J+Qe]+=Me*m[de+Qe];de+=s.outChannels}}}}}}}}return h.toTensor()}conv2dDerInput(e,t,s){ee([e,t],"conv2dDerInput");const n=ge(s.inShape,"float32"),i=n.values,r=this.readSync(e.dataId),o=this.readSync(t.dataId),[a,l,c]=t.strides,{batchSize:p,filterHeight:u,filterWidth:h,inChannels:d,inHeight:m,inWidth:f,outChannels:g,outHeight:y,outWidth:w,strideHeight:x,strideWidth:T,dataFormat:A}=s,_=u-1-s.padInfo.top,E=h-1-s.padInfo.left,F=A==="channelsLast",D=n.strides[0],M=F?n.strides[1]:n.strides[2],P=F?n.strides[2]:1,B=F?1:n.strides[1],Y=e.strides[0],q=F?e.strides[1]:e.strides[2],K=F?e.strides[2]:1,H=F?1:e.strides[1];for(let Q=0;Q<p;++Q)for(let J=0;J<d;++J)for(let ie=0;ie<m;++ie){const ne=ie-_,le=Math.max(0,Math.ceil(ne/x)),ue=Math.min(y,(u+ne)/x);for(let oe=0;oe<f;++oe){const de=oe-E,Ae=Math.max(0,Math.ceil(de/T)),Me=Math.min(w,(h+de)/T);let Qe=0;for(let $t=le;$t<ue;++$t){const ks=$t*x-ne;for(let bt=Ae;bt<Me;++bt){const ps=bt*T-de,Fn=Y*Q+q*$t+K*bt,Gs=a*(u-1-ks)+l*(h-1-ps)+c*J;for(let Ds=0;Ds<g;++Ds){const cn=r[Fn+H*Ds],Ic=o[Gs+Ds];Qe+=cn*Ic}}}const St=D*Q+M*ie+P*oe+B*J;i[St]=Qe}}return n.toTensor()}conv3dDerInput(e,t,s){const n=ge(s.inShape,"float32"),i=n.values,[r,o,a,l]=n.strides,c=this.readSync(e.dataId),[p,u,h,d]=e.strides,m=this.readSync(t.dataId),[f,g,y,w]=t.strides,{batchSize:x,filterDepth:T,filterHeight:A,filterWidth:_,inChannels:E,inDepth:F,inHeight:D,inWidth:M,outChannels:P,outDepth:B,outHeight:Y,outWidth:q,strideDepth:K,strideHeight:H,strideWidth:Q}=s,J=T-1-s.padInfo.front,ie=A-1-s.padInfo.top,ne=_-1-s.padInfo.left;for(let le=0;le<x;++le)for(let ue=0;ue<E;++ue)for(let oe=0;oe<F;++oe){const de=oe-J,Ae=Math.max(0,Math.ceil(de/K)),Me=Math.min(B,(T+de)/K);for(let Qe=0;Qe<D;++Qe){const St=Qe-ie,$t=Math.max(0,Math.ceil(St/H)),ks=Math.min(Y,(A+St)/H);for(let bt=0;bt<M;++bt){const ps=bt-ne,Fn=Math.max(0,Math.ceil(ps/Q)),Gs=Math.min(q,(_+ps)/Q);let Ds=0;for(let cn=Ae;cn<Me;++cn){const Ic=cn*K-de;for(let ka=$t;ka<ks;++ka){const Da=ka*H-St;for(let Fa=Fn;Fa<Gs;++Fa){const Fg=Fa*Q-ps,Mg=p*le+u*cn+h*ka+d*Fa,KE=f*(T-1-Ic)+g*(A-1-Da)+y*(_-1-Fg)+w*ue;for(let Yu=0;Yu<P;++Yu){const XE=c[Mg+Yu],JE=m[KE+Yu];Ds+=XE*JE}}}}i[r*le+o*oe+a*Qe+l*bt+ue]=Ds}}}return n.toTensor()}conv2dDerFilter(e,t,s){ee([e,t],"conv2dDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=s.dataFormat==="channelsLast",l=ge(s.filterShape,"float32"),c=s.padInfo.left,p=s.padInfo.top,u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<r;++d){const m=Math.max(0,Math.ceil((p-d)/n)),f=Math.min(s.outHeight,(s.inHeight+p-d)/n);for(let g=0;g<o;++g){const y=Math.max(0,Math.ceil((c-g)/i)),w=Math.min(s.outWidth,(s.inWidth+c-g)/i);for(let x=0;x<s.inChannels;++x)for(let T=0;T<s.outChannels;++T){let A=0;for(let _=0;_<s.batchSize;++_)for(let E=m;E<f;++E){const F=d+E*n-p;for(let D=y;D<w;++D){const M=g+D*i-c;a?A+=u.get(_,F,M,x)*h.get(_,E,D,T):A+=u.get(_,x,F,M)*h.get(_,T,E,D)}}l.set(A,d,g,x,T)}}}return l.toTensor()}conv3dDerFilter(e,t,s){const n=s.strideDepth,i=s.strideHeight,r=s.strideWidth,o=s.filterDepth,a=s.filterHeight,l=s.filterWidth,c=ge(s.filterShape,"float32"),p=c.values,[u,h,d,m]=c.strides,f=this.readSync(t.dataId),[g,y,w,x]=t.strides,T=this.readSync(e.dataId),[A,_,E,F]=e.strides,D=s.padInfo.front,M=s.padInfo.left,P=s.padInfo.top;for(let B=0;B<o;++B){const Y=Math.max(0,Math.ceil((D-B)/n)),q=Math.min(s.outDepth,(s.inDepth+D-B)/n),K=B*u;for(let H=0;H<a;++H){const Q=Math.max(0,Math.ceil((P-H)/i)),J=Math.min(s.outHeight,(s.inHeight+P-H)/i),ie=H*h+K;for(let ne=0;ne<l;++ne){const le=Math.max(0,Math.ceil((M-ne)/r)),ue=Math.min(s.outWidth,(s.inWidth+M-ne)/r),oe=ne*d+ie;for(let de=0;de<s.inChannels;++de){const Ae=de*m+oe;for(let Me=0;Me<s.outChannels;++Me){let Qe=0;for(let St=0;St<s.batchSize;++St){const $t=St*A,ks=St*g;for(let bt=Y;bt<q;++bt){const ps=B+bt*n-D,Fn=ps*_+$t,Gs=bt*y+ks;for(let Ds=Q;Ds<J;++Ds){const cn=H+Ds*i-P,Ic=cn*E+Fn,ka=Ds*w+Gs;for(let Da=le;Da<ue;++Da){const Fa=ne+Da*r-M,Fg=Fa*F+Ic,Mg=Da*x+ka;Qe+=T[Fg+de]*f[Mg+Me]}}}}p[Ae+Me]=Qe}}}}}return c.toTensor()}fusedDepthwiseConv2D({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){let o=this.depthwiseConv2D(e,t,s);return n&&(o=$(o,n)),i&&(o=Kw(this,o,i,r)),o}depthwiseConv2D(e,t,s){ee([e,t],"depthwiseConv2D");const n=s.filterHeight,i=s.filterWidth,r=s.dilationHeight,o=s.dilationWidth,a=s.padInfo.left,l=s.padInfo.top,c=s.outChannels/s.inChannels,p=ge(s.outShape,e.dtype),u=this.readSync(e.dataId),h=this.readSync(t.dataId),d=p.values;for(let m=0;m<s.batchSize;++m){const f=m*e.strides[0],g=m*p.strides[0];for(let y=0;y<s.outHeight;++y){const w=g+y*p.strides[1],x=y*s.strideHeight-a;for(let T=0;T<n;++T){const A=x+T*r;if(A<0||A>=s.inHeight)continue;const _=T*t.strides[0],E=f+A*e.strides[1];for(let F=0;F<s.outWidth;++F){const D=w+F*p.strides[2],M=F*s.strideWidth-l;for(let P=0;P<i;++P){const B=M+P*o;if(B<0||B>=s.inWidth)continue;const Y=_+P*t.strides[1],q=E+B*s.inChannels;let K=D,H=Y;for(let Q=0;Q<s.inChannels;++Q){const J=u[q+Q];for(let ie=0;ie<c;++ie)d[K+ie]+=J*h[H+ie];K+=c,H+=c}}}}}}return p.toTensor()}depthwiseConv2DDerInput(e,t,s){ee([e,t],"depthwiseConv2DDerInput");const n=ge(s.inShape,"float32"),i=n.values,[r,o,a]=n.strides,l=this.readSync(e.dataId),[c,p,u]=e.strides,h=this.readSync(t.dataId),[d,m,f]=t.strides,{batchSize:g,filterHeight:y,filterWidth:w,inChannels:x,inHeight:T,inWidth:A,outChannels:_,outHeight:E,outWidth:F,strideHeight:D,strideWidth:M}=s,P=y-1-s.padInfo.top,B=w-1-s.padInfo.left,Y=_/x;for(let q=0;q<g;++q)for(let K=0;K<x;++K)for(let H=0;H<T;++H){const Q=H-P,J=Math.max(0,Math.ceil(Q/D)),ie=Math.min(E,(y+Q)/D);for(let ne=0;ne<A;++ne){const le=ne-B,ue=Math.max(0,Math.ceil(le/M)),oe=Math.min(F,(w+le)/M);let de=0;for(let Ae=J;Ae<ie;++Ae){const Me=Ae*D-Q;for(let Qe=ue;Qe<oe;++Qe){const St=Qe*M-le,$t=c*q+p*Ae+u*Qe,ks=d*(y-1-Me)+m*(w-1-St)+f*K;for(let bt=0;bt<Y;++bt){const ps=K*Y+bt,Fn=l[$t+ps],Gs=h[ks+bt];de+=Fn*Gs}}}i[r*q+o*H+a*ne+K]=de}}return n.toTensor()}depthwiseConv2DDerFilter(e,t,s){ee([e,t],"depthwiseConv2DDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=ge(s.filterShape,"float32"),l=s.padInfo.left,c=s.padInfo.top,p=s.outChannels/s.inChannels,u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<r;++d){const m=Math.max(0,Math.ceil((c-d)/n)),f=Math.min(s.outHeight,(s.inHeight+c-d)/n);for(let g=0;g<o;++g){const y=Math.max(0,Math.ceil((l-g)/i)),w=Math.min(s.outWidth,(s.inWidth+l-g)/i);for(let x=0;x<s.outChannels;++x){const T=Math.trunc(x/p),A=x%p;let _=0;for(let E=0;E<s.batchSize;++E)for(let F=m;F<f;++F){const D=d+F*n-c;for(let M=y;M<w;++M){const P=g+M*i-l;_+=u.get(E,D,P,T)*h.get(E,F,M,x)}}a.set(_,d,g,T,A)}}}return a.toTensor()}tile(e,t){return ee(e,"tile"),wj(this.bufferSync(e),t)}gather(e,t,s){ee([e,t],"gather");const n=e.shape.slice(),i=this.readSync(t.dataId);n[s]=i.length;const r=ge(n,e.dtype),o=this.bufferSync(e);for(let a=0;a<r.size;++a){const l=r.indexToLoc(a),c=l.slice();c[s]=i[l[s]];const p=o.locToIndex(c);r.values[a]=o.values[p]}return r.toTensor()}batchToSpaceND(e,t,s){ee([e],"batchToSpaceND");const n=t.reduce((c,p)=>c*p),i=U.getReshaped(e.shape,t,n),r=U.getPermuted(i.length,t.length),o=U.getReshapedPermuted(e.shape,t,n),a=U.getSliceBeginCoords(s,t.length),l=U.getSliceSize(o,s,t.length);return se(e.reshape(i),r).reshape(o).slice(a,l)}pool3d(e,t,s){ee(e,"pool3d");const n=t.strideDepth,i=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,h=t.padInfo.front,d=t.padInfo.top,m=t.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,g=this.readSync(e.dataId),y=ge(t.outShape,e.dtype),w=y.values,x=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],T=t.outShape[2]*t.outShape[3]*t.outShape[4],A=t.outShape[3]*t.outShape[4],_=t.outShape[4];for(let E=0;E<t.batchSize;++E){const F=E*x,D=E*e.strides[0];for(let M=0;M<t.inChannels;++M)for(let P=0;P<t.outDepth;++P){const B=P*n-h;let Y=B;for(;Y<0;)Y+=o;const q=Math.min(t.inDepth,c+B),K=F+P*T;for(let H=0;H<t.outHeight;++H){const Q=H*i-d;let J=Q;for(;J<0;)J+=a;const ie=Math.min(t.inHeight,p+Q),ne=K+H*A;for(let le=0;le<t.outWidth;++le){const ue=le*r-m;let oe=ue;for(;oe<0;)oe+=l;const de=Math.min(t.inWidth,u+ue),Ae=ne+le*_;let Me=f,Qe=0,St=0;for(let ks=Y;ks<q;ks+=o){const bt=D+ks*e.strides[1];for(let ps=J;ps<ie;ps+=a){const Fn=bt+ps*e.strides[2];for(let Gs=oe;Gs<de;Gs+=l){const Ds=Fn+Gs*e.strides[3],cn=g[Ds+M];if(s==="max"&&cn>Me?Me=cn:s==="avg"&&(Qe+=cn,St++),isNaN(Me))break}if(isNaN(Me))break}if(isNaN(Me))break}const $t=Ae+M;w[$t]=s==="avg"?Qe/St:Me}}}}return y.toTensor()}avgPool3d(e,t){return ee(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,s){ee([e,t],"avgPool3dBackprop");const n=s.strideDepth,i=s.strideHeight,r=s.strideWidth,o=s.filterDepth,a=s.filterHeight,l=s.filterWidth,c=s.dilationDepth,p=s.dilationHeight,u=s.dilationWidth,h=s.effectiveFilterDepth,d=s.effectiveFilterHeight,m=s.effectiveFilterWidth,f=h-1-s.padInfo.front,g=m-1-s.padInfo.left,y=d-1-s.padInfo.top,w=ge(t.shape,"float32"),x=1/(o*a*l),T=this.bufferSync(e);for(let A=0;A<s.batchSize;++A)for(let _=0;_<s.inChannels;++_)for(let E=0;E<s.inDepth;++E)for(let F=0;F<s.inHeight;++F)for(let D=0;D<s.inWidth;++D){const M=E-f,P=F-y,B=D-g;let Y=0;for(let q=0;q<h;q+=c){const K=(M+q)/n;if(K<0||K>=s.outDepth||Math.floor(K)!==K)continue;for(let H=0;H<d;H+=p){const Q=(P+H)/i;if(Q<0||Q>=s.outHeight||Math.floor(Q)!==Q)continue;for(let J=0;J<m;J+=u){const ie=(B+J)/r;if(ie<0||ie>=s.outWidth||Math.floor(ie)!==ie)continue;const ne=T.get(A,K,Q,ie,_);Y+=ne}}}w.set(Y*x,A,E,F,D,_)}return w.toTensor()}maxPool3d(e,t){return ee(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const s=ge(t.outShape,"int32"),n=t.strideDepth,i=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,h=t.padInfo.front,d=t.padInfo.top,m=t.padInfo.left,f=this.bufferSync(e);for(let g=0;g<t.batchSize;++g)for(let y=0;y<t.inChannels;++y)for(let w=0;w<t.outDepth;++w){const x=w*n-h;let T=x;for(;T<0;)T+=o;const A=Math.min(t.inDepth,c+x);for(let _=0;_<t.outHeight;++_){const E=_*i-d;let F=E;for(;F<0;)F+=a;const D=Math.min(t.inHeight,p+E);for(let M=0;M<t.outWidth;++M){const P=M*r-m;let B=P;for(;B<0;)B+=l;const Y=Math.min(t.inWidth,u+P);let q=Number.NEGATIVE_INFINITY,K=-1;for(let H=T;H<A;H+=o){const Q=H-x;for(let J=F;J<D;J+=a){const ie=J-E;for(let ne=B;ne<Y;ne+=l){const le=ne-P,ue=f.get(g,H,J,ne,y);ue>=q&&(q=ue,K=Q*p*u+ie*p+le)}}}s.set(K,g,w,_,M,y)}}}return s.toTensor()}maxPool3dBackprop(e,t,s,n){ee([t,s],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,n),r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,l=n.dilationDepth,c=n.dilationHeight,p=n.dilationWidth,u=n.effectiveFilterDepth,h=n.effectiveFilterHeight,d=n.effectiveFilterWidth,m=u-1-n.padInfo.front,f=d-1-n.padInfo.left,g=h-1-n.padInfo.top,y=ge(t.shape,"float32"),w=this.bufferSync(i),x=this.bufferSync(e);for(let T=0;T<n.batchSize;++T)for(let A=0;A<n.inChannels;++A)for(let _=0;_<n.inDepth;++_)for(let E=0;E<n.inHeight;++E)for(let F=0;F<n.inWidth;++F){const D=_-m,M=E-g,P=F-f;let B=0;for(let Y=0;Y<u;Y+=l){const q=(D+Y)/r;if(q<0||q>=n.outDepth||Math.floor(q)!==q)continue;for(let K=0;K<h;K+=c){const H=(M+K)/o;if(H<0||H>=n.outHeight||Math.floor(H)!==H)continue;for(let Q=0;Q<d;Q+=p){const J=(P+Q)/a;if(J<0||J>=n.outWidth||Math.floor(J)!==J)continue;const ie=u*h*d-1-w.get(T,q,H,J,A),ne=Y*h*d+K*d+Q,le=ie===ne?1:0;if(le===0)continue;const ue=x.get(T,q,H,J,A);B+=ue*le}}}y.set(B,T,_,E,F,A)}return y.toTensor()}resizeBilinear(e,t,s,n){ee(e,"resizeBilinear");const[i,r,o,a]=e.shape,l=this.readSync(e.dataId),c=new Float32Array(N.sizeFromShape([i,t,s,a])),p=[n&&t>1?r-1:r,n&&s>1?o-1:o],u=[n&&t>1?t-1:t,n&&s>1?s-1:s];let h=0;const d=p[0]/u[0],m=p[1]/u[1];for(let f=0;f<i;f++)for(let g=0;g<t;g++){const y=d*g,w=Math.floor(y),x=y-w,T=Math.min(r-1,Math.ceil(y)),A=f*e.strides[0]+w*e.strides[1],_=f*e.strides[0]+T*e.strides[1];for(let E=0;E<s;E++){const F=m*E,D=Math.floor(F),M=F-D,P=Math.min(o-1,Math.ceil(F)),B=A+D*e.strides[2],Y=_+D*e.strides[2],q=A+P*e.strides[2],K=_+P*e.strides[2];for(let H=0;H<a;H++){const Q=l[B+H],J=l[Y+H],ie=l[q+H],ne=l[K+H],le=Q+(ie-Q)*M,ue=J+(ne-J)*M,oe=le+(ue-le)*x;c[h++]=oe}}}return ze(c,[i,t,s,a])}resizeBilinearBackprop(e,t,s){ee([e,t],"resizeBilinearBackprop");const[n,i,r,o]=t.shape,[,a,l]=e.shape,c=new Float32Array(n*i*r*o),p=[s&&a>1?i-1:i,s&&l>1?r-1:r],u=[s&&a>1?a-1:a,s&&l>1?l-1:l],h=p[0]/u[0],d=p[1]/u[1],m=this.readSync(e.dataId);let f=0;for(let g=0;g<n;g++){const y=g*t.strides[0];for(let w=0;w<a;w++){const x=w*h,T=Math.floor(x),A=Math.min(Math.ceil(x),i-1),_=y+T*t.strides[1],E=y+A*t.strides[1],F=x-T,D=1-F;for(let M=0;M<l;M++){const P=M*d,B=Math.floor(P),Y=Math.min(Math.ceil(P),r-1),q=P-B,K=1-q,H=_+B*t.strides[2],Q=_+Y*t.strides[2],J=E+B*t.strides[2],ie=E+Y*t.strides[2],ne=D*K,le=D*q,ue=F*K,oe=F*q;for(let de=0;de<o;de++){const Ae=m[f++];c[H+de]+=Ae*ne,c[Q+de]+=Ae*le,c[J+de]+=Ae*ue,c[ie+de]+=Ae*oe}}}}return ss(c,[n,r,i,o],t.dtype)}resizeNearestNeighbor(e,t,s,n){ee(e,"resizeNearestNeighbor");const[i,r,o,a]=e.shape,l=this.readSync(e.dataId),c=new Float32Array(i*t*s*a),p=[n&&t>1?r-1:r,n&&s>1?o-1:o],u=[n&&t>1?t-1:t,n&&s>1?s-1:s],h=p[0]/u[0],d=p[1]/u[1];let m=0;for(let f=0;f<i;f++){const g=f*e.strides[0];for(let y=0;y<t;y++){const w=h*y,x=Math.min(r-1,n?Math.round(w):Math.floor(w)),T=g+x*e.strides[1];for(let A=0;A<s;A++){const _=d*A,E=Math.min(o-1,n?Math.round(_):Math.floor(_)),F=T+E*e.strides[2];for(let D=0;D<a;D++){const M=l[F+D];c[m++]=M}}}}return ze(c,[i,t,s,a],e.dtype)}resizeNearestNeighborBackprop(e,t,s){ee([e,t],"resizeNearestNeighborBackprop");const[n,i,r,o]=t.shape,[,a,l]=e.shape,c=new Float32Array(n*i*r*o),p=this.readSync(e.dataId),u=[s&&a>1?i-1:i,s&&l>1?r-1:r],h=[s&&a>1?a-1:a,s&&l>1?l-1:l],d=u[0]/h[0],m=u[1]/h[1],f=1/d,g=1/m,y=Math.ceil(f)*2+2,w=Math.ceil(g)*2+2;for(let x=0;x<n;x++){const T=x*t.strides[0];for(let A=0;A<i;A++){const _=T+A*t.strides[1],E=Math.floor(A*f),F=Math.floor(E-y/2);for(let D=0;D<r;D++){const M=_+D*t.strides[2],P=Math.floor(D*g),B=Math.floor(P-w/2);for(let Y=0;Y<o;Y++){let q=0;for(let K=0;K<y;K++){const H=K+F;if(H<0||H>=a)continue;const Q=T+H*e.strides[1],J=H*d,ie=Math.min(i-1,s?Math.round(J):Math.floor(J));if(A!==ie)continue;for(let ne=0;ne<w;ne++){const le=ne+B;if(le<0||le>=l)continue;const ue=Q+le*e.strides[2],oe=le*m,de=Math.min(r-1,s?Math.round(oe):Math.floor(oe));D===de&&(q+=p[ue+Y])}}c[M+Y]=q}}}}return ss(c,t.shape,t.dtype)}localResponseNormalization4D(e,t,s,n,i){ee(e,"localResponseNormalization4D");const r=e.shape[3],o=r-1,a=this.readSync(e.dataId),l=e.size,c=new Float32Array(l);function p(u){const h=u%r;let d=u-h+Math.max(0,h-t);const m=u-h+Math.min(h+t,o);let f=0;for(;d<=m;d++){const g=a[d];f+=g*g}return f}for(let u=0;u<l;u++){const h=p(u),d=a[u]*Math.pow(s+n*h,-i);c[u]=d}return ss(c,e.shape)}LRNGrad(e,t,s,n,i,r,o){ee(e,"LRNGrad");const a=e.shape[3],l=this.readSync(e.dataId),c=this.readSync(t.dataId),p=this.readSync(s.dataId),u=new Float32Array(e.size),h=e.size;for(let d=0;d<h;d++){const m=d%a,f=d-m+Math.max(0,m-n),g=d-m+Math.min(a,m+n+1);let y=0;for(let w=f;w<g;w++)y+=Math.pow(c[w],2);y=r*y+i;for(let w=f;w<g;w++){let x=-2*r*o*c[w]*p[d]/y;d===w&&(x+=Math.pow(y,-o)),x*=l[d],u[w]+=x}}return ss(u,e.shape)}multinomial(e,t,s,n){ee(e,"multinomial");const i=t?e:ts(e),r=i.shape[0],o=i.shape[1],a=ye([r,s],"int32"),l=this.readSync(a.dataId),c=this.readSync(i.dataId);for(let p=0;p<r;++p){const u=p*o,h=new Float32Array(o-1);h[0]=c[u];for(let f=1;f<h.length;++f)h[f]=h[f-1]+c[u+f];const d=PC.alea(n.toString()),m=p*s;for(let f=0;f<s;++f){const g=d();l[m+f]=h.length;for(let y=0;y<h.length;y++)if(g<h[y]){l[m+f]=y;break}}}return a}oneHot(e,t,s,n){ee(e,"oneHot");const i=new Float32Array(e.size*t);i.fill(n);const r=this.readSync(e.dataId);for(let o=0;o<e.size;++o)r[o]>=0&&r[o]<t&&(i[o*t+r[o]]=s);return ls(i,[e.size,t],"int32")}nonMaxSuppression(e,t,s,n,i){ee(e,"nonMaxSuppression");const r=this.readSync(e.dataId),o=this.readSync(t.dataId);return yj(r,o,s,n,i)}depthToSpace(e,t,s){N.assert(s==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${s}`),N.assert(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const n=e.shape[0],i=e.shape[1],r=e.shape[2],o=e.shape[3],a=i*t,l=r*t,c=o/(t*t),p=this.readSync(e.dataId),u=new Float32Array(n*a*l*c);let h=0;for(let d=0;d<n;++d)for(let m=0;m<a;++m){const f=Math.floor(m/t),g=m%t;for(let y=0;y<l;++y){const w=Math.floor(y/t),x=y%t,T=(g*t+x)*c;for(let A=0;A<c;++A){const _=A+T,E=_+o*(w+r*(f+i*d));u[h++]=p[E]}}}return ss(u,[n,a,l,c])}broadcastedBinaryOp(e,t,s,n){const i=U.assertAndGetBroadcastShape(e.shape,t.shape),r=ge(i,s),o=this.readSync(e.dataId),a=this.readSync(t.dataId),l=U.getBroadcastDims(e.shape,i),c=U.getBroadcastDims(t.shape,i),p=r.values;if(l.length+c.length===0)for(let u=0;u<p.length;++u)p[u]=n(o[u%o.length],a[u%a.length]);else{const u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<p.length;++d){const m=r.indexToLoc(d),f=m.slice(-e.rank);l.forEach(x=>f[x]=0);const g=u.locToIndex(f),y=m.slice(-t.rank);c.forEach(x=>y[x]=0);const w=h.locToIndex(y);p[d]=n(o[g],a[w])}}return r.toTensor()}split(e,t,s){return bj(e,t,s)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(e,t,s,n,i,r){const[o,a,l,c]=e.shape,p=t.shape[0],[u,h]=n,d=ge([p,u,h,c],"float32"),m=this.readSync(t.dataId),f=this.readSync(s.dataId),g=this.readSync(e.dataId),y=e.strides,w=d.strides;for(let x=0;x<p;x++){const T=x*4,A=m[T],_=m[T+1],E=m[T+2],F=m[T+3],D=f[x];if(D>=o)continue;const M=u>1?(E-A)*(a-1)/(u-1):0,P=h>1?(F-_)*(l-1)/(h-1):0;for(let B=0;B<u;B++){const Y=u>1?A*(a-1)+B*M:.5*(A+E)*(a-1);if(Y<0||Y>a-1){for(let q=0;q<h;q++)for(let K=0;K<c;K++){const H=K+q*w[2]+B*w[1]+x*w[0];d.values[H]=r}continue}if(i==="bilinear"){const q=Math.floor(Y),K=Math.ceil(Y),H=Y-q;for(let Q=0;Q<h;Q++){const J=h>1?_*(l-1)+Q*P:.5*(_+F)*(l-1);if(J<0||J>l-1){for(let ue=0;ue<c;ue++){const oe=ue+Q*w[2]+B*w[1]+x*w[0];d.values[oe]=r}continue}const ie=Math.floor(J),ne=Math.ceil(J),le=J-ie;for(let ue=0;ue<c;ue++){let oe=ue+ie*y[2]+q*y[1]+D*y[0];const de=g[oe];oe=ue+ne*y[2]+q*y[1]+D*y[0];const Ae=g[oe];oe=ue+ie*y[2]+K*y[1]+D*y[0];const Me=g[oe];oe=ue+ne*y[2]+K*y[1]+D*y[0];const Qe=g[oe],St=de+(Ae-de)*le,$t=Me+(Qe-Me)*le;oe=ue+Q*w[2]+B*w[1]+x*w[0],d.values[oe]=St+($t-St)*H}}}else for(let q=0;q<h;++q){const K=h>1?_*(l-1)+q*P:.5*(_+F)*(l-1);if(K<0||K>l-1){for(let J=0;J<c;J++){const ie=J+q*w[2]+B*w[1]+x*w[0];d.values[ie]=r}continue}const H=Math.round(K),Q=Math.round(Y);for(let J=0;J<c;J++){const ie=J+H*y[2]+Q*y[1]+D*y[0],ne=J+q*w[2]+B*w[1]+x*w[0];d.values[ne]=g[ie]}}}}return d.toTensor()}sparseToDense(e,t,s,n){const{sliceRank:i,numUpdates:r,sliceSize:o,strides:a,outputSize:l}=U.calculateShapes(t,e,s),c=!1;return this.scatter(e,t,s,l,o,r,i,a,n,c)}gatherND(e,t){const s=t.shape,n=s[s.length-1],[i,r,o,a]=U.prepareAndValidate(e,t);if(r===0)return ze([],i,e.dtype);const l=new dn([r,o],e.dtype),c=this.readSync(t.dataId),p=this.readSync(e.dataId);for(let u=0;u<r;u++){const h=[];let d=0;for(let m=0;m<n;m++){const f=c[u*n+m];d+=f*a[m],h.push(f)}if(d<0||d>=e.size/o)throw new Error(`Invalid indices: ${h} does not index into ${e.shape}`);for(let m=0;m<o;m++)l.values[u*o+m]=p[d*o+m]}return l.toTensor().reshape(i)}scatterND(e,t,s){const{sliceRank:n,numUpdates:i,sliceSize:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=j(0),c=!0;return this.scatter(e,t,s,a,r,i,n,o,l,c)}fill(e,t,s){s=s||N.inferDtype(t);const n=N.getArrayFromDType(s,N.sizeFromShape(e));return n.fill(t),Us().makeTensor(n,e,s,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=N.getArrayFromDType(e.dtype,N.sizeFromShape(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,s){return U.linspaceImpl(e,t,s)}scatter(e,t,s,n,i,r,o,a,l,c){const p=[n/i,i],u=this.readSync(e.dataId),h=this.readSync(t.dataId);if(n===0)return ze([],s,t.dtype);const d=new dn(p,t.dtype);d.values.fill(this.readSync(l.dataId)[0]);for(let m=0;m<r;m++){const f=[];let g=0;for(let y=0;y<o;y++){const w=u[m*o+y];f.push(w),g+=w*a[y]}if(g<0||g>=n/i)throw new Error(`Invalid indices: ${f} does not index into ${s}`);for(let y=0;y<i;y++)c?d.values[g*i+y]+=h[m*i+y]:d.values[g*i+y]=t.rank===0?h[0]:h[m*i+y]}return d.toTensor().reshape(s)}}function Jw(e){const t=new Float32Array(e.length);for(let s=0;s<e.length;++s)t[s]=Math.abs(e[s]);return t}const Sj=e=>{const{x:t}=e.inputs,s=e.backend;let n=new Float32Array(N.sizeFromShape(t.shape));if(t.dtype!=="complex64"){const i=s.data.get(t.dataId).values;n=Jw(i)}else{const i=s.data.get(t.dataId),r=i.complexTensorInfos.real,o=i.complexTensorInfos.imag,a=s.data.get(r.dataId).values,l=s.data.get(o.dataId).values;for(let c=0;c<a.length;c++){const p=a[c],u=l[c];n[c]=Math.hypot(p,u)}}return s.makeOutput(n,t.shape,"float32")},BC={kernelName:_o,backendName:"cpu",kernelFunc:Sj};function js(e){return(t,s,n,i,r)=>{const o=U.assertAndGetBroadcastShape(t,s),a=o.length,l=N.computeStrides(o),c=N.sizeFromShape(o),p=N.getTypedArrayFromDType(r,c),u=t.length,h=s.length,d=N.computeStrides(t),m=N.computeStrides(s),f=U.getBroadcastDims(t,o),g=U.getBroadcastDims(s,o);if(f.length+g.length===0)for(let y=0;y<p.length;++y)p[y]=e(n[y%n.length],i[y%i.length]);else for(let y=0;y<p.length;++y){const w=N.indexToLoc(y,a,l),x=w.slice(-u);f.forEach(E=>x[E]=0);const T=N.locToIndex(x,u,d),A=w.slice(-h);g.forEach(E=>A[E]=0);const _=N.locToIndex(A,h,m);p[y]=e(n[T],i[_])}return[p,o]}}function Es(e){const{inputs:t,backend:s}=e,{real:n,imag:i}=t,r=s.data.get(n.dataId).values,o=s.data.get(i.dataId).values,a=s.makeTensorInfo(n.shape,"complex64"),l=s.data.get(a.dataId);return l.complexTensorInfos={real:s.makeTensorInfo(n.shape,"float32",r),imag:s.makeTensorInfo(i.shape,"float32",o)},a}const jC={kernelName:jc,backendName:"cpu",kernelFunc:Es};function bi(e){const{inputs:t,backend:s}=e,{x:n}=t;return 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Kf(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t,{begin:r,size:o}=n;ee(i,"slice");const[a,l]=Ms.parseSliceParams(i,r,o);Ms.assertParamsValid(i,a,l);const c=s.data.get(i.dataId).values,p=lx(c,a,l,i.shape,i.dtype);return s.makeTensorInfo(l,i.dtype,p)}const t0={kernelName:jo,backendName:"cpu",kernelFunc:Kf};const cx=js((e,t)=>e-t),Ej=Jl((e,t,s,n)=>({real:e-s,imag:t-n})),px=On(Br,cx,Ej),s0={kernelName:Br,backendName:"cpu",kernelFunc:px};function Zl(e,t,s,n,i){const r=t.length,o=N.sizeFromShape(t),a=N.computeStrides(t),l=N.computeStrides(i),c=N.getTypedArrayFromDType(s,N.sizeFromShape(i));for(let p=0;p<o;++p){const u=N.indexToLoc(p,r,a),h=new Array(u.length);for(let m=0;m<h.length;m++)h[m]=u[n[m]];const d=N.locToIndex(h,r,l);c[d]=e[p]}return c}function Xf(e,t,s,n){const i=N.parseAxisParam(t,s)[0],r=[1,s[0],1];for(let m=0;m<i;m++)r[0]*=s[m];r[1]=s[i];for(let m=i+1;m<s.length;m++)r[2]*=s[m];const o={},a=new Int32Array(s[i]),l=new dn(r,n,e),c=[],p=r[0]===1&&r[2]===1;for(let m=0;m<s[i];m++){let f;if(p)f=e[m].toString();else{const g=[];for(let y=0;y<r[0];y++)for(let w=0;w<r[2];w++)g.push(l.get(y,m,w));f=g.join(",")}if(o[f]!==void 0)a[m]=o[f];else{const g=Object.keys(o).length;o[f]=g,a[m]=g,c.push(m)}}const u=r.slice();u[1]=Object.keys(o).length;const h=new dn(u,n);c.forEach((m,f)=>{for(let g=0;g<r[0];g++)for(let y=0;y<r[2];y++)h.set(l.get(g,m,y),g,f,y)});const d=s.slice();return d[i]=u[1],{outputValues:h.values,outputShape:d,indices:a}}const ux={};Ee(ux,{addImpl:()=>Zw,ceilImpl:()=>ex,expImpl:()=>tx,expm1Impl:()=>sx,floorImpl:()=>nx,logImpl:()=>ix,maxImpl:()=>Yf,multiplyImpl:()=>rx,rsqrtImpl:()=>ax,simpleAbsImpl:()=>Jw,sliceImpl:()=>lx,subImpl:()=>cx,transposeImpl:()=>Zl,uniqueImpl:()=>Xf});const hx="2.6.0";Vp("cpu",()=>new Xw,1);const _j=fe(dr,e=>Math.acos(e)),n0={kernelName:dr,backendName:"cpu",kernelFunc:_j};const kj=fe(mr,e=>Math.acosh(e)),i0={kernelName:mr,backendName:"cpu",kernelFunc:kj};const 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le=M;le<P;le+=l){const ue=_+le*n[1];for(let oe=K;oe<H;oe+=c){const de=ue+oe*n[2],Ae=e[de+E];r==="max"&&Ae>Q?Q=Ae:r==="avg"&&(J+=Ae,ie++)}if(isNaN(Q))break}const ne=B+Y*x+E;g[ne]=r==="avg"?J/ie:Q}}}return f}function Jf(e,t,s,n,i=!1,r=!1){const o=ge(n.outShape,"int32"),a=n.strideHeight,l=n.strideWidth,c=n.dilationHeight,p=n.dilationWidth,u=n.effectiveFilterHeight,h=n.effectiveFilterWidth,d=n.padInfo.top,m=n.padInfo.left,f=ge(t,s,e);for(let g=0;g<n.batchSize;++g)for(let y=0;y<n.inChannels;++y)for(let w=0;w<n.outHeight;++w){const x=w*a-d;let T=x;for(;T<0;)T+=c;const A=Math.min(n.inHeight,u+x);for(let _=0;_<n.outWidth;++_){const E=_*l-m;let F=E;for(;F<0;)F+=p;const D=Math.min(n.inWidth,h+E);let M=Number.NEGATIVE_INFINITY,P=-1;for(let B=T;B<A;B+=c){const Y=B-x;for(let q=F;q<D;q+=p){const K=q-E,H=f.get(g,B,q,y);H>M&&(M=H,i?P=r?((g*n.inHeight+B)*n.inWidth+q)*n.inChannels+y:(B*n.inWidth+q)*n.inChannels+y:P=Y*h+K)}}o.set(P,g,w,_,y)}}return o}function $j(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;ee(i,"avgPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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The new shape and old shape must have the same number of elements.`),s.incRef(i.dataId);const c=s.data.get(i.dataId);if(c.complexTensorInfos!=null){const p=c.complexTensorInfos.real,u=c.complexTensorInfos.imag;p.shape=a,u.shape=a}return{dataId:i.dataId,shape:a,dtype:i.dtype}}const m0={kernelName:Ei,backendName:"cpu",kernelFunc:kn};function tc(e){const{inputs:t,backend:s,attrs:n}=e,{axis:i}=n,r=N.parseAxisParam(i,t[0].shape)[0];let o=U.computeOutShape(t.map(d=>d.shape),r);if(N.sizeFromShape(o)===0)return s.makeTensorInfo(o,t[0].dtype,[]);const a=t.filter(d=>N.sizeFromShape(d.shape)>0);if(a.length===1)return a[0];const l=a.map(d=>d.shape);if(U.assertParamsConsistent(l,r),a[0].dtype==="complex64"){const d=a.map(w=>bo({inputs:{input:w},backend:s})),m=a.map(w=>ec({inputs:{input:w},backend:s})),f=tc({inputs:d,backend:s,attrs:{axis:i}}),g=tc({inputs:m,backend:s,attrs:{axis:i}}),y=Es({inputs:{real:f,imag:g},backend:s});return d.forEach(w=>s.disposeIntermediateTensorInfo(w)),m.forEach(w=>s.disposeIntermediateTensorInfo(w)),s.disposeIntermediateTensorInfo(f),s.disposeIntermediateTensorInfo(g),y}const c=a.map(d=>{const m=N.sizeFromShape(d.shape.slice(r)),f=[-1,m];return kn({inputs:{x:d},backend:s,attrs:{shape:f}})});o=U.computeOutShape(c.map(d=>d.shape),1);const p=N.getTypedArrayFromDType(a[0].dtype,N.sizeFromShape(o));if(c[0].shape[0]===1){let d=0;c.forEach(m=>{const f=s.data.get(m.dataId).values,g=N.sizeFromShape(m.shape);p.set(f,d),d+=g})}else{let d=0;c.forEach(m=>{const f=s.data.get(m.dataId).values;let g=0;for(let y=0;y<m.shape[0];++y){const w=y*o[1]+d;for(let x=0;x<m.shape[1];++x)p[w+x]=f[g++]}d+=m.shape[1]})}const u=U.computeOutShape(a.map(d=>d.shape),r),h=s.makeTensorInfo(u,t[0].dtype,p);return c.forEach(d=>s.disposeIntermediateTensorInfo(d)),h}const f0={kernelName:Fo,backendName:"cpu",kernelFunc:tc};const Bj=fe(Qn,e=>Math.cos(e)),g0={kernelName:Qn,backendName:"cpu",kernelFunc:Bj};const jj=fe(Lr,e=>Math.cosh(e)),y0={kernelName:Lr,backendName:"cpu",kernelFunc:jj};const b0={kernelName:Mo,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i}=e,{strides:r,pad:o,dilations:a}=s,l=t,c=l.data.get(n.dataId).values,p=n.shape.length,u=l.data.get(i.dataId).values,h=i.shape.length,{batchSize:d,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:w,padInfo:x,strideHeight:T,strideWidth:A,filterHeight:_,filterWidth:E,dilationHeight:F,dilationWidth:D,outShape:M}=U.computeDilation2DInfo(n.shape,i.shape,r,o,"NHWC",a),P=N.sizeFromShape(M),B=M.length,Y=N.getArrayFromDType(n.dtype,P);for(let K=0;K<d;++K)for(let H=0;H<y;++H){const Q=H*T-x.top;for(let J=0;J<w;++J){const ie=J*A-x.left;for(let ne=0;ne<g;++ne){let le=Number.MIN_SAFE_INTEGER;for(let oe=0;oe<_;++oe){const de=Q+oe*F;if(de>=0&&de<m)for(let Ae=0;Ae<E;++Ae){const Me=ie+Ae*D;if(Me>=0&&Me<f){const Qe=N.locToIndex([K,de,Me,ne],p,N.computeStrides(n.shape)),St=N.locToIndex([oe,Ae,ne],h,N.computeStrides(i.shape)),$t=c[Qe]+u[St];$t>le&&(le=$t)}}}const ue=N.locToIndex([K,H,J,ne],B,N.computeStrides(M));Y[ue]=le}}}const q=l.write(N.toTypedArray(Y,n.dtype),M,n.dtype);return{dataId:q,shape:M,dtype:n.dtype}}};const w0={kernelName:Ha,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i,dy:r}=e,{strides:o,pad:a,dilations:l}=s,c=t,p=N.toNestedArray(n.shape,c.data.get(n.dataId).values),u=N.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:w,strideHeight:x,strideWidth:T,filterHeight:A,filterWidth:_,dilationHeight:E,dilationWidth:F,outShape:D}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);N.assert(r.rank===D.length,()=>`Error in ${Ha}, dy must have the same rank as output ${D.length}, but got ${r.rank}`);const M=N.toNestedArray(D,c.data.get(r.dataId).values),P=N.makeZerosNestedTypedArray(i.shape,i.dtype);for(let Y=0;Y<h;++Y)for(let q=0;q<g;++q){const K=q*x-w.top;for(let H=0;H<y;++H){const Q=H*T-w.left;for(let J=0;J<f;++J){let ie=Number.MIN_SAFE_INTEGER,ne=0,le=0;for(let ue=0;ue<A;++ue){const oe=K+ue*E;if(oe>=0&&oe<d)for(let de=0;de<_;++de){const Ae=Q+de*F;if(Ae>=0&&Ae<m){const Me=p[Y][oe][Ae][J]+u[ue][de][J];Me>ie&&(ie=Me,ne=ue,le=de)}}}P[ne][le][J]+=M[Y][q][H][J]}}}const B=c.write(N.toTypedArray(P,n.dtype),i.shape,i.dtype);return{dataId:B,shape:i.shape,dtype:i.dtype}}};const x0={kernelName:qa,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i,dy:r}=e,{strides:o,pad:a,dilations:l}=s,c=t,p=N.toNestedArray(n.shape,c.data.get(n.dataId).values),u=N.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:w,strideHeight:x,strideWidth:T,filterHeight:A,filterWidth:_,dilationHeight:E,dilationWidth:F,outShape:D}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);N.assert(r.rank===D.length,()=>`Error in ${qa}, dy must have the same rank as output ${D.length}, but got ${r.rank}`);const M=N.toNestedArray(D,c.data.get(r.dataId).values),P=N.makeZerosNestedTypedArray(n.shape,n.dtype);for(let Y=0;Y<h;++Y)for(let q=0;q<g;++q){const K=q*x-w.top;for(let H=0;H<y;++H){const Q=H*T-w.left;for(let J=0;J<f;++J){let ie=Number.MIN_SAFE_INTEGER,ne=K<0?0:K,le=Q<0?0:Q;for(let ue=0;ue<A;++ue){const oe=K+ue*E;if(oe>=0&&oe<d)for(let de=0;de<_;++de){const Ae=Q+de*F;if(Ae>=0&&Ae<m){const Me=p[Y][oe][Ae][J]+u[ue][de][J];Me>ie&&(ie=Me,ne=oe,le=Ae)}}}P[Y][ne][le][J]+=M[Y][q][H][J]}}}const B=c.write(N.toTypedArray(P,n.dtype),n.shape,n.dtype);return{dataId:B,shape:n.shape,dtype:n.dtype}}};const Vj=js((e,t)=>e/t),Gj=On(ei,Vj),Du={kernelName:ei,backendName:"cpu",kernelFunc:Gj};const qj=fe(Sr,e=>e>=0?e:Math.exp(e)-1),L0={kernelName:Sr,backendName:"cpu",kernelFunc:qj};const Hj=U.ERF_P,Yj=U.ERF_A1,Kj=U.ERF_A2,Xj=U.ERF_A3,Jj=U.ERF_A4,Zj=U.ERF_A5,Qj=fe(Ir,e=>{const t=Math.sign(e),s=Math.abs(e),n=1/(1+Hj*s);return t*(1-((((Zj*n+Jj)*n+Xj)*n+Kj)*n+Yj)*n*Math.exp(-s*s))}),S0={kernelName:Ir,backendName:"cpu",kernelFunc:Qj};function Zf(e,t,s){const n=e.shape,i=n[0],r=n[1],o=s.data.get(e.dataId),a=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,c=[i,r],p=N.sizeFromShape(c),u=N.getTypedArrayFromDType("float32",p),h=N.getTypedArrayFromDType("float32",p);for(let g=0;g<i;g++){const y=Kf({inputs:{x:a},backend:s,attrs:{begin:[g,0],size:[1,r]}}),w=Kf({inputs:{x:l},backend:s,attrs:{begin:[g,0],size:[1,r]}}),x=Es({inputs:{real:y,imag:w},backend:s}),{real:T,imag:A}=e3(x,t,s),_=U.mergeRealAndImagArrays(T,A);for(let E=0;E<r;E++){const F=U.getComplexWithIndex(_,E);u[g*r+E]=F.real,h[g*r+E]=F.imag}s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(w),s.disposeIntermediateTensorInfo(x)}const d=s.makeTensorInfo(c,"float32",u),m=s.makeTensorInfo(c,"float32",h),f=Es({inputs:{real:d,imag:m},backend:s});return s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(m),f}function e3(e,t,s){const n=N.sizeFromShape(e.shape),i=s.data.get(e.dataId),r=s.data.get(i.complexTensorInfos.real.dataId).values,o=s.data.get(i.complexTensorInfos.imag.dataId).values;if(t3(n)){const a=dx(r,o,n,t,s),l=[e.shape[0],e.shape[1]];if(t){const c=s.makeTensorInfo(l,"float32",a.real),p=s.makeTensorInfo(l,"float32",a.imag),u=s.makeTensorInfo([],"float32",N.createScalarValue(n,"float32")),h=bi({inputs:{x:u},backend:s}),d=Du.kernelFunc({inputs:{a:c,b:u},backend:s}),m=Du.kernelFunc({inputs:{a:p,b:h},backend:s}),f=s.data.get(d.dataId).values,g=s.data.get(m.dataId).values;return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(p),s.disposeIntermediateTensorInfo(u),s.disposeIntermediateTensorInfo(h),s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return a}else{const a=U.mergeRealAndImagArrays(r,o),l=s3(a,n,t);return U.splitRealAndImagArrays(l)}}function t3(e){return(e&e-1)===0}function dx(e,t,s,n,i){if(s===1)return{real:e,imag:t};const r=U.mergeRealAndImagArrays(e,t),o=s/2,a=U.complexWithEvenIndex(r),l=a.real,c=a.imag,p=[l.length],u=i.makeTensorInfo(p,"float32",l),h=i.makeTensorInfo(p,"float32",c),d=Es({inputs:{real:u,imag:h},backend:i}),m=U.complexWithOddIndex(r),f=m.real,g=m.imag,y=[f.length],w=i.makeTensorInfo(y,"float32",f),x=i.makeTensorInfo(y,"float32",g),T=Es({inputs:{real:w,imag:x},backend:i}),A=dx(l,c,o,n,i),_=A.real,E=A.imag,F=[_.length],D=i.makeTensorInfo(F,"float32",_),M=i.makeTensorInfo(F,"float32",E),P=Es({inputs:{real:D,imag:M},backend:i}),B=dx(f,g,o,n,i),Y=B.real,q=B.imag,K=[Y.length],H=i.makeTensorInfo(K,"float32",Y),Q=i.makeTensorInfo(K,"float32",q),J=Es({inputs:{real:H,imag:Q},backend:i}),ie=U.exponents(s,n),ne=[ie.real.length],le=i.makeTensorInfo(ne,"float32",ie.real),ue=i.makeTensorInfo(ne,"float32",ie.imag),oe=Es({inputs:{real:le,imag:ue},backend:i}),de=ox({inputs:{a:oe,b:J},backend:i}),Ae=Qw({inputs:{a:P,b:de},backend:i}),Me=px({inputs:{a:P,b:de},backend:i}),Qe=bo({inputs:{input:Ae},backend:i}),St=bo({inputs:{input:Me},backend:i}),$t=ec({inputs:{input:Ae},backend:i}),ks=ec({inputs:{input:Me},backend:i}),bt=tc({inputs:[Qe,St],backend:i,attrs:{axis:0}}),ps=tc({inputs:[$t,ks],backend:i,attrs:{axis:0}}),Fn=i.data.get(bt.dataId).values,Gs=i.data.get(ps.dataId).values;return i.disposeIntermediateTensorInfo(u),i.disposeIntermediateTensorInfo(h),i.disposeIntermediateTensorInfo(d),i.disposeIntermediateTensorInfo(w),i.disposeIntermediateTensorInfo(x),i.disposeIntermediateTensorInfo(T),i.disposeIntermediateTensorInfo(D),i.disposeIntermediateTensorInfo(M),i.disposeIntermediateTensorInfo(P),i.disposeIntermediateTensorInfo(H),i.disposeIntermediateTensorInfo(Q),i.disposeIntermediateTensorInfo(J),i.disposeIntermediateTensorInfo(le),i.disposeIntermediateTensorInfo(ue),i.disposeIntermediateTensorInfo(oe),i.disposeIntermediateTensorInfo(de),i.disposeIntermediateTensorInfo(Ae),i.disposeIntermediateTensorInfo(Me),i.disposeIntermediateTensorInfo(Qe),i.disposeIntermediateTensorInfo($t),i.disposeIntermediateTensorInfo(St),i.disposeIntermediateTensorInfo(ks),i.disposeIntermediateTensorInfo(bt),i.disposeIntermediateTensorInfo(ps),{real:Fn,imag:Gs}}function s3(e,t,s){const n=new Float32Array(t*2);for(let i=0;i<t;i++){let r=0,o=0;for(let a=0;a<t;a++){const l=U.exponent(i*a,t,s),c=U.getComplexWithIndex(e,a);r+=c.real*l.real-c.imag*l.imag,o+=c.real*l.imag+c.imag*l.real}s&&(r/=t,o/=t),U.assignToTypedArray(n,r,o,i)}return n}function n3(e){const{inputs:t,backend:s}=e,{input:n}=t,i=N.sizeFromShape(n.shape),r=n.shape[n.shape.length-1],o=i/r,a=kn({inputs:{x:n},backend:s,attrs:{shape:[o,r]}}),l=Zf(a,!1,s),c=kn({inputs:{x:l},backend:s,attrs:{shape:n.shape}});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(l),c}const I0={kernelName:Kc,backendName:"cpu",kernelFunc:n3};const v0={kernelName:Uo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,i=s,r=N.getTypedArrayFromDType(n.dtype,N.sizeFromShape(n.shape)),[o,a,l,c]=n.shape,p=i.data.get(n.dataId).values;for(let h=0;h<o;h++){const d=h*l*a*c;for(let m=0;m<a;m++){const f=m*(l*c);for(let g=0;g<l;g++){const y=g*c;for(let w=0;w<c;w++){const x=[o,m,g,w],T=x[2],A=Math.round(l-T),_=d+f+y+w;let E=p[_];if(A>=0&&A<l){const F=A*c,D=d+f+F+w;E=p[D]}r[_]=E}}}}const u=i.write(r,n.shape,n.dtype);return{dataId:u,shape:n.shape,dtype:n.dtype}}};function i3(e){const{inputs:t,backend:s}=e,{input:n}=t,i=N.sizeFromShape(n.shape),r=n.shape[n.shape.length-1],o=i/r,a=kn({inputs:{x:n},backend:s,attrs:{shape:[o,r]}}),l=Zf(a,!0,s),c=kn({inputs:{x:l},backend:s,attrs:{shape:n.shape}});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(l),c}const T0={kernelName:Qc,backendName:"cpu",kernelFunc:i3};const r3=fe(Nr,e=>Number.isFinite(e)?1:0,"bool"),A0={kernelName:Nr,backendName:"cpu",kernelFunc:r3};const o3=fe(Cr,e=>Math.abs(e)===Infinity?1:0,"bool"),N0={kernelName:Cr,backendName:"cpu",kernelFunc:o3};const a3=fe(Rr,e=>Number.isNaN(e)?1:0,"bool"),C0={kernelName:Rr,backendName:"cpu",kernelFunc:a3};const l3=fe(Er,e=>Math.log1p(e)),R0={kernelName:Er,backendName:"cpu",kernelFunc:l3};const c3=fe(Ya,e=>e?0:1,"bool"),O0={kernelName:Ya,backendName:"cpu",kernelFunc:c3};const E0={kernelName:Ri,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{reductionIndices:i,keepDims:r}=t,o=s;let a=n.shape;const l=a.length,c=N.parseAxisParam(i,a);let p=c;const u=U.getAxesPermutation(p,l);let h=o.data.get(n.dataId).values;if(u!=null){const x=new Array(l);for(let T=0;T<x.length;T++)x[T]=a[u[T]];h=Zl(h,a,n.dtype,u,x),p=U.getInnerMostAxes(p.length,l),a=x}ee(n,"max"),U.assertAxesAreInnerMostDims("max",p,l);const[d,m]=U.computeOutAndReduceShapes(a,p),f=N.sizeFromShape(m),g=Yf(h,f,d,n.dtype),y=o.write(g,d,n.dtype);let w=d;if(r){const x=U.expandShapeToKeepDim(d,c);w=x}return{dataId:y,shape:w,dtype:n.dtype}}};function p3(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;ee(i,"maxPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);let u;if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))u=bi({inputs:{x:i},backend:s});else{const h=s.data.get(i.dataId).values,d=N.computeStrides(i.shape),m=Ql(h,i.shape,i.dtype,d,p,"max");u=s.makeTensorInfo(p.outShape,i.dtype,m.values)}return u}const _0={kernelName:Oi,backendName:"cpu",kernelFunc:p3};function u3(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r,output:o}=t,a=r;ee([r,o],"maxPoolBackprop");const{filterSize:l,strides:c,pad:p,dimRoundingMode:u}=n,h=U.computePool2DInfo(a.shape,l,c,1,p,u),d=s.data.get(a.dataId).values,m=ge(h.outShape,a.dtype,Jf(d,a.shape,a.dtype,h).values),f=h.strideHeight,g=h.strideWidth,y=h.dilationHeight,w=h.dilationWidth,x=h.effectiveFilterHeight,T=h.effectiveFilterWidth,A=T-1-h.padInfo.left,_=x-1-h.padInfo.top,E=ge(a.shape,"float32"),F=s.data.get(i.dataId).values,D=ge(i.shape,"float32",F);for(let M=0;M<h.batchSize;++M)for(let P=0;P<h.inChannels;++P)for(let B=0;B<h.inHeight;++B)for(let Y=0;Y<h.inWidth;++Y){const q=B-_,K=Y-A;let H=0;for(let Q=0;Q<x;Q+=y){const J=(q+Q)/f;if(J<0||J>=h.outHeight||Math.floor(J)!==J)continue;for(let ie=0;ie<T;ie+=w){const ne=(K+ie)/g;if(ne<0||ne>=h.outWidth||Math.floor(ne)!==ne)continue;const le=x*T-1-m.get(M,J,ne,P),ue=Q*T+ie,oe=le===ue?1:0;if(oe===0)continue;const de=D.get(M,J,ne,P);H+=de*oe}}E.set(H,M,B,Y,P)}return s.makeTensorInfo(E.shape,E.dtype,E.values)}const k0={kernelName:$o,backendName:"cpu",kernelFunc:u3};function D0(e,t,s,n,i){const r=N.computeStrides(t),o=Ql(e,t,s,r,i,"max"),a=Jf(e,t,s,i,!0,n);return[o.values,a.values]}const F0={kernelName:Wo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{filterSize:i,strides:r,pad:o,includeBatchInIndex:a}=t,l=s;ee(n,"MaxPoolWithArgmax");const 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n=1,i=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,n,i,0,t.textureFormatFloat,t.textureTypeFloat,null);const r=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,r),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);const o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(r),o}function Y3(e,t){const s=Fu(e,t),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);const i=1,r=1;e.texImage2D(e.TEXTURE_2D,0,s.internalFormatHalfFloat,i,r,0,s.textureFormatFloat,s.textureTypeHalfFloat,null);const o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);const a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(o),a}function TR(e){if(e!==2)return!1;const t=on(e),s=t.fenceSync!=null;return s}function xi(e,t){Array.isArray(e)||(e=[e]),e.forEach(s=>{s!=null&&N.assert(s.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}const ve=W();ve.registerFlag("HAS_WEBGL",()=>ve.getNumber("WEBGL_VERSION")>0);ve.registerFlag("WEBGL_VERSION",()=>Lx(2)?2:Lx(1)?1:0);ve.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ve.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ve.get("WEBGL_VERSION")===2);ve.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ve.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ve.registerFlag("WEBGL_PACK",()=>ve.getBool("HAS_WEBGL"));ve.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_CLIP",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ve.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_REDUCE",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_LAZILY_UNPACK",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_CONV_IM2COL",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>xR(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>LR(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const e=ve.getNumber("WEBGL_VERSION");return e===0?0:SR(e)});ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ve.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!rl.isMobile());ve.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>IR(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ve.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ve.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ve.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>vR(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_FENCE_API_ENABLED",()=>TR(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const e=ve.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return e?4:0});ve.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});const{simpleAbsImpl:AR,addImpl:NR,ceilImpl:CR,expImpl:RR,expm1Impl:OR,floorImpl:ER,logImpl:_R,maxImpl:kR,multiplyImpl:DR,rsqrtImpl:FR,sliceImpl:MR,subImpl:UR,transposeImpl:ig,uniqueImpl:$R}=ux;class WR{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,r)=>`T${r}`);const s=[];this.variableNames.forEach(i=>{s.push(`float v${i} = get${i}AtOutCoords();`)});const n=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
+      void main() {
+        ${s.join(`
+        `)}
+
+        float result = ${n};
+        setOutput(result);
+      }
+    `}}class zR{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((i,r)=>`T${r}`);const s=[];this.variableNames.forEach(i=>{s.push(`vec4 v${i} = get${i}AtOutCoords();`)});const n=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
+      void main() {
+        ${s.join(`
+        `)}
+
+        vec4 result = ${n};
+        setOutput(result);
+      }
+    `}}class PR{constructor(e,t,s){this.variableNames=["A"];const{windowSize:n,batchSize:i,outSize:r}=e;s||this.variableNames.push("bestIndicesA"),this.outputShape=[i,r];const o=t==="max"?">":"<",a=s?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${n};
+
+        int bestIndex = inOffset;
+        float bestValue = getA(batch, bestIndex);
+
+        for (int i = 0; i < ${n}; i++) {
+          int inIdx = ${a};
+          float candidate = getA(batch, inIdx);
+          if (candidate ${o} bestValue) {
+            bestValue = candidate;
+            bestIndex = inIdx;
+          }
+        }
+        setOutput(float(bestIndex));
+      }
+    `}}function Ix(e,t){return["x","y","z","w","u","v"].slice(0,t).map(s=>`${e}.${s}`)}function _t(e,t){return t===1?[e]:Ix(e,t)}function BR(e,t){if(e===1)return"rc";let s="";for(let n=0;n<e;n++)s+=t[n],n<e-1&&(s+=",");return s}function ct(){let e,t,s,n,i,r,o,a,l,c;return W().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",s="out",n="in",i="texture",r="outputColor",o="out vec4 outputColor;",a=`
+      bool isnan_custom(float val) {
+        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
+      }
+
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan_custom(val.x),
+          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
+      }
+
+      #define isnan(value) isnan_custom(value)
+    `,l="",c=`
+      #define round(value) newRound(value)
+      int newRound(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 newRound(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `):(e="",t="attribute",s="varying",n="varying",i="texture2D",r="gl_FragColor",o="",a=`
+      #define isnan(value) isnan_custom(value)
+      bool isnan_custom(float val) {
+        return (val > 0. || val < 1. || val == 0.) ? false : true;
+      }
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
+      }
+    `,l=`
+      uniform float INFINITY;
+
+      bool isinf(float val) {
+        return abs(val) == INFINITY;
+      }
+      bvec4 isinf(vec4 val) {
+        return equal(abs(val), vec4(INFINITY));
+      }
+    `,c=`
+      int round(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 round(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `),{version:e,attribute:t,varyingVs:s,varyingFs:n,texture2D:i,output:r,defineOutput:o,defineSpecialNaN:a,defineSpecialInf:l,defineRound:c}}function Hn(e,t,s="index"){const n=N.computeStrides(t);return n.map((i,r)=>{const o=`int ${e[r]} = ${s} / ${i}`,a=r===n.length-1?`int ${e[r+1]} = ${s} - ${e[r]} * ${i}`:`index -= ${e[r]} * ${i}`;return`${o}; ${a};`}).join("")}function nc(e){const t=N.computeStrides(e).map(s=>s.toString());return`
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
+  }
+`}const rg=`
+  const float FLOAT_MAX = 1.70141184e38;
+  const float FLOAT_MIN = 1.17549435e-38;
+
+  lowp vec4 encode_float(highp float v) {
+    if (isnan(v)) {
+      return vec4(255, 255, 255, 255);
+    }
+
+    highp float av = abs(v);
+
+    if(av < FLOAT_MIN) {
+      return vec4(0.0, 0.0, 0.0, 0.0);
+    } else if(v > FLOAT_MAX) {
+      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
+    } else if(v < -FLOAT_MAX) {
+      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
+    }
+
+    highp vec4 c = vec4(0,0,0,0);
+
+    highp float e = floor(log2(av));
+    highp float m = exp2(fract(log2(av))) - 1.0;
+
+    c[2] = floor(128.0 * m);
+    m -= c[2] / 128.0;
+    c[1] = floor(32768.0 * m);
+    m -= c[1] / 32768.0;
+    c[0] = floor(8388608.0 * m);
+
+    highp float ebias = e + 127.0;
+    c[3] = floor(ebias / 2.0);
+    ebias -= c[3] * 2.0;
+    c[2] += floor(ebias) * 128.0;
+
+    c[3] += 128.0 * step(0.0, -v);
+
+    return c / 255.0;
+  }
+`;const{getBroadcastDims:jR}=U;function VR(e,t,s,n){const i=[];e.forEach(m=>{const f=N.sizeFromShape(m.shapeInfo.logicalShape);m.shapeInfo.isUniform?i.push(`uniform float ${m.name}${f>1?`[${f}]`:""};`):(i.push(`uniform sampler2D ${m.name};`),i.push(`uniform int offset${m.name};`))});const r=i.join(`
+`),o=e.map(m=>K3(m,t,n)).join(`
+`),a=t.texShape,l=ct(),c=Z3(l);let p,u,h=tV(l);t.isPacked?(p=X3(t.logicalShape,a),u=eV(l)):(p=J3(t.logicalShape,a),u=Q3(l)),n&&(h+=sV);const d=[h,c,u,r,p,o,s].join(`
+`);return d}function ic(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return nV(e);case 1:return iV(e);case 2:return rV(e);case 3:return oV(e);case 4:return aV(e);case 5:return lV(e);case 6:return cV(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function GR(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return pV(e);case 1:return uV(e);case 2:return hV(e);case 3:return dV(e);default:return mV(e)}}function K3(e,t,s=!1){let n="";s?n+=GR(e):n+=ic(e);const i=e.shapeInfo.logicalShape,r=t.logicalShape;return i.length<=r.length&&(s?n+=fV(e,t):n+=gV(e,t)),n}function X3(e,t){switch(e.length){case 0:return qR();case 1:return yV(e,t);case 2:return xV(e,t);case 3:return bV(e,t);default:return wV(e,t)}}function J3(e,t){switch(e.length){case 0:return qR();case 1:return LV(e,t);case 2:return AV(e,t);case 3:return SV(e,t);case 4:return IV(e,t);case 5:return vV(e,t);case 6:return TV(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Z3(e){return`
+    float sampleTexture(sampler2D textureSampler, vec2 uv) {
+      return ${e.texture2D}(textureSampler, uv).r;
+    }
+  `}function Q3(e){return`
+    void setOutput(float val) {
+      ${e.output} = vec4(val, 0, 0, 0);
+    }
+  `}function eV(e){return`
+    void setOutput(vec4 val) {
+      ${e.output} = val;
+    }
+  `}function tV(e){const t=`${e.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${e.varyingFs} vec2 resultUV;
+    ${e.defineOutput}
+    const vec2 halfCR = vec2(0.5, 0.5);
+
+    struct ivec5
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+    };
+
+    struct ivec6
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+      int v;
+    };
+
+    uniform float NAN;
+    ${e.defineSpecialNaN}
+    ${e.defineSpecialInf}
+    ${e.defineRound}
+
+    int imod(int x, int y) {
+      return x - y * (x / y);
+    }
+
+    int idiv(int a, int b, float sign) {
+      int res = a / b;
+      int mod = imod(a, b);
+      if (sign < 0. && mod != 0) {
+        res -= 1;
+      }
+      return res;
+    }
+
+    //Based on the work of Dave Hoskins
+    //https://www.shadertoy.com/view/4djSRW
+    #define HASHSCALE1 443.8975
+    float random(float seed){
+      vec2 p = resultUV * seed;
+      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
+      p3 += dot(p3, p3.yzx + 19.19);
+      return fract((p3.x + p3.y) * p3.z);
+    }
+
+    ${NV}
+    ${CV}
+    ${RV}
+  `;return t}const NV=`
+vec2 uvFromFlat(int texNumR, int texNumC, int index) {
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
+  int texelIndex = index / 2;
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,CV=`
+vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
+  int texNumC, int row, int col) {
+  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,RV=`
+vec2 packedUVfrom3D(int texNumR, int texNumC,
+    int texelsInBatch, int texelsInLogicalRow, int b,
+    int row, int col) {
+  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,sV=`
+  float getChannel(vec4 frag, vec2 innerDims) {
+    vec2 modCoord = mod(innerDims, 2.);
+    return modCoord.x == 0. ?
+      (modCoord.y == 0. ? frag.r : frag.g) :
+      (modCoord.y == 0. ? frag.b : frag.a);
+  }
+  float getChannel(vec4 frag, int dim) {
+    float modCoord = mod(float(dim), 2.);
+    return modCoord == 0. ? frag.r : frag.g;
+  }
+`;function qR(){return`
+    int getOutputCoords() {
+      return 0;
+    }
+  `}function yV(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ${s[1]}.0);
+      }
+    `:s[1]===1?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ${s[0]}.0);
+      }
+    `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
+    }
+  `}function LV(e,t){return t[0]===1?`
+      int getOutputCoords() {
+        return int(resultUV.x * ${t[1]}.0);
+      }
+    `:t[1]===1?`
+      int getOutputCoords() {
+        return int(resultUV.y * ${t[0]}.0);
+      }
+    `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      return resTexRC.x * ${t[1]} + resTexRC.y;
+    }
+  `}function bV(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[2]/2),i=n*Math.ceil(e[1]/2);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+
+      int b = index / ${i};
+      index -= b * ${i};
+
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `}function SV(e,t){const s=Hn(["r","c","d"],e);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      ${s}
+      return ivec3(r, c, d);
+    }
+  `}function wV(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[e.length-1]/2),i=n*Math.ceil(e[e.length-2]/2);let r=i,o="",a="b, r, c";for(let l=2;l<e.length-1;l++)r*=e[e.length-l-1],o=`
+      int b${l} = index / ${r};
+      index -= b${l} * ${r};
+    `+o,a=`b${l}, `+a;return`
+    ivec${e.length} getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+
+      ${o}
+
+      int b = index / ${i};
+      index -= b * ${i};
+
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
+
+      return ivec${e.length}(${a});
+    }
+  `}function IV(e,t){const s=Hn(["r","c","d","d2"],e);return`
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      ${s}
+      return ivec4(r, c, d, d2);
+    }
+  `}function vV(e,t){const s=Hn(["r","c","d","d2","d3"],e);return`
+    ivec5 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
+                             ${t[1]}));
+
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+
+      ${s}
+
+      ivec5 outShape = ivec5(r, c, d, d2, d3);
+      return outShape;
+    }
+  `}function TV(e,t){const s=Hn(["r","c","d","d2","d3","d4"],e);return`
+    ivec6 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+
+      ${s}
+
+      ivec6 result = ivec6(r, c, d, d2, d3, d4);
+      return result;
+    }
+  `}function xV(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(N.arraysEqual(e,t))return`
+      ivec2 getOutputCoords() {
+        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
+      }
+    `;const n=Math.ceil(e[1]/2);return`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
+
+      return ivec2(r, c);
+    }
+  `}function AV(e,t){return N.arraysEqual(e,t)?`
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
+      }
+    `:e[1]===1?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${t[0]}, ${t[1]}));
+        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `:e[0]===1?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${t[0]}, ${t[1]}));
+        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `:`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      int r = index / ${e[1]};
+      int c = index - r * ${e[1]};
+      return ivec2(r, c);
+    }
+  `}function Aa(e){return`offset${e}`}function pV(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1),n=ct();return`
+    vec4 ${s}() {
+      return ${n.texture2D}(${t}, halfCR);
+    }
+  `}function nV(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${t};}`;const[n,i]=e.shapeInfo.texShape;if(n===1&&i===1)return`
+      float ${s}() {
+        return sampleTexture(${t}, halfCR);
+      }
+    `;const[r,o]=e.shapeInfo.texShape,a=Aa(t);return`
+    float ${s}() {
+      vec2 uv = uvFromFlat(${r}, ${o}, ${a});
+      return sampleTexture(${t}, uv);
+    }
+  `}function uV(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e.shapeInfo.texShape,i=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],r=ct();return`
+    vec4 ${s}(int index) {
+      vec2 uv = packedUVfrom1D(
+        ${i[0]}, ${i[1]}, index);
+      return ${r.texture2D}(${t}, uv);
+    }
+  `}function iV(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+      float ${s}(int index) {
+        ${rc(e)}
+      }
+    `;const n=e.shapeInfo.texShape,i=n[0],r=n[1];if(r===1&&i===1)return`
+      float ${s}(int index) {
+        return sampleTexture(${t}, halfCR);
+      }
+    `;const o=Aa(t);return r===1?`
+      float ${s}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${i}.0);
+        return sampleTexture(${t}, uv);
+      }
+    `:i===1?`
+      float ${s}(int index) {
+        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${r}.0, 0.5);
+        return sampleTexture(${t}, uv);
+      }
+    `:`
+    float ${s}(int index) {
+      vec2 uv = uvFromFlat(${i}, ${r}, index + ${o});
+      return sampleTexture(${t}, uv);
+    }
+  `}function hV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape,r=i[0],o=i[1],a=ct();if(i!=null&&N.arraysEqual(t,i))return`
+      vec4 ${n}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${r}.0);
+
+        return ${a.texture2D}(${s}, uv);
+      }
+    `;const l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=Math.ceil(t[1]/2);return`
+    vec4 ${n}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
+      return ${a.texture2D}(${s}, uv);
+    }
+  `}function rV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape;if(i!=null&&N.arraysEqual(t,i)){const u=i[0],h=i[1];return`
+    float ${n}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${u}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `}const{newShape:r,keptDims:o}=N.squeezeShape(t),a=r;if(a.length<t.length){const u=oc(e,a),h=["row","col"];return`
+      ${ic(u)}
+      float ${n}(int row, int col) {
+        return ${n}(${ac(h,o)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${n}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
+        ${rc(e)}
+      }
+    `;const l=i[0],c=i[1],p=Aa(s);return c===1?`
+    float ${n}(int row, int col) {
+      float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `:l===1?`
+    float ${n}(int row, int col) {
+      float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
+      return sampleTexture(${s}, uv);
+    }
+  `:`
+  float ${n}(int row, int col) {
+    // Explicitly use integer operations as dot() only works on floats.
+    int index = row * ${t[1]} + col + ${p};
+    vec2 uv = uvFromFlat(${l}, ${c}, index);
+    return sampleTexture(${s}, uv);
+  }
+`}function dV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape,r=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const u=t.slice(1),h=[1,2],d=oc(e,u),m=["b","row","col"];return`
+        ${GR(d)}
+        vec4 ${n}(int b, int row, int col) {
+          return ${n}(${ac(m,h)});
+        }
+      `}const o=r[0],a=r[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),p=ct();return`
+    vec4 ${n}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${o}, ${a}, ${c}, ${l}, b, row, col);
+      return ${p.texture2D}(${s}, uv);
+    }
+  `}function oV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[1]*t[2],r=t[2],{newShape:o,keptDims:a}=N.squeezeShape(t),l=o;if(l.length<t.length){const m=oc(e,l),f=["row","col","depth"];return`
+        ${ic(m)}
+        float ${n}(int row, int col, int depth) {
+          return ${n}(${ac(f,a)});
+        }
+      `}if(e.shapeInfo.isUniform)return`
+      float ${n}(int row, int col, int depth) {
+        int index = round(dot(vec3(row, col, depth),
+                          vec3(${i}, ${r}, 1)));
+        ${rc(e)}
+      }
+    `;const c=e.shapeInfo.texShape,p=c[0],u=c[1],h=e.shapeInfo.flatOffset;if(u===i&&h==null)return`
+        float ${n}(int row, int col, int depth) {
+          float texR = float(row);
+          float texC = dot(vec2(col, depth), vec2(${r}, 1));
+          vec2 uv = (vec2(texC, texR) + halfCR) /
+                     vec2(${u}.0, ${p}.0);
+          return sampleTexture(${s}, uv);
+        }
+      `;if(u===r&&h==null)return`
+    float ${n}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
+      float texC = float(depth);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}.0, ${p}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `;const d=Aa(s);return`
+      float ${n}(int row, int col, int depth) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${i} + col * ${r} + depth + ${d};
+        vec2 uv = uvFromFlat(${p}, ${u}, index);
+        return sampleTexture(${s}, uv);
+      }
+  `}function mV(e){const t=e.shapeInfo.logicalShape,s=t.length,n=e.name,i="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=o[0],l=o[1],c=Math.ceil(t[s-1]/2);let p=c*Math.ceil(t[s-2]/2),u="int b, int row, int col",h=`b * ${p} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<s-1;m++)u=`int b${m}, `+u,p*=t[s-m-1],h=`b${m} * ${p} + `+h;const d=ct();return`
+    vec4 ${i}(${u}) {
+      int index = ${h};
+      int texR = index / ${l};
+      int texC = index - texR * ${l};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${a});
+      return ${d.texture2D}(${n}, uv);
+    }
+  `}function aV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[3],r=t[2]*i,o=t[1]*r,{newShape:a,keptDims:l}=N.squeezeShape(t);if(a.length<t.length){const m=oc(e,a),f=["row","col","depth","depth2"];return`
+      ${ic(m)}
+      float ${n}(int row, int col, int depth, int depth2) {
+        return ${n}(${ac(f,l)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${n}(int row, int col, int depth, int depth2) {
+        int index = round(dot(vec4(row, col, depth, depth2),
+                          vec4(${o}, ${r}, ${i}, 1)));
+        ${rc(e)}
+      }
+    `;const c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,u=p[0],h=p[1];if(h===o&&c==null)return`
+      float ${n}(int row, int col, int depth, int depth2) {
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(${r}, ${i}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${h}.0, ${u}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;if(h===i&&c==null)return`
+      float ${n}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${h}.0, ${u}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;const d=Aa(s);return`
+    float ${n}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${o} + col * ${r} +
+          depth * ${i} + depth2;
+      vec2 uv = uvFromFlat(${u}, ${h}, index + ${d});
+      return sampleTexture(${s}, uv);
+    }
+  `}function lV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[4],r=t[3]*i,o=t[2]*r,a=t[1]*o,{newShape:l,keptDims:c}=N.squeezeShape(t);if(l.length<t.length){const f=oc(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${ic(f)}
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
+        return ${n}(${ac(g,c)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
+        float index = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${a}, ${o}, ${r}, ${i})) +
+          depth3;
+        ${rc(e)}
+      }
+    `;const p=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===a&&p==null)return`
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+                         vec4(${o}, ${r}, ${i}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${d}.0, ${h}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;if(d===i&&p==null)return`
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
+        float texR = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${t[1]*t[2]*t[3]},
+               ${t[2]*t[3]}, ${t[3]}, 1));
+        int texC = depth3;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${d}.0, ${h}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;const m=Aa(s);return`
+    float ${n}(int row, int col, int depth, int depth2, int depth3) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${a} + col * ${o} + depth * ${r} +
+          depth2 * ${i} + depth3 + ${m};
+      vec2 uv = uvFromFlat(${h}, ${d}, index);
+      return sampleTexture(${s}, uv);
+    }
+  `}function cV(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),{newShape:i,keptDims:r}=N.squeezeShape(t);if(i.length<t.length){const g=oc(e,i),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${ic(g)}
+      float ${n}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        return ${n}(${ac(y,r)});
+      }
+    `}const o=t[5],a=t[4]*o,l=t[3]*a,c=t[2]*l,p=t[1]*c;if(e.shapeInfo.isUniform)return`
+      float ${n}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+        int index = round(dot(
+          vec4(row, col, depth, depth2),
+          vec4(${p}, ${c}, ${l}, ${a})) +
+          dot(
+            vec2(depth3, depth4),
+            vec2(${o}, 1)));
+        ${rc(e)}
+      }
+    `;const u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],m=h[1];if(m===p&&u==null)return`
+      float ${n}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+          vec4(${c}, ${l}, ${a}, ${o})) +
+               float(depth4);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${m}.0, ${d}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;if(m===o&&u==null)return`
+      float ${n}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        float texR = dot(vec4(row, col, depth, depth2),
+          vec4(${t[1]*t[2]*t[3]*t[4]},
+               ${t[2]*t[3]*t[4]},
+               ${t[3]*t[4]},
+               ${t[4]})) + float(depth3);
+        int texC = depth4;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${m}.0, ${d}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;const f=Aa(s);return`
+    float ${n}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${p} + col * ${c} + depth * ${l} +
+          depth2 * ${a} + depth3 * ${o} + depth4 + ${f};
+      vec2 uv = uvFromFlat(${d}, ${m}, index);
+      return sampleTexture(${s}, uv);
+    }
+  `}function rc(e){const t=e.name,s=N.sizeFromShape(e.shapeInfo.logicalShape);return s<2?`return ${t};`:`
+    for (int i = 0; i < ${s}; i++) {
+      if (i == index) {
+        return ${t}[i];
+      }
+    }
+  `}function fV(e,t){const s=e.name,n=s.charAt(0).toUpperCase()+s.slice(1),i="get"+n+"AtOutCoords",r=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,a=jR(e.shapeInfo.logicalShape,t.logicalShape),l=Re(o),c=o-r;let p;const u=["x","y","z","w","u","v"];r===0?p="":o<2&&a.length>=1?p="coords = 0;":p=a.map(w=>`coords.${u[w+c]} = 0;`).join(`
+`);let h="";o<2&&r>0?h="coords":h=e.shapeInfo.logicalShape.map((w,x)=>`coords.${u[x+c]}`).join(", ");let d="return outputValue;";const m=N.sizeFromShape(e.shapeInfo.logicalShape),f=m===1,g=N.sizeFromShape(t.logicalShape),y=g===1;if(r===1&&!f&&!y)d=`
+      return vec4(outputValue.xy, outputValue.xy);
+    `;else if(f&&!y)o===1?d=`
+        return vec4(outputValue.x, outputValue.x, 0., 0.);
+      `:d=`
+        return vec4(outputValue.x);
+      `;else if(a.length){const w=r-2,x=r-1;a.indexOf(w)>-1&&a.indexOf(x)>-1?d="return vec4(outputValue.x);":a.indexOf(w)>-1?d="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(x)>-1&&(d="return vec4(outputValue.xx, outputValue.zz);")}return`
+    vec4 ${i}() {
+      ${l} coords = getOutputCoords();
+      ${p}
+      vec4 outputValue = get${n}(${h});
+      ${d}
+    }
+  `}function gV(e,t){const s=e.name,n=s.charAt(0).toUpperCase()+s.slice(1),i="get"+n+"AtOutCoords",r=t.texShape,o=e.shapeInfo.texShape,a=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&a===l&&e.shapeInfo.flatOffset==null&&N.arraysEqual(o,r))return`
+      float ${i}() {
+        return sampleTexture(${s}, resultUV);
+      }
+    `;const c=Re(l),p=jR(e.shapeInfo.logicalShape,t.logicalShape),u=l-a;let h;const d=["x","y","z","w","u","v"];a===0?h="":l<2&&p.length>=1?h="coords = 0;":h=p.map(f=>`coords.${d[f+u]} = 0;`).join(`
+`);let m="";return l<2&&a>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${d[g+u]}`).join(", "),`
+    float ${i}() {
+      ${c} coords = getOutputCoords();
+      ${h}
+      return get${n}(${m});
+    }
+  `}function Re(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function oc(e,t){const s=JSON.parse(JSON.stringify(e));return s.shapeInfo.logicalShape=t,s}function ac(e,t){return t.map(s=>e[s]).join(", ")}class HR{constructor(e,t,s,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,N.assert(e.length>2,()=>`Packed arg${s.charAt(0).toUpperCase()+s.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],r=Math.ceil(i/t);this.outputShape=e.slice(0,-1),r>1&&this.outputShape.push(r),n||this.variableNames.push("bestIndicesA");const o=this.outputShape,a=o.length,l=Re(a),c=_t("coords",a);let p,u;if(r===1){u=a+1;const E=Re(u);p=`
+        ${E} sourceLocR = ${E}(${c.join()}, 0);
+        ++${c[a-1]};
+        ${E} sourceLocG = ${E}(${c.join()}, 0);
+        ++${c[a-2]};
+        ${E} sourceLocA = ${E}(${c.join()}, 0);
+        --${c[a-1]};
+        ${E} sourceLocB = ${E}(${c.join()}, 0);
+        --${c[a-2]};`}else u=a,p=`
+        ${l} sourceLocR = coords;
+        ++${c[a-1]};
+        ${l} sourceLocG = coords;
+        ++${c[a-2]};
+        ${l} sourceLocA = coords;
+        --${c[a-1]};
+        ${l} sourceLocB = coords;
+        --${c[a-2]};`;const h=["x","y","z","w","u","v"].slice(0,u),d="."+h[u-1],m=h.map(E=>"int "+E),f=_t("sourceLocR",u-1).concat("inIdx.r"),g=_t("sourceLocG",u-1).concat("inIdx.g"),y=_t("sourceLocB",u-1).concat("inIdx.b"),w=_t("sourceLocA",u-1).concat("inIdx.a"),x=s==="max"?"greaterThan":"lessThan",T=n?"":`
+          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
+                             getBestIndicesAChannel(${g.join()}),
+                             getBestIndicesAChannel(${y.join()}),
+                             getBestIndicesAChannel(${w.join()})));`,A=`vec4(
+            getAChannel(${f.join()}),
+            hasNextCol ? getAChannel(${g.join()}) : 0.,
+            hasNextRow ? getAChannel(${y.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,_=n?"":`
+      float getBestIndicesAChannel(${m.join()}) {
+        return getChannel(getBestIndicesA(${h.join()}),
+                                          vec2(${h.slice(-2).join()}));
+      }`;this.userCode=`
+      float getAChannel(${m.join()}) {
+        return getChannel(getA(${h.join()}),
+                               vec2(${h.slice(-2).join()}));
+      }
+      ${_}
+      void main() {
+        ${l} coords = getOutputCoords();
+        bool hasNextCol = ${c[a-1]} < ${o[a-1]-1};
+        bool hasNextRow = ${c[a-2]} < ${o[a-2]-1};
+        ${p}
+        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
+          sourceLocB${d}, sourceLocA${d}) * ${t};
+        ivec4 inIdx = srcIdx;
+        vec4 bestIndex = vec4(inIdx);
+        vec4 bestValue = ${A};
+
+        for (int i = 0; i < ${t}; i++) {
+          inIdx = srcIdx;
+          ${T}
+          vec4 candidate = ${A};
+          bvec4 nan = isnan(candidate);
+          bvec4 replace = bvec4(
+            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+
+          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
+                           replace.y  ? candidate.y : bestValue.y,
+                           replace.z  ? candidate.z : bestValue.z,
+                           replace.w  ? candidate.w : bestValue.w);
+          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
+          srcIdx++;
+        }
+        setOutput(bestIndex);
+      }
+    `}}class YR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=e.dilationHeight,o=e.dilationWidth,a=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=a-1-e.padInfo.top,p=l-1-e.padInfo.left,u=1/(t*s);this.userCode=`
+      const ivec2 pads = ivec2(${c}, ${p});
+      const float avgMultiplier = float(${u});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${a};
+            wR += ${r}) {
+          float dyR = float(dyRCorner + wR) / ${n}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${l};
+            wC+= ${o}) {
+            float dyC = float(dyCCorner + wC) / ${i}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+
+            dotProd += dyValue * avgMultiplier;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class KR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,s=e.filterHeight,n=e.filterWidth,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=p-1-e.padInfo.front,m=u-1-e.padInfo.top,f=h-1-e.padInfo.left,g=1/(t*s*n);this.userCode=`
+      const ivec3 pads = ivec3(${d}, ${m}, ${f});
+      const float avgMultiplier = float(${g});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${p};
+            wD += ${a}) {
+          float dyD = float(dyDCorner + wD) / ${i}.0;
+
+          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${u};
+              wR += ${l}) {
+            float dyR = float(dyRCorner + wR) / ${r}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${h};
+                wC += ${c}) {
+              float dyC = float(dyCCorner + wC) / ${o}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+
+              dotProd += dyValue * avgMultiplier;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}const vx={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class Tx{constructor(e,t,s){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=U.assertAndGetBroadcastShape(t,s),this.userCode=`
+      float binaryOpComplex(
+          float areal, float aimag, float breal, float bimag) {
+        ${e}
+      }
+
+      void main() {
+        float areal = getARealAtOutCoords();
+        float aimag = getAImagAtOutCoords();
+        float breal = getBRealAtOutCoords();
+        float bimag = getBImagAtOutCoords();
+        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
+      }
+    `}}const XR=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,og="return a + b;",ag="return a - b;",Ax="return a * b;",JR=`
+  float s = sign(a) * sign(b);
+  int ia = round(a);
+  int ib = round(b);
+  if (ib != 0) {
+    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+    return float(idiv(ia, ib, s));
+  } else {
+    return NAN;
+  }
+`,ZR=`
+if(a < 0.0 && floor(b) < b){
+  return NAN;
+}
+if (b == 0.0) {
+  return 1.0;
+}
+return (round(mod(b, 2.0)) != 1) ?
+    pow(abs(a), b) : sign(a) * pow(abs(a), b);
+`,QR="return float(a == b);",e2="return float(a != b);",t2="return float(a < b);",s2="return float(a <= b);",n2="return float(a > b);",i2="return float(a >= b);",r2="return float(a >= 1.0 && b >= 1.0);",o2="return float(a >= 1.0 || b >= 1.0);",a2=XR+`
+  return max(a, b);
+`,l2=XR+`
+  return min(a, b);
+`,c2=`if (b == 0.0) return NAN;
+  return mod(a, b);`,p2="return (b >= 1.0) ? a : a * (b + 1.0);",Nx="return (a < 0.) ? b * a : a;";class kt{constructor(e,t,s){this.variableNames=["A","B"],this.outputShape=U.assertAndGetBroadcastShape(t,s),this.userCode=`
+      float binaryOperation(float a, float b) {
+        ${e}
+      }
+
+      void main() {
+        float a = getAAtOutCoords();
+        float b = getBAtOutCoords();
+        setOutput(binaryOperation(a, b));
+      }
+    `}}const lg=`
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`,u2=`
+  ivec4 ia = round(a);
+  ivec4 ib = round(b);
+  bvec4 cond = notEqual(ib, ivec4(0));
+  ivec4 result = ivec4(0);
+  vec4 s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    result[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    result[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    result[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    result[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4(result);
+`,h2=`
+  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
+  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
+  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
+  vec4 result = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  bvec4 isExpZero = equal(b, vec4(0.0));
+  result.r = isExpZero.r ? 1.0 : result.r;
+  result.g = isExpZero.g ? 1.0 : result.g;
+  result.b = isExpZero.b ? 1.0 : result.b;
+  result.a = isExpZero.a ? 1.0 : result.a;
+
+  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
+  `+lg+`
+  return result;
+`,Cx=`
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`,d2=`
+  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
+  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
+`,m2=`
+  return vec4(equal(a, b));
+`,f2=`
+  return vec4(notEqual(a, b));
+`,g2=`
+  return vec4(lessThan(a, b));
+`,y2=`
+  return vec4(lessThanEqual(a, b));
+`,b2=`
+  return vec4(greaterThan(a, b));
+`,w2=`
+  return vec4(greaterThanEqual(a, b));
+`,x2=`
+  return vec4(
+    vec4(greaterThanEqual(a, vec4(1.0))) *
+    vec4(greaterThanEqual(b, vec4(1.0))));
+`,L2=`
+  return min(
+    vec4(greaterThanEqual(a, vec4(1.0))) +
+    vec4(greaterThanEqual(b, vec4(1.0))),
+    vec4(1.0));
+`,S2=`
+  vec4 result = vec4(max(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+lg+`
+  return result;
+`,I2=`
+  vec4 result = vec4(min(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+lg+`
+  return result;
+`,v2=`
+  vec4 result = mod(a, b);
+  vec4 isNaN = vec4(equal(b, vec4(0.0)));
+  `+lg+`
+  return result;
+`;class Li{constructor(e,t,s,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=U.assertAndGetBroadcastShape(t,s);const i=this.outputShape.length;let r="";if(n)if(i===0||N.sizeFromShape(this.outputShape)===1)r=`
+          result.y = 0.;
+          result.z = 0.;
+          result.w = 0.;
+        `;else{const o=Re(i);if(r=`
+          ${o} coords = getOutputCoords();
+        `,i===1)r+=`
+            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;else{const a=_t("coords",i);r+=`
+            bool nextRowOutOfBounds =
+              (${a[i-2]} + 1) >= ${this.outputShape[i-2]};
+            bool nextColOutOfBounds =
+              (${a[i-1]} + 1) >= ${this.outputShape[i-1]};
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `}}this.userCode=`
+      vec4 binaryOperation(vec4 a, vec4 b) {
+        ${e}
+      }
+
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+
+        vec4 result = binaryOperation(a, b);
+        ${r}
+
+        setOutput(result);
+      }
+    `}}class T2{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+      uniform float minVal;
+      uniform float maxVal;
+
+      void main() {
+        float value = getAAtOutCoords();
+        if (isnan(value)) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, minVal, maxVal));
+      }
+    `}getCustomSetupFunc(e,t){return(s,n)=>{this.minLoc==null&&(this.minLoc=s.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=s.getUniformLocationNoThrow(n,"maxVal")),s.gl.uniform1f(this.minLoc,e),s.gl.uniform1f(this.maxLoc,t)}}}class A2{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+      uniform float minVal;
+      uniform float maxVal;
+
+      void main() {
+        vec4 value = getAAtOutCoords();
+
+        if (any(isnan(value))) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
+      }
+    `}getCustomSetupFunc(e,t){return(s,n)=>{this.minLoc==null&&(this.minLoc=s.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=s.getUniformLocationNoThrow(n,"maxVal")),s.gl.uniform1f(this.minLoc,e),s.gl.uniform1f(this.maxLoc,t)}}}class N2{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+      void main() {
+        float re = abs(getRealAtOutCoords());
+        float im = abs(getImagAtOutCoords());
+        float mx = max(re, im);
+
+        // sadly the length function in glsl is not underflow-safe
+        // (at least not on Intel GPUs). So the safe solution is
+        // to ensure underflow-safety in all cases.
+        setOutput(
+          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
+        );
+      }
+    `}}class C2{constructor(e){this.outputShape=[],this.outputShape=U.computeOutShape(e,1),this.variableNames=e.map((r,o)=>`T${o}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let r=1;r<t.length;r++)t[r]=t[r-1]+e[r][1];const s=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let r=1;r<t.length;r++){const o=t[r-1];s.push(`else if (yC < ${t[r]}) setOutput(getT${r}(yR, yC-${o}));`)}const n=t.length,i=t[t.length-1];s.push(`else setOutput(getT${n}(yR, yC-${i}));`),this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int yR = coords.x;
+        int yC = coords.y;
+
+        ${s.join(`
+        `)}
+      }
+    `}}class R2{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=U.computeOutShape(e,t);const s=this.outputShape,n=s.length,i=Re(n),r=_t("coords",n),o=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);const a=new Array(e.length-1);a[0]=e[0][t];for(let m=1;m<a.length;m++)a[m]=a[m-1]+e[m][t];const l=o[t],c=o.slice(-2),p=o.join();let u=`if (${l} < ${a[0]}) {
+        return getChannel(
+            getT0(${p}), vec2(${c.join()}));
+        }`;for(let m=1;m<a.length;m++){const f=a[m-1];u+=`
+        if (${l} < ${a[m]}  && ${l} >= ${a[m-1]}) {
+          return getChannel(
+            getT${m}(${cg(o,l,f)}),
+            vec2(${cg(c,l,f)}));
+        }`}const h=a.length,d=a[a.length-1];u+=`
+        return getChannel(
+          getT${h}(${cg(o,l,d)}),
+          vec2(${cg(c,l,d)}));`,this.userCode=`
+      float getValue(${o.map(m=>"int "+m)}) {
+        ${u}
+      }
+
+      void main() {
+        ${i} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${r}), 0., 0., 0.);
+
+        ${r[n-1]} = ${r[n-1]} + 1;
+        if (${r[n-1]} < ${s[n-1]}) {
+          result.g = getValue(${r});
+        }
+
+        ${r[n-2]} = ${r[n-2]} + 1;
+        if (${r[n-2]} < ${s[n-2]}) {
+          result.a = getValue(${r});
+        }
+
+        ${r[n-1]} = ${r[n-1]} - 1;
+        if (${r[n-2]} < ${s[n-2]} &&
+            ${r[n-1]} < ${s[n-1]}) {
+          result.b = getValue(${r});
+        }
+        setOutput(result);
+      }
+    `}}function cg(e,t,s){const n=e.indexOf(t),i=e.map((r,o)=>o===n?`${r} - ${s}`:r);return i.join()}class O2{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,s=e.strideWidth,n=e.padInfo.top,i=e.padInfo.left,r=e.dataFormat==="channelsLast";this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int d2 = coords.w;
+
+        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yR = 0; yR < ${e.outHeight}; yR++) {
+            int xR = wR + yR * ${t} - ${n};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${e.outWidth}; yC++) {
+              int xC = wC + yC * ${s} - ${i};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              if (${r}) {
+                float dyValue = getDy(b, yR, yC, d2);
+                float xValue = getX(b, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              } else {
+                float dyValue = getDy(b, d2, yR, yC);
+                float xValue = getX(b, d1, xR, xC);
+                dotProd += (xValue * dyValue);
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class E2{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,a=s-1-e.padInfo.left,l=r?1:2,c=r?2:3,p=r?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${a});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[${p}];
+
+        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${t}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${n}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${t} - 1 - wR;
+
+          for (int wC = 0; wC < ${s}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${i}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${s} - 1 - wC;
+
+            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
+
+              if (${r}) {
+                float xValue = getDy(batch, idyR, idyC, d2);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              } else {
+                float xValue = getDy(batch, d2, idyR, idyC);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class _2{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,s=e.strideHeight,n=e.strideWidth,i=e.padInfo.front,r=e.padInfo.top,o=e.padInfo.left;this.userCode=`
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int wF = coords.x;
+        int wR = coords.y;
+        int wC = coords.z;
+        int d1 = coords.w;
+        int d2 = coords.u;
+
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yF = 0; yF < ${e.outDepth}; yF++) {
+            int xF = wF + yF * ${t} - ${i};
+
+            if (xF < 0 || xF >= ${e.inDepth}) {
+              continue;
+            }
+
+            for (int yR = 0; yR < ${e.outHeight}; yR++) {
+              int xR = wR + yR * ${s} - ${r};
+
+              if (xR < 0 || xR >= ${e.inHeight}) {
+                continue;
+              }
+
+              for (int yC = 0; yC < ${e.outWidth}; yC++) {
+                int xC = wC + yC * ${n} - ${o};
+
+                if (xC < 0 || xC >= ${e.inWidth}) {
+                  continue;
+                }
+
+                float dyValue = getDy(b, yF, yR, yC, d2);
+                float xValue = getX(b, xF, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class k2{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,s=e.filterHeight,n=e.filterWidth,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.front,l=s-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${a}, ${l}, ${c});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.u;
+
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyFCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${t}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${i}.0;
+
+          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
+            continue;
+          }
+          int idyF = int(dyF);
+
+          int wFPerm = ${t} - 1 - wF;
+
+          for (int wR = 0; wR < ${s}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${r}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+              fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            int wRPerm = ${s} - 1 - wR;
+
+            for (int wC = 0; wC < ${n}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${o}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              int wCPerm = ${n} - 1 - wC;
+
+              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
+                float xValue = getDy(batch, idyF, idyR, idyC, d2);
+                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class D2{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,s=e.strideWidth,n=e.padInfo.top,i=e.padInfo.left,r=e.outChannels/e.inChannels;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int dm = coords.w;
+        int d2 = d1 * ${r} + dm;
+
+        float dotProd = 0.0;
+
+        // TO DO: Vec4 over the batch size
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yR = 0; yR < ${e.outHeight}; yR++) {
+            int xR = wR + yR * ${t} - ${n};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${e.outWidth}; yC++) {
+              int xC = wC + yC * ${s} - ${i};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              float dyValue = getDy(b, yR, yC, d2);
+              float xValue = getX(b, xR, xC, d1);
+              dotProd += (xValue * dyValue);
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class F2{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=t-1-e.padInfo.top,o=s-1-e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${r}, ${o});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[3];
+        ivec2 dyCorner = coords.yz - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        float dotProd = 0.0;
+
+        for (int wR = 0; wR < ${t}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${n}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${t} - 1 - wR;
+
+          for (int wC = 0; wC < ${s}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${i}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${s} - 1 - wC;
+
+            // TO DO: Vec4 over the channelMul
+            for (int dm = 0; dm < ${a}; dm++) {
+              int d2 = d1 * ${a} + dm;
+              float xValue = getDy(batch, idyR, idyC, d2);
+              float wValue = getW(wRPerm, wCPerm, d1, dm);
+              dotProd += xValue * wValue;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class Rx{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,r=e.padInfo.left,o=e.strideHeight,a=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,u=e.filterWidth,h=Math.floor(e.inChannels/4)*4,d=e.inChannels%4,m=e.dataFormat==="channelsLast",f=m?1:2,g=m?2:3,y=m?3:1;let w="",x="";s&&(n?w=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${s}
+        }`:w=`
+          float activation(float x) {
+            ${s}
+          }
+        `,x="result = activation(result);");const T=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${w}
+
+      const ivec2 strides = ivec2(${o}, ${a});
+      const ivec2 pads = ivec2(${i}, ${r});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d2 = coords[${y}];
+
+        ivec2 xRCCorner =
+            ivec2(coords[${f}], coords[${g}]) * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${p}; wR++) {
+          int xR = xRCorner + wR * ${l};
+
+          if (xR < 0 || xR >= ${e.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${u}; wC++) {
+            int xC = xCCorner + wC * ${c};
+
+            if (xC < 0 || xC >= ${e.inWidth}) {
+              continue;
+            }
+
+            for (int d1 = 0; d1 < ${h}; d1 += 4) {
+              vec4 wValues = vec4(
+                getW(wR, wC, d1, d2),
+                getW(wR, wC, d1 + 1, d2),
+                getW(wR, wC, d1 + 2, d2),
+                getW(wR, wC, d1 + 3, d2)
+              );
+
+              if (${m}) {
+                vec4 xValues = vec4(
+                  getX(batch, xR, xC, d1),
+                  getX(batch, xR, xC, d1 + 1),
+                  getX(batch, xR, xC, d1 + 2),
+                  getX(batch, xR, xC, d1 + 3)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec4 xValues = vec4(
+                  getX(batch, d1, xR, xC),
+                  getX(batch, d1 + 1, xR, xC),
+                  getX(batch, d1 + 2, xR, xC),
+                  getX(batch, d1 + 3, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+
+            if (${d===1}) {
+
+              if (${m}) {
+                dotProd +=
+                    getX(batch, xR, xC, ${h}) *
+                    getW(wR, wC, ${h}, d2);
+              } else {
+                dotProd +=
+                    getX(batch, ${h}, xR, xC) *
+                    getW(wR, wC, ${h}, d2);
+              }
+
+            } else if (${d===2}) {
+              vec2 wValues = vec2(
+                getW(wR, wC, ${h}, d2),
+                getW(wR, wC, ${h} + 1, d2)
+              );
+
+              if (${m}) {
+                vec2 xValues = vec2(
+                  getX(batch, xR, xC, ${h}),
+                  getX(batch, xR, xC, ${h} + 1)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec2 xValues = vec2(
+                  getX(batch, ${h}, xR, xC),
+                  getX(batch, ${h} + 1, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            } else if (${d===3}) {
+              vec3 wValues = vec3(
+                getW(wR, wC, ${h}, d2),
+                getW(wR, wC, ${h} + 1, d2),
+                getW(wR, wC, ${h} + 2, d2)
+              );
+
+              if (${m}) {
+                vec3 xValues = vec3(
+                  getX(batch, xR, xC, ${h}),
+                  getX(batch, xR, xC, ${h} + 1),
+                  getX(batch, xR, xC, ${h} + 2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec3 xValues = vec3(
+                  getX(batch, ${h}, xR, xC),
+                  getX(batch, ${h} + 1, xR, xC),
+                  getX(batch, ${h} + 2, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            }
+          }
+        }
+
+        float result = dotProd;
+        ${T}
+        ${x}
+        setOutput(result);
+      }
+    `}}class M2{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,s=e.padInfo.top,n=e.padInfo.left,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,u=e.filterHeight,h=e.filterWidth,d=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${i}, ${r}, ${o});
+      const ivec3 pads = ivec3(${t}, ${s}, ${n});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d2 = coords.u;
+
+        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xFCorner = xFRCCorner.x;
+        int xRCorner = xFRCCorner.y;
+        int xCCorner = xFRCCorner.z;
+
+        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
+        // y(yF, yR, yC, d2). ? = to be determined. : = across all
+        // values in that axis.
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${p}; wF++) {
+          int xF = xFCorner + wF * ${a};
+
+          if (xF < 0 || xF >= ${e.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${u}; wR++) {
+            int xR = xRCorner + wR * ${l};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${h}; wC++) {
+              int xC = xCCorner + wC * ${c};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              for (int d1 = 0; d1 < ${d}; d1 += 4) {
+                vec4 xValues = vec4(
+                  getX(batch, xF, xR, xC, d1),
+                  getX(batch, xF, xR, xC, d1 + 1),
+                  getX(batch, xF, xR, xC, d1 + 2),
+                  getX(batch, xF, xR, xC, d1 + 3)
+                );
+                vec4 wValues = vec4(
+                  getW(wF, wR, wC, d1, d2),
+                  getW(wF, wR, wC, d1 + 1, d2),
+                  getW(wF, wR, wC, d1 + 2, d2),
+                  getW(wF, wR, wC, d1 + 3, d2)
+                );
+
+                dotProd += dot(xValues, wValues);
+              }
+
+              if (${m===1}) {
+                dotProd +=
+                  getX(batch, xF, xR, xC, ${d}) *
+                  getW(wF, wR, wC, ${d}, d2);
+              } else if (${m===2}) {
+                vec2 xValues = vec2(
+                  getX(batch, xF, xR, xC, ${d}),
+                  getX(batch, xF, xR, xC, ${d} + 1)
+                );
+                vec2 wValues = vec2(
+                  getW(wF, wR, wC, ${d}, d2),
+                  getW(wF, wR, wC, ${d} + 1, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else if (${m===3}) {
+                vec3 xValues = vec3(
+                  getX(batch, xF, xR, xC, ${d}),
+                  getX(batch, xF, xR, xC, ${d} + 1),
+                  getX(batch, xF, xR, xC, ${d} + 2)
+                );
+                vec3 wValues = vec3(
+                  getW(wF, wR, wC, ${d}, d2),
+                  getW(wF, wR, wC, ${d} + 1, d2),
+                  getW(wF, wR, wC, ${d} + 2, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class Ox{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.inHeight,r=e.inWidth,o=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,m=e.outChannels/e.inChannels;let f="",g="";s&&(n?f=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${s}
+        }`:f=`
+          float activation(float x) {
+            ${s}
+          }
+        `,g="result = activation(result);");const y=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${f}
+
+      const ivec2 strides = ivec2(${l}, ${c});
+      const ivec2 pads = ivec2(${o}, ${a});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${m};
+        int q = d2 - d1 * ${m};
+
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
+        for (int wR = 0; wR < ${h}; wR++) {
+          int xR = xRCorner + wR * ${p};
+
+          if (xR < 0 || xR >= ${i}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${d}; wC++) {
+            int xC = xCCorner + wC * ${u};
+
+            if (xC < 0 || xC >= ${r}) {
+              continue;
+            }
+
+            float xVal = getX(batch, xR, xC, d1);
+            float wVal = getW(wR, wC, d1, q);
+            dotProd += xVal * wVal;
+          }
+        }
+
+        float result = dotProd;
+        ${y}
+        ${g}
+        setOutput(result);
+      }
+    `}}class Ex{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,r=e.inWidth,o=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,m=d;let f="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let T=0;T<d;T++)f+=`
+          vec4 xTexelR${x}C${T*2} = vec4(0.);
+          vec4 wR${x}C${T} = vec4(0.);
+          vec4 xR${x}C${T} = vec4(0.);`;for(let x=0;x<h;x++)for(let T=0;T<m;T++){const A=T*2;if(f+=`
+          xR = xRCorner + ${x*p};
+          xC = xCCorner + ${A*u};
+        `,c===1){if(A<d&&(a%2===1?f+=`
+                xCOffset = xC + 1;
+                if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${r}) {
+                  xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if(xCOffset + 1 >= ${r}) {
+                    xTexelR${x}C${A}.zw = vec2(0.);
+                  }
+                } else {
+                  xTexelR${x}C${A} = vec4(0.);
+                }
+
+                xCOffset = xC + 1 - 2;
+                if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${r}) {
+                  vec4 previous = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if(xCOffset + 1 >= ${r}) {
+                    previous.zw = vec2(0.);
+                  }
+
+                  xR${x}C${A} = vec4(previous.zw, xTexelR${x}C${A}.xy);
+                } else {
+                  xR${x}C${A} = vec4(0, 0, xTexelR${x}C${A}.xy);
+                }
+              `:f+=`
+                if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${r}) {
+                  xTexelR${x}C${A} = getX(batch, xR, xC, d1);
+                } else {
+                  xTexelR${x}C${A} = vec4(0.);
+                }
+
+                xR${x}C${A} = xTexelR${x}C${A};
+              `,A+1<d)){const _=a%2===0?N.nearestLargerEven(u):u;u%2===0&&a%2===1||u%2!==0&&a%2!==1?(f+=`
+                  xCOffset = xC + ${a%2} + ${_};
+
+                  if(xR >= 0 && xR < ${i} &&
+                    xCOffset >= 0 && xCOffset < ${r}) {
+                    xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
+                  }
+                `,u>1&&(f+=`
+                    xCOffset -= 2;
+                    if(xR >= 0 && xR < ${i} &&
+                      xCOffset >= 0 && xCOffset < ${r}) {
+                      xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);
+                    } else {
+                      xTexelR${x}C${A} = vec4(0.);
+                    }
+                  `),f+=`
+                  xR${x}C${A+1} = vec4(
+                    xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.xy);
+                `):f+=`
+                  xCOffset = xC + ${_};
+
+                  if(xR >= 0 && xR < ${i} &&
+                    xCOffset >= 0 && xCOffset < ${r}) {
+                    xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
+                  }
+
+                  xR${x}C${A+1} = xTexelR${x}C${A+2};
+                `}}else A<d&&(f+=`
+              if(xR >= 0 && xR < ${i}) {
+            `,a%2===1?(f+=`
+                xCOffset = xC + 1 - ${c};
+                if(xCOffset >= 0 && xCOffset < ${r}) {
+                  xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);
+                } else {
+                  xTexelR${x}C${A} = vec4(0.);
+                }
+
+                if(xC + 1 >= 0 && xC + 1 < ${r}) {
+                  xTexelR${x}C${A+2} = getX(batch, xR, xC + 1, d1);
+                } else {
+                  xTexelR${x}C${A+2} = vec4(0.);
+                }
+
+                xR${x}C${A} = vec4(
+                  xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.zw);
+              `,A+1<d&&(f+=`
+                  vec4 final = vec4(0.);
+                  xCOffset = xC + 1 + ${c};
+                  if(xCOffset >= 0 && xCOffset < ${r}) {
+                    final = getX(batch, xR, xCOffset, d1);
+                  }
+                  xR${x}C${A+1} = vec4(xTexelR${x}C${A+2}.xy, final.xy);
+                `)):(f+=`
+                if(xC >= 0 && xC < ${r}) {
+                  xTexelR${x}C${A} = getX(batch, xR, xC, d1);
+                } else {
+                  xTexelR${x}C${A} = vec4(0.);
+                }
+
+                xCOffset = xC + ${c};
+                if(xCOffset >= 0 && xCOffset < ${r}) {
+                  xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
+                } else {
+                  xTexelR${x}C${A+2} = vec4(0.);
+                }
+
+                xR${x}C${A} = vec4(
+                  xTexelR${x}C${A}.xy, xTexelR${x}C${A+2}.xy);
+              `,A+1<d&&(f+=`
+                  xR${x}C${A+1} = vec4(
+                    xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.zw);
+                `)),f+="}");A<d&&(f+=`
+            vec4 wTexelR${x}C${A} = getW(${x}, ${A}, d1, q);
+            wR${x}C${A} = vec4(wTexelR${x}C${A}.xz, wTexelR${x}C${A}.xz);
+          `,A+1<d&&(f+=`
+              vec4 wTexelR${x}C${A+1} = getW(${x}, ${A+1}, d1, q);
+              wR${x}C${A+1} =
+                vec4(wTexelR${x}C${A+1}.xz, wTexelR${x}C${A+1}.xz);`))}for(let x=0;x<h;x++)for(let T=0;T<d;T++)f+=`dotProd += xR${x}C${T} * wR${x}C${T};`;let g="",y="";s&&(n?g=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${s}
+        }`:g=`vec4 activation(vec4 x) {
+          ${s}
+        }`,y="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${g}
+
+      const ivec2 strides = ivec2(${l}, ${c});
+      const ivec2 pads = ivec2(${o}, ${a});
+
+      void main() {
+
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2;
+        int q = 0;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        vec4 dotProd = vec4(0.);
+
+        ${f}
+
+        vec4 result = dotProd;
+        ${w}
+        ${y}
+        setOutput(result);
+      }
+    `}}class U2{constructor(e,t,s,n,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[r,o,a,l]=e,[c]=t,[p,u]=s;this.outputShape=[c,p,u,l];const h=n==="bilinear"?1:0,[d,m]=[`${o-1}.0`,`${a-1}.0`],[f,g,y]=p>1?[`${(o-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,x,T]=u>1?[`${(a-1)/(u-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
+      const float height_ratio = float(${f});
+      const float width_ratio = float(${w});
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int y = coords[1];
+        int x = coords[2];
+        int d = coords[3];
+
+        // get box vals
+        float y1 = getBoxes(b,0);
+        float x1 = getBoxes(b,1);
+        float y2 = getBoxes(b,2);
+        float x2 = getBoxes(b,3);
+
+        // get image in batch index
+        int bInd = round(getBoxInd(b));
+        if(bInd < 0 || bInd >= ${r}) {
+          return;
+        }
+
+        float height_scale = ${g};
+        float width_scale = ${x};
+
+        float in_y = ${y};
+        if( in_y < 0.0 || in_y > ${d} ) {
+          setOutput(float(${i}));
+          return;
+        }
+        float in_x = ${T};
+        if( in_x < 0.0 || in_x > ${m} ) {
+          setOutput(float(${i}));
+          return;
+        }
+
+        vec2 sourceFracIndexCR = vec2(in_x,in_y);
+        if(${h} == 1) {
+          // Compute the four integer indices.
+          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
+          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
+
+          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
+          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
+          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
+          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
+
+          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
+
+          float top = topLeft + (topRight - topLeft) * fracCR.x;
+          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          float newValue = top + (bottom - top) * fracCR.y;
+          setOutput(newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          ivec2 sourceNearestCR = ivec2(floor(
+            sourceFracIndexCR + vec2(0.5,0.5)));
+          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutput(newValue);
+        }
+      }
+    `}}class _x{constructor(e,t,s){this.variableNames=["x"],this.outputShape=e;const n=e.length,i=t?"0.0":`getX(${$2(n,"coords")})`,r=e[e.length-1];let o="",a="";t?(o=s?`end != ${r-1}`:"end != 0",a=s?"end + 1":"end - 1"):(o=s?`end + pow2 < ${r}`:"end >= pow2",a=s?"end + pow2":"end - pow2"),this.userCode=`
+      uniform float index;
+      void main() {
+        ${Re(n)} coords = getOutputCoords();
+        int end = ${W2(n,"coords")};
+        float val = ${i};
+        int pow2 = int(pow(2.0, index));
+        if (${o}) {
+          int idx = ${a};
+          ${W2(n,"coords")} = idx;
+          val += getX(${$2(n,"coords")});
+        }
+        setOutput(val);
+      }
+    `}getCustomSetupFunc(e){return(t,s)=>{this.index==null&&(this.index=t.getUniformLocation(s,"index")),t.gl.uniform1f(this.index,e)}}}function $2(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function W2(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class z2{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=wo.DENSE;const t=xo(e),s=ct();this.outputShape=e,this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${Hn(["r","c","d"],e)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+          vec2(${t[0]}, ${t[1]}));
+        int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getA(rc.x, rc.y, rc.z);
+        }
+
+        ${s.output} = result;
+      }
+    `}}class P2{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=wo.DENSE;const t=xo(e),s=ct();this.outputShape=e,this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${Hn(["r","c","d"],e)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+          vec2(${t[0]}, ${t[1]}));
+        int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
+        }
+
+        ${s.output} = result;
+      }
+    `}}class B2{constructor(e,t,s){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=s,this.userCode=`
+    void main() {
+      ivec4 coords = getOutputCoords();
+      int b = coords[0];
+      int h = ${this.getHeightCoordString()};
+      int w = ${this.getWidthCoordString()};
+      int d = ${this.getDepthCoordString()};
+
+      int in_h = h / ${t};
+      int offset_h = imod(h, ${t});
+      int in_w = w / ${t};
+      int offset_w = imod(w, ${t});
+      int offset_d = (offset_h * ${t} + offset_w) *
+        ${this.getOutputDepthSize()};
+      int in_d = d + offset_d;
+
+      float result = ${this.getInputSamplingString()};
+      setOutput(result);
+    }
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class j2{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+      void main() {
+          ivec2 coords = getOutputCoords();
+          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
+          setOutput(val);
+      }
+    `}}class V2{constructor(e){this.variableNames=["A"],this.outTexUsage=is.DOWNLOAD;const t=ct();this.outputShape=e,this.userCode=`
+      ${rg}
+
+      void main() {
+        float x = getAAtOutCoords();
+        ${t.output} = encode_float(x);
+      }
+    `}}class G2{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=is.DOWNLOAD;const t=ct();this.outputShape=e,this.userCode=`
+      ${rg}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
+        ${t.output} = encode_float(x);
+      }
+    `}}class q2{constructor(e,t,s=!1){this.variableNames=["A"];const n=ct(),[i,r]=t;this.outputShape=e;let o="result";s&&(o="floor(result * 255. + 0.5)"),this.userCode=`
+      ${nc(e)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        int flatIndex = getFlatIndex(coords);
+        int offset = imod(flatIndex, 4);
+
+        flatIndex = idiv(flatIndex, 4, 1.);
+
+        int r = flatIndex / ${r};
+        int c = imod(flatIndex, ${r});
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(${r}.0, ${i}.0);
+        vec4 values = ${n.texture2D}(A, uv);
+
+        float result;
+
+        if(offset == 0) {
+          result = values[0];
+        } else if(offset == 1) {
+          result = values[1];
+        } else if(offset == 2) {
+          result = values[2];
+        } else {
+          result = values[3];
+        }
+
+        ${n.output} = vec4(${o}, 0., 0., 0.);
+      }
+    `}}class H2{constructor(e,t,s=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const n=ct(),[i,r]=t;this.outputShape=e;let o="",a="result";s&&(a="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){const p=l*2+c;o+=`
+          localCoords = coords;
+          if(localCoords[2] + ${c} < ${e[2]}) {
+            localCoords[2] += ${c};
+            if(localCoords[1] + ${l} < ${e[1]}) {
+              localCoords[1] += ${l};
+
+              flatIndex = getFlatIndex(localCoords);
+              offset = imod(flatIndex, 4);
+
+              flatIndex = idiv(flatIndex, 4, 1.);
+
+              r = flatIndex / ${r};
+              c = imod(flatIndex, ${r});
+              uv = (vec2(c, r) + halfCR) / vec2(${r}.0, ${i}.0);
+              values = ${n.texture2D}(A, uv);
+
+              if(offset == 0) {
+                result[${p}] = values[0];
+              } else if(offset == 1) {
+                result[${p}] = values[1];
+              } else if(offset == 2) {
+                result[${p}] = values[2];
+              } else {
+                result[${p}] = values[3];
+              }
+            }
+          }
+        `}this.userCode=`
+      ${nc(e)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        vec4 result = vec4(0.);
+        int flatIndex, r, c, offset;
+        ivec3 localCoords;
+        vec2 uv;
+        vec4 values;
+
+        ${o}
+
+        ${n.output} = ${a};
+      }
+    `}}const kx={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class Dx{constructor(e,t,s){this.variableNames=["real","imag"];const n=t[1];this.outputShape=t;const i=s?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,r=s?`${n}.0`:"1.0";this.userCode=`
+      const float exponentMultiplier = ${i};
+
+      float unaryOpComplex(float real, float expR, float imag, float expI) {
+        ${e}
+      }
+
+      float mulMatDFT(int batch, int index) {
+        float indexRatio = float(index) / float(${n});
+        float exponentMultiplierTimesIndexRatio =
+            exponentMultiplier * indexRatio;
+
+        float result = 0.0;
+
+        for (int i = 0; i < ${n}; i++) {
+          // x = (-2|2 * PI / N) * index * i;
+          float x = exponentMultiplierTimesIndexRatio * float(i);
+          float expR = cos(x);
+          float expI = sin(x);
+          float real = getReal(batch, i);
+          float imag = getImag(batch, i);
+
+          result +=
+              unaryOpComplex(real, expR, imag, expI) / ${r};
+        }
+
+        return result;
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        setOutput(mulMatDFT(coords[0], coords[1]));
+      }
+    `}}class Y2{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+      uniform float value;
+      void main() {
+        // Input can be obtained from uniform value.
+        setOutput(value);
+      }
+    `}getCustomSetupFunc(e){return(t,s)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(s,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class K2{constructor(e,t,s){this.variableNames=["A","indices"];const n=e.slice();n[s]=t,this.outputShape=n,this.rank=n.length;const i=Re(this.rank),r=OV(e,s);this.userCode=`
+      void main() {
+        ${i} resRC = getOutputCoords();
+        setOutput(getA(${r}));
+      }
+    `}}function OV(e,t){const s=e.length;if(s>4)throw Error(`Gather for rank ${s} is not yet supported`);if(s===1)return"int(getIndices(resRC))";const n=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[];for(let r=0;r<e.length;r++)r===t?i.push(`int(getIndices(${n[r]}))`):i.push(`${n[r]}`);return i.join()}class X2{constructor(e,t,s){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=s;const n=Re(t.length),i=Re(s.length),r=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${n} strides = ${n}(${this.strides});
+         void main() {
+          ${i} coords = getOutputCoords();
+          int flattenIndex = 0;
+          for (int j = 0; j < ${this.sliceDim}; j++) {
+            int index = round(getIndices(coords[0], j));
+            flattenIndex += index * ${r};
+          }
+          setOutput(getX(flattenIndex, coords[1]));
+        }
+      `}}function J2(e){const t=ct(),s=`${t.version}
+    precision highp float;
+    ${t.attribute} vec3 clipSpacePos;
+    ${t.attribute} vec2 uv;
+    ${t.varyingVs} vec2 resultUV;
+
+    void main() {
+      gl_Position = vec4(clipSpacePos, 1);
+      resultUV = uv;
+    }`;return aR(e,s)}function Z2(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return uR(e,t)}function Q2(e){const t=new Uint16Array([0,1,2,2,1,3]);return hR(e,t)}function $u(e,t,s,n,i,r){mR(t,s);const o=dR(e),a=e.TEXTURE_2D;return pe(e,()=>e.bindTexture(a,o)),pe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(a,e.TEXTURE_MIN_FILTER,e.NEAREST)),pe(e,()=>e.texParameteri(a,e.TEXTURE_MAG_FILTER,e.NEAREST)),pe(e,()=>e.texImage2D(a,0,n,t,s,0,i,r,null)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function Fx(e){return e.internalFormatFloat}function eO(e,t,s,n){const[i,r]=Ta(t,s);return $u(e,i,r,Fx(n),n.textureFormatFloat,e.FLOAT)}function Mx(e){return e.internalFormatHalfFloat}function tO(e,t,s,n){const[i,r]=Ta(t,s);return $u(e,i,r,Mx(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Ux(e){return e.downloadTextureFormat}function sO(e,t,s,n){const[i,r]=Ta(t,s);return $u(e,i,r,Ux(n),e.RGBA,e.UNSIGNED_BYTE)}function $x(e){return e.internalFormatPackedFloat}function nO(e,t,s,n){const[i,r]=wi(t,s);return $u(e,i,r,$x(n),e.RGBA,e.FLOAT)}function Wx(e){return e.internalFormatPackedHalfFloat}function iO(e,t,s,n){const[i,r]=wi(t,s);return $u(e,i,r,Wx(n),e.RGBA,n.textureTypeHalfFloat)}function rO(e,t,s){const n=0,i=3*4,r=3*4+2*4;pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s));const o=yx(e,t,"clipSpacePos",s,3,r,n);return o&&yx(e,t,"uv",s,2,r,i)}function oO(e,t,s,n,i,r){pe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,a,l;i instanceof Uint8Array?(o=new Uint8Array(s*n*4),a=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(s*n*4),a=e.FLOAT,l=r.internalFormatPackedFloat),o.set(i),pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,s,n,0,e.RGBA,a,o)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function aO(e,t,s){pe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),s.data instanceof Uint8Array?pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,s.width,s.height,0,e.RGBA,e.UNSIGNED_BYTE,s.data)):pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,s)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function lO(e,t,s,n){const i=e.createBuffer();pe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const r=4,o=4,a=r*o*t*s;return pe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ)),pe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,0)),pe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function cO(e,t,s){const n=e,i=new Float32Array(s);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,i),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),i}function pO(e,t,s,n){const[i,r]=Ta(t,s),o=4,a=new Uint8Array(iR(t*s,o));return pe(e,()=>e.readPixels(0,0,i,r,n.downloadTextureFormat,e.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function uO(e,t,s,n,i,r,o,a){const l=e,c=new Float32Array(rR(r,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function hO(e,t,s){const n=new Float32Array(t*s*4);return pe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,n)),n}class zx{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,gx(t,e)):this.gl=on(t);let s="WEBGL_color_buffer_float";const n="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",r="OES_texture_half_float";if(this.textureFloatExtension=Mu(this.gl,i),an(this.gl,r))this.textureHalfFloatExtension=Mu(this.gl,r);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(s),an(this.gl,n))this.colorBufferHalfFloatExtension=Mu(this.gl,n);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(s="EXT_color_buffer_float",an(this.gl,s))this.colorBufferFloatExtension=this.gl.getExtension(s);else if(an(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Z2(this.gl),this.indexBuffer=Q2(this.gl),this.framebuffer=fR(this.gl),this.textureConfig=Fu(this.gl,this.textureHalfFloatExtension)}get debug(){return W().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),eO(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),tO(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),sO(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),aO(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,s,n){this.throwIfDisposed(),oO(this.gl,e,t,s,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),iO(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),nO(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(bx(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,s){return this.downloadMatrixDriver(e,()=>pO(this.gl,t,s,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,s,n,i,r){return uO(this.gl,e,t,s,n,i,r,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return cO(this.gl,e,t)}createBufferFromTexture(e,t,s){this.bindTextureToFrameBuffer(e);const n=lO(this.gl,t,s,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,s;if(W().getBool("WEBGL_FENCE_API_ENABLED")){const n=e,i=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),s=()=>{const r=n.clientWaitSync(i,0,0);return r===n.ALREADY_SIGNALED||r===n.CONDITION_SATISFIED},t=i}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),s=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):s=()=>!0;return{query:t,isFencePassed:s}}downloadMatrixFromPackedTexture(e,t,s){return this.downloadMatrixDriver(e,()=>hO(this.gl,t,s))}createProgram(e){this.throwIfDisposed();const t=this.gl,s=lR(t,e),n=J2(t),i=cR(t);return pe(t,()=>t.attachShader(i,n)),pe(t,()=>t.attachShader(i,s)),pR(t,i),this.debug&&eg(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=rO(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&pe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&eg(this.gl,this.program),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,s=!0){return this.throwIfDisposed(),s?gR(this.gl,e,t):yR(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,s){this.throwIfDisposed(),this.throwIfNoProgram(),bR(this.gl,e,t,s)}setOutputMatrixTexture(e,t,s){this.setOutputMatrixTextureDriver(e,s,t)}setOutputPackedMatrixTexture(e,t,s){this.throwIfDisposed();const[n,i]=wi(t,s);this.setOutputMatrixTextureDriver(e,n,i)}setOutputMatrixWriteRegion(e,t,s,n){this.setOutputMatrixWriteRegionDriver(s,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,s,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&eg(this.gl,this.program),Uu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Mu(this.gl,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const s=this.gl,n=this.getQueryTimerExtensionWebGL2(),i=s.createQuery();return s.beginQuery(n.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,s=this.getQueryTimerExtensionWebGL2();t.endQuery(s.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await N.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const s=this.gl,n=s.getQueryParameter(e,s.QUERY_RESULT);return n/1e6}else{const s=this.getQueryTimerExtensionWebGL1(),n=s.getQueryObjectEXT(e,s.QUERY_RESULT_EXT);return n/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const s=this.gl,n=this.getQueryTimerExtensionWebGL2(),i=s.getQueryParameter(e,s.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const s=this.getQueryTimerExtensionWebGL1(),n=s.getQueryObjectEXT(e,s.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=EV(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:s}=this.itemsToPoll[t];s()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;N.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),tg(this.gl,e,this.framebuffer),this.debug&&Uu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(tg(this.gl,this.outputTexture,this.framebuffer),this.debug&&Uu(this.gl)):bx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const s=t();return this.unbindTextureToFrameBuffer(),s}setOutputMatrixTextureDriver(e,t,s){this.throwIfDisposed();const n=this.gl;tg(n,e,this.framebuffer),this.debug&&Uu(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,s)),pe(n,()=>n.scissor(0,0,t,s))}setOutputMatrixWriteRegionDriver(e,t,s,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,s,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function EV(e){let t=0;for(;t<e.length;++t){const s=e[t]();if(!s)break}return t-1}function dO(e,t,s,n){const i=t.userCode,r=s.map((d,m)=>{const f={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(f.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),o=r.map(d=>d.shapeInfo),a={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},l=VR(r,a,i,t.packedInputs),c=e.createProgram(l);let p=null;const u=e.getUniformLocation(c,"NAN",!1);W().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(c,"INFINITY",!1));const h={};for(let d=0;d<t.variableNames.length;d++){const m=t.variableNames[d],f=!1;h[m]=e.getUniformLocation(c,m,f),h[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:h,inShapeInfos:o,outShapeInfo:a,infLoc:p,nanLoc:u}}function mO(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((s,n)=>{const i=s.logicalShape,r=t[n],o=r.shape;if(!N.arraysEqual(i,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${o} must match`);if(s.isUniform&&r.isUniform)return;const a=s.texShape,l=r.isUniform?null:r.texData.texShape;if(!N.arraysEqual(a,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${l} must match`)})}function fO(e,t,s,n,i){mO(t.inShapeInfos,s),mO([t.outShapeInfo],[n]);const r=n.texData.texture,o=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(r,o[0],o[1]):e.setOutputMatrixTexture(r,o[0],o[1]),e.setProgram(t.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),s.forEach((a,l)=>{const c=t.program.variableNames[l],p=t.uniformLocations[c],u=t.uniformLocations[`offset${c}`];if(p==null)return;if(a.isUniform){if(N.sizeFromShape(a.shape)<2)e.gl.uniform1f(p,a.uniformValues[0]);else{let h=a.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),e.gl.uniform1fv(p,h)}return}a.texData.slice!=null&&u!=null&&e.gl.uniform1i(u,a.texData.slice.flatOffset),e.setInputMatrixTexture(a.texData.texture,p,l)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function gO(e,t,s){let n="";t.concat(s).forEach(o=>{const a=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0,l=o.isUniform?"uniform":o.texData.texShape;n+=`${o.shape}_${l}_${a}`});const i=e.userCode;let r=e.constructor.name;return r+="_"+n+"_"+i,r}class yO{constructor(e,t,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:n,inChannels:i,strideWidth:r,strideHeight:o,padInfo:a,outWidth:l,dilationWidth:c,dilationHeight:p,dataFormat:u}=s,{left:h,top:d}=a,m=i*n,f=ct(),g=u==="channelsLast",y=g?0:1,w=g?1:2;let x="";for(let T=0;T<=1;T++)for(let A=0;A<=1;A++)x+=`
+          blockIndex = rc.y + ${A};
+          pos = rc.x + ${T};
+
+          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
+            offsetY = int(blockIndex / (${l})) * ${o} - ${d};
+            d0 = offsetY + ${p} * (pos / ${m});
+
+            if(d0 < ${t[y]} && d0 >= 0) {
+
+              offsetX = int(mod(float(blockIndex), ${l}.) * ${r}. - ${h}.);
+              d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${i}.));
+
+              if(d1 < ${t[w]} && d1 >= 0) {
+
+                ch = int(mod(float(pos), ${i}.));
+
+                if (${g}) {
+                  innerDims = vec2(d1, ch);
+                  result[${T*2+A}] = getChannel(
+                    getA(d0, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                } else {
+                  innerDims = vec2(d0, d1);
+                  result[${T*2+A}] = getChannel(
+                    getA(ch, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                }
+              }
+            }
+          }
+        `;this.userCode=`
+      void main() {
+        ivec2 rc = getOutputCoords();
+
+        vec4 result = vec4(0);
+
+        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
+        vec2 innerDims;
+
+        ${x}
+
+        ${f.output} = result;
+      }
+    `}}class bO{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[];const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${s}) + float(${n}) * sum`;i===.5?a=`inversesqrt(${l})`:i===1?a=`1.0/(${l})`:a=`exp(log(${l}) * float(-${i}));`,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+        int d = coords[3];
+        float x = getX(b, r, c, d);
+        float sum = 0.0;
+        for (int j = -${r}; j <= ${r}; j++) {
+          int idx = d + j;
+          if (idx >= 0 && idx <=  ${o}) {
+            float z = getX(b, r, c, idx);
+            sum += z * z;
+          }
+        }
+        float val = x * ${a};
+        setOutput(val);
+      }
+    `}}class wO{constructor(e,t,s,n,i){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=s,this.alpha=n,this.beta=i,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+
+        float result = 0.0;
+        for (int d = 0; d < ${this.depth}; ++d) {
+          int depthBegin = int(max(0.0, float(d - ${t})));
+          int depthEnd = int(min(float(${this.depth}),
+              float(d + ${t} + 1)));
+
+          const int MIN_DEPTH_BEGIN = 0;
+          const int MAX_DEPTH_END = ${this.depth};
+
+          float norm = 0.0;
+          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd) {
+              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
+            }
+            else {
+              break;
+            }
+          }
+
+          norm = float(${n}) * norm + float(${s});
+
+          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd){
+              float dyi = -2.0 * float(${n})
+                * float(${i})
+                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
+                / norm;
+              if (k == d) {
+                dyi += pow(norm, -1.0 * ${i});
+              }
+              if (k == coords[3]) {
+                dyi *= getDy(b, r, c, d);
+                result += dyi;
+              }
+            }
+            else {
+              break;
+            }
+          }
+      }
+      setOutput(result);
+      }
+    `}}class xO{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${s}) + float(${n}) * sum`;i===.5?a=`inversesqrt(${l})`:i===1?a=`1.0/(${l})`:a=`exp(log(${l}) * float(-${i}));`,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords.x;
+        int r = coords.y;
+        int c = coords.z;
+        int d = coords.w;
+
+        bool hasNextCol = d < ${this.outputShape[3]};
+        bool hasNextRow = c < ${this.outputShape[2]};
+
+        vec4 sum = vec4(0.);
+        vec4 xFragAtOutputCoords = getX(b, r, c, d);
+
+        vec4 xAtOutputCoords = vec4(
+          getChannel(xFragAtOutputCoords, vec2(c, d)),
+          hasNextCol ?
+            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
+          hasNextRow ?
+            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
+        );
+
+        int firstChannel = d - ${r};
+        vec2 cache = vec2(0.);
+        if(firstChannel >= 0){
+          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
+          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
+            if(hasNextRow){
+              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
+            }
+        }
+
+        ivec2 depth = ivec2(d, d + 1);
+        for (int j = - ${r}; j <= ${r}; j++) {
+          ivec2 idx = depth + j;
+          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
+
+          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
+          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
+
+          if(depthInRange || depthPlusOneInRange){
+            vec4 z = vec4(0.);
+            vec4 xFragAtCurrentDepth;
+            z.xz = cache.xy;
+            if(depthPlusOneInRange && hasNextCol){
+              xFragAtCurrentDepth = idx.y != d ?
+                getX(b, r, c, idx.y) : xFragAtOutputCoords;
+              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
+              if(hasNextRow){
+                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
+              }
+            }
+            cache.xy = z.yw;
+            sum += z * z;
+          }
+        }
+        vec4 result = xAtOutputCoords * ${a};
+        setOutput(result);
+      }
+    `}}class LO{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,s=e.strideWidth,n=e.dilationHeight,i=e.effectiveFilterHeight,r=e.effectiveFilterWidth,o=i-1-e.padInfo.top,a=r-1-e.padInfo.left,l=i*r-1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${a});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${i};
+          wR += ${n}) {
+          float dyR = float(dyRCorner + wR) / ${t}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${r}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${s}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
+
+            // Get the current value, check it against the value from the
+            // position matrix.
+            int curPosValue = wR * ${r} + wC;
+            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+            dotProd += dyValue * mask;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class SO{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,s=e.strideHeight,n=e.strideWidth,i=e.dilationDepth,r=e.dilationHeight,o=e.dilationWidth,a=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=a-1-e.padInfo.front,u=l-1-e.padInfo.top,h=c-1-e.padInfo.left,d=a*l*c-1;this.userCode=`
+      const ivec3 pads = ivec3(${p}, ${u}, ${h});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${a};
+           wD += ${i}) {
+          float dyD = float(dyDCorner + wD) / ${t}.0;
+
+          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${l};
+              wR += ${r}) {
+            float dyR = float(dyRCorner + wR) / ${s}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${c};
+                wC += ${o}) {
+              float dyC = float(dyCCorner + wC) / ${n}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+              int maxPosValue = ${d} -
+                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
+
+              // Get the current value, check it against the value from the
+              // position matrix.
+              int curPosValue =
+                  wD * ${l} * ${c} +
+                  wR * ${c} + wC;
+              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+              dotProd += dyValue * mask;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}}class pg{constructor(e,t,s=!1,n=!1,i=!1,r=null,o=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;const a=s?e[1]:e[2],l=Math.ceil(a/2),c=s?"i * 2, rc.y":"rc.y, i * 2",p=n?"rc.z, i * 2":"i * 2, rc.z",u=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=n?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let d="",m="";r&&(o?d=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${r}
+        }`:d=`vec4 activation(vec4 x) {
+          ${r}
+        }`,m="result = activation(result);");const f=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${d}
+
+      const float sharedDimension = ${l}.0;
+
+      vec4 dot2x2ARowBCol(ivec3 rc) {
+        vec4 result = vec4(0);
+        for (int i = 0; i < ${l}; i++) {
+          vec4 a = getMatrixA(rc.x, ${c});
+          vec4 b = getMatrixB(rc.x, ${p});
+
+          // These swizzled products need to be separately added.
+          // See: https://github.com/tensorflow/tfjs/issues/1735
+          result += (${u[0]} * ${h[0]});
+          result += (${u[1]} * ${h[1]});
+        }
+        return result;
+      }
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+        vec4 result = dot2x2ARowBCol(rc);
+
+        ${f}
+
+        ${m}
+
+        setOutput(result);
+      }
+    `}}class IO{constructor(e,t,s){this.variableNames=["probs"],this.outputShape=[e,s],this.userCode=`
+      uniform float seed;
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+
+        float r = random(seed);
+        float cdf = 0.0;
+
+        for (int i = 0; i < ${t-1}; i++) {
+          cdf += getProbs(batch, i);
+
+          if (r < cdf) {
+            setOutput(float(i));
+            return;
+          }
+        }
+
+        // If no other event happened, last event happened.
+        setOutput(float(${t-1}));
+      }
+    `}getCustomSetupFunc(e){return(t,s)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(s,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}class vO{constructor(e,t,s,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int index = round(getIndices(coords.x));
+        setOutput(mix(float(${n}), float(${s}),
+                      float(index == coords.y)));
+      }
+    `}}class TO{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(t===0)this.userCode=`
+        void main() {
+          setOutput(vec4(getA(), 0., 0., 0.));
+        }
+      `;else{const s=_t("rc",t),n=Re(t),i=_V(t,e,s),r=kV(t,e[e.length-1],e[e.length-2],s),o=DV(e,s);this.userCode=`
+        void main() {
+          ${n} rc = getOutputCoords();
+
+          if(${i}) {
+            setOutput(vec4(0));
+          } else {
+            ${r}
+
+            setOutput(vec4(${o}));
+          }
+        }
+      `}}}function FV(e,t){const s=[];for(let n=0;n<=1;n++)for(let i=0;i<=1;i++){let r=`${n===0?"r":"rp1"}, ${i===0?"c":"cp1"}`;for(let o=2;o<e;o++)r=`${t[t.length-1-o]},`+r;s.push(r)}return s}function _V(e,t,s){if(e===1)return`rc > ${t[0]}`;let n="";for(let i=e-2;i<e;i++)n+=`${s[i]} >= ${t[i]}`,i<e-1&&(n+="||");return n}function kV(e,t,s,n){if(e===1)return"";const i=n.slice(-2);return`
+    int r = ${i[0]};
+    int c = ${i[1]};
+    int rp1 = r + 1;
+    int cp1 = c + 1;
+
+    bool cEdge = cp1 >= ${t};
+    bool rEdge = rp1 >= ${s};
+  `}function DV(e,t){const s=e.length,n=FV(s,t);return s===1?`getA(rc),
+            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
+            0, 0`:`getA(${n[0]}),
+          cEdge ? 0. : getA(${n[1]}),
+          rEdge ? 0. : getA(${n[2]}),
+          rEdge || cEdge ? 0. : getA(${n[3]})`}class AO{constructor(e,t,s){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);const n=e.length,i=Re(n),r=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),a=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+        int start = ${r};
+        int end = ${o};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start || outC >= end) {
+            setOutput(float(${s}));
+          } else {
+            setOutput(getX(outC - start));
+          }
+        }
+      `;return}this.userCode=`
+      ${i} start = ${i}(${r});
+      ${i} end = ${i}(${o});
+
+      void main() {
+        ${i} outC = getOutputCoords();
+        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
+          setOutput(float(${s}));
+        } else {
+          ${i} coords = outC - start;
+          setOutput(getX(${a}));
+        }
+      }
+    `}}class NO{constructor(e,t,s){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);const n=e.length,i=Re(n),r=t.map(m=>m[0]).join(","),o=t.map((m,f)=>m[0]+e[f]).join(","),a=_t("rc",n),l=_t("source",n),c=`${a[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,u=[`${i} rc = outputLoc;`,`${a[n-1]} += 1;
+       if(${c}) {
+      `,n===1?"":`}
+       rc = outputLoc;
+       ${a[n-2]} += 1;
+       if(${a[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":`  ${a[n-1]} += 1;
+         if(${c}) {`],h=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let d="";for(let m=0,f=n===1?2:4;m<f;m++)d+=`
+        ${u[m]}
+        if (${h}) {
+          result[${m}] = float(${s});
+        } else {
+          ${i} source = rc - start;
+          result[${m}] = getChannel(getX(${l.join()}), ${p});
+        }
+      `;d+=n===1?"} ":"}}",this.userCode=`
+      const ${i} start = ${i}(${r});
+      const ${i} end = ${i}(${o});
+
+      void main() {
+        ${i} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${d}
+        setOutput(result);
+      }
+    `}}class Zi{constructor(e,t,s,n=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&s)throw new Error("Cannot compute positions for average pool.");const r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,h=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;const m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(m||(y="-1.0 / 1e-20"),s){const E=">=";this.userCode=`
+        const ivec2 strides = ivec2(${o}, ${a});
+        const ivec2 pads = ivec2(${h}, ${d});
+
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int batch = coords[0];
+          int d = coords[3];
+
+          ivec2 xRCCorner = coords.yz * strides - pads;
+          int xRCorner = xRCCorner.x;
+          int xCCorner = xRCCorner.y;
+
+          // max/min x(?, ?, d) to get y(yR, yC, d).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+          float avgValue = 0.0;
+
+          for (int wR = 0; wR < ${p};
+              wR += ${l}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${u};
+                wC += ${c}) {
+              int xC = xCCorner + wC;
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              float value = getX(batch, xR, xC, d);
+
+              // If a min / max value has already been found, use it. If not,
+              // use the current value.
+              float currMinMaxValue = mix(
+                  value, minMaxValue, minMaxValueFound);
+              if (value ${E} currMinMaxValue) {
+                minMaxValue = value;
+                minMaxValueFound = 1.0;
+                minMaxPosition = ${n?i?f:g:`wR * ${u} + wC`};
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}const w="max";let x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");const T=Math.floor(r/4)*4,A=r%4,_=`
+      if (${m}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${w}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec2 strides = ivec2(${o}, ${a});
+      const ivec2 pads = ivec2(${h}, ${d});
+      const float initializationValue = ${y};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xR, int xC, int d) {
+        if (xC < 0 || xC >= ${e.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xR, xC, d);
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d = coords[3];
+
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // max/min x(?, ?, d) to get y(yR, yC, d).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${y});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wR = 0; wR < ${p};
+            wR += ${l}) {
+          int xR = xRCorner + wR;
+
+          if (xR < 0 || xR >= ${e.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${T}; wC += 4) {
+            int xC = xCCorner + wC * ${c};
+
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + 2 * ${c}, d),
+              getValue(batch, xR, xC + 3 * ${c}, d)
+            );
+
+            ${_}
+          }
+
+          int xC = xCCorner + ${T};
+          if (${A===1}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              initializationValue,
+              initializationValue,
+              initializationValue
+            );
+
+            ${_}
+          } else if (${A===2}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${c}, d),
+              initializationValue,
+              initializationValue
+            );
+
+            ${_}
+          } else if (${A===3}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + 2 * ${c}, d),
+              initializationValue
+            );
+
+            ${_}
+          }
+        }
+        setOutput(${x});
+      }
+    `}}class ug{constructor(e,t,s,n=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&s)throw new Error("Cannot compute positions for average pool.");const r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,u=e.dilationWidth,h=e.effectiveFilterDepth,d=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const w=t==="avg";let x="0.0";if(w||(x="-1.0 / 1e-20"),s){const D=">=";this.userCode=`
+        const ivec3 strides =
+            ivec3(${o}, ${a}, ${l});
+        const ivec3 pads = ivec3(${f}, ${g}, ${y});
+
+        void main() {
+          ivec5 coords = getOutputCoords();
+          int batch = coords.x;
+          int ch = coords.u;
+
+          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+          int xDCorner = xCorner.x;
+          int xRCorner = xCorner.y;
+          int xCCorner = xCorner.z;
+
+          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+
+          for (int wD = 0; wD < ${h};
+              wD += ${c}) {
+            int xD = xDCorner + wD;
+
+            if (xD < 0 || xD >= ${e.inDepth}) {
+              continue;
+            }
+
+            for (int wR = 0; wR < ${d};
+                wR += ${p}) {
+              int xR = xRCorner + wR;
+
+              if (xR < 0 || xR >= ${e.inHeight}) {
+                continue;
+              }
+
+              for (int wC = 0; wC < ${m};
+                  wC += ${u}) {
+                int xC = xCCorner + wC;
+
+                if (xC < 0 || xC >= ${e.inWidth}) {
+                  continue;
+                }
+
+                float value = getX(batch, xD, xR, xC, ch);
+
+                // If a min / max value has already been found, use it. If not,
+                // use the current value.
+                float currMinMaxValue = mix(
+                    value, minMaxValue, minMaxValueFound);
+                if (value ${D} currMinMaxValue) {
+                  minMaxValue = value;
+                  minMaxValueFound = 1.0;
+                  minMaxPosition = ${n?i?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${m} +
+                      wR * ${m} + wC`};
+                }
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}const T="max";let A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");const _=Math.floor(r/4)*4,E=r%4,F=`
+      if (${w}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${T}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec3 strides =
+        ivec3(${o}, ${a}, ${l});
+      const ivec3 pads = ivec3(${f}, ${g}, ${y});
+      const float initializationValue = ${x};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xD, int xR, int xC, int ch) {
+        if (xC < 0 || xC >= ${e.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xD, xR, xC, ch);
+      }
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xDCorner = xCorner.x;
+        int xRCorner = xCorner.y;
+        int xCCorner = xCorner.z;
+
+        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${x});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wD = 0; wD < ${h};
+            wD += ${c}) {
+          int xD = xDCorner + wD;
+
+          if (xD < 0 || xD >= ${e.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${d};
+            wR += ${p}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${_}; wC += 4) {
+              int xC = xCCorner + wC * ${u};
+
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${u}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${u}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${u}, ch)
+              );
+
+              ${F}
+            }
+
+            int xC = xCCorner + ${_};
+            if (${E===1}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                initializationValue,
+                initializationValue,
+                initializationValue
+              );
+
+              ${F}
+            } else if (${E===2}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${u}, ch),
+                initializationValue,
+                initializationValue
+              );
+
+              ${F}
+            } else if (${E===3}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${u}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${u}, ch),
+                initializationValue
+              );
+
+              ${F}
+            }
+          }
+          setOutput(${A});
+        }
+      }
+    `}}class hg{constructor(e,t){this.variableNames=["x"];const{windowSize:s,batchSize:n,inSize:i,outSize:r}=e;this.outputShape=[n,r];let o="0.0",a="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",a="min"):t==="max"&&(o="-1.0 / 1e-20",a="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");const c=Math.floor(s/4)*4,p=s%4;let u=`
+      if (${t==="sum"}) {
+        sumValue += dot(values, ones);
+      } else if (${t==="prod"}) {
+        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
+        prodValue *= tmp[0] * tmp[1];
+      } else {
+        minMaxValue = ${a}(values, minMaxValue);
+      }
+    `,h="vec4";t==="all"?(o="1.0",u=`
+        bool reducedAllValue = all(values);
+        float floatedReducedAllValue = float(reducedAllValue);
+        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
+      `,h="bvec4"):t==="any"&&(o="0.0",u=`
+        bool reducedAnyValue = any(values);
+        float floatedReducedAnyValue = float(reducedAnyValue);
+        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
+      `,h="bvec4");let d="";i%s>0&&(d=`
+        if (inIdx < 0 || inIdx >= ${i}) {
+          return initializationValue;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${o};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${d}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${s};
+
+        vec4 minMaxValue = vec4(${o});
+        float prodValue = 1.0;
+        float sumValue = 0.0;
+        float allValue = 1.0;
+        float anyValue = 0.0;
+
+        for (int i = 0; i < ${c}; i += 4) {
+          int inIdx = inOffset + i;
+          ${h} values = ${h}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${u}
+        }
+
+        int inIdx = inOffset + ${c};
+        if (${p===1}) {
+          ${h} values = ${h}(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          ${u}
+        } else if (${p===2}) {
+          ${h} values = ${h}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          ${u}
+        } else if (${p===3}) {
+          ${h} values = ${h}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          ${u}
+        }
+        setOutput(${l});
+      }
+    `}}class dg{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let s="";for(let n=0;n<4;n++){let i="thisRC = rc;";n%2===1&&(i+="thisRC.z += 1;"),n>1&&(i+="thisRC.y += 1;"),s+=`
+        ${i}
+        ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+          int flatIndex = getFlatIndex(thisRC);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
+          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${n}] =
+            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
+        ${n>0?"}":""}
+      `}this.userCode=`
+      ${MV(t)}
+      ${nc(e)}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.);
+
+        ivec3 thisRC;
+        int rows = ${e[1]};
+        int cols = ${e[2]};
+
+        ${s}
+
+        setOutput(result);
+      }
+    `}}function MV(e){const t=Hn(["r","c","d"],e);return`
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${t}
+      return ivec3(r, c, d);
+    }
+  `}class CO{constructor(e,t,s){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,n,i]=t.shape,[,r,o]=e.shape,a=[s&&r>1?n-1:n,s&&o>1?i-1:i],l=[s&&r>1?r-1:r,s&&o>1?o-1:o],c=a[0]/l[0],p=a[1]/l[1],u=1/c,h=1/p,d=Math.ceil(u)*2+2,m=Math.ceil(h)*2+2;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${c});
+        const float widthScale = float(${p});
+
+        const float invHeightScale = float(${u});
+        const float invWidthScale = float(${h});
+
+        const int winHeight = int(${d});
+        const int winWidth = int(${m});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(startRLerp - float(winHeight / 2));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(startCLerp - float(winWidth / 2));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${r}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${o}) {
+              continue;
+            }
+
+            float dxR = float(dyR) * heightScale;
+            int topDxRIndex = int(floor(dxR));
+            int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));
+            float dxRLerp = dxR - float(topDxRIndex);
+            float inverseDxRLerp = 1.0 - dxRLerp;
+
+            float dxC = float(dyC) * widthScale;
+            int leftDxCIndex = int(floor(dxC));
+            int rightDxCIndex = int(min(ceil(dxC), ${i-1}.0));
+            float dxCLerp = dxC - float(leftDxCIndex);
+            float inverseDxCLerp = 1.0 - dxCLerp;
+
+            if (r == topDxRIndex && c == leftDxCIndex) {
+              // topLeft
+              accumulator +=
+                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
+            }
+
+            if (r == topDxRIndex && c == rightDxCIndex) {
+              // topRight
+              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == leftDxCIndex) {
+              // bottomLeft
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == rightDxCIndex) {
+              // bottomRight
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `}}class RO{constructor(e,t,s,n){this.variableNames=["A"],this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s];this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${l[0]/c[0]},
+          ${l[1]/c[1]});
+      const vec2 inputShapeRC = vec2(${r}.0, ${o}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
+
+        // Compute the four integer indices.
+        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
+        ivec2 sourceCeilRC = ivec2(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
+        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
+        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
+        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
+
+        float top = topLeft + (topRight - topLeft) * fracRC.y;
+        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+        float newValue = top + (bottom - top) * fracRC.x;
+
+        setOutput(newValue);
+      }
+    `}}class OO{constructor(e,t,s,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s];this.userCode=`
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${l[0]/c[0]},
+          ${l[1]/c[1]},
+          ${l[1]/c[1]});
+      const vec3 inputShapeRC = vec3(${r}.0, ${o}.0,
+                                     ${o}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;
+
+        // Compute the four integer indices.
+        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
+        ivec3 sourceCeilRC = ivec3(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${a-1};
+        bool hasNextRow = coords.z < ${s-1};
+
+        // In parallel, construct four corners for all four components in
+        // packed 2x2 cell.
+        vec4 topLeft = vec4(
+          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 bottomLeft = vec4(
+          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 topRight = vec4(
+          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec4 bottomRight = vec4(
+          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
+
+        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
+        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
+        vec4 newValue = mix(top, bottom, fracRC.x);
+
+        setOutput(newValue);
+      }
+    `}}class EO{constructor(e,t,s){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,n,i]=t.shape,[,r,o]=e.shape,a=[s&&r>1?n-1:n,s&&o>1?i-1:i],l=[s&&r>1?r-1:r,s&&o>1?o-1:o],c=a[0]/l[0],p=a[1]/l[1],u=1/c,h=1/p,d=Math.ceil(u)*2+2,m=Math.ceil(h)*2+2;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${c});
+        const float widthScale = float(${p});
+
+        const float invHeightScale = float(${u});
+        const float invWidthScale = float(${h});
+
+        const int winHeight = int(${d});
+        const int winWidth = int(${m});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${r}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${o}) {
+              continue;
+            }
+
+            float sourceFracRow =
+              float(${a[0]}) *
+                (float(dyR) / float(${l[0]}));
+
+            float sourceFracCol =
+                float(${a[1]}) *
+                  (float(dyC) / float(${l[1]}));
+
+            int sourceNearestRow = int(min(
+                float(int(${n}) - 1),
+                ${s} ? float(round(sourceFracRow)) :
+                                  float(floor(sourceFracRow))));
+
+            int sourceNearestCol = int(min(
+                float(int(${i}) - 1),
+                ${s} ? float(round(sourceFracCol)) :
+                                  float(floor(sourceFracCol))));
+
+            if (r == sourceNearestRow && c == sourceNearestCol) {
+              accumulator += getDy(b, dyR, dyC, d);
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `}}class _O{constructor(e,t,s,n){this.variableNames=["A"],this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s],p=n?"0.5":"0.0";this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${l[0]/c[0]},
+          ${l[1]/c[1]});
+      const vec2 inputShapeRC = vec2(${r}.0, ${o}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec2 sourceNearestRC = ivec2(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
+
+        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+        setOutput(newValue);
+      }
+    `}}class kO{constructor(e,t){this.variableNames=["x"];const s=e.length;if(s>4)throw new Error(`WebGL backend: Reverse of rank-${s} tensor is not yet supported`);if(this.outputShape=e,s===1){this.userCode=`
+        void main() {
+          int coord = getOutputCoords();
+          setOutput(getX(${e[0]} - coord - 1));
+        }
+      `;return}const n=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,i=e.map((o,a)=>n(a)).join(","),r=Re(s);this.userCode=`
+      void main() {
+        ${r} coords = getOutputCoords();
+        setOutput(getX(${i}));
+      }
+    `}}class DO{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const s=e.length;if(s>4)throw new Error(`WebGL backend: Reverse of rank-${s} tensor is not yet supported`);this.outputShape=e;const n=_t("rc",s),i=`${n[s-1]} + 1 < ${this.outputShape[s-1]}`,r=`${n[s-2]} + 1 < ${this.outputShape[s-2]}`,o=Re(s);s===1?this.userCode=`
+        void main(){
+          int rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = getChannel(getX(${e[0]} - rc - 1),
+            ${e[0]} - rc - 1);
+          if(${i}){
+              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
+                ${e[0]} - (rc  + 1) - 1);
+          }
+          setOutput(result);
+        }
+      `:this.userCode=`
+        void main() {
+          ${o} rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = ${a(n.slice())};
+          if(${i}){
+            result.g = ${l(n.slice())};
+          }
+          if(${r}) {
+            result.b = ${c(n.slice())};
+            if(${i}) {
+              result.a = ${p(n.slice())};
+            }
+          }
+          setOutput(result);
+        }
+    `;function a(d){return u(d)}function l(d){return d[s-1]="("+d[s-1]+" + 1)",u(d)}function c(d){return d[s-2]="("+d[s-2]+" + 1)",u(d)}function p(d){return d[s-1]="("+d[s-1]+" + 1)",d[s-2]="("+d[s-2]+" + 1)",u(d)}function u(d){const m=e.map((y,w)=>h(w,d)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function h(d,m){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${m[d]} - 1`:`${m[d]}`}}}class Px{constructor(e,t,s,n,i,r,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=r;const a=Re(i.length),l=Re(r.length);let c="";s===1?c="i":s===2&&(c="i, j");const p=`getIndices(${c})`;let u="";n===1?u="i":n===2&&(u="i, coords[1]");const h=`getUpdates(${u})`,d=t>1?"strides[j]":"strides";this.userCode=`
+        ${a} strides = ${a}(${i});
+
+        void main() {
+          ${l} coords = getOutputCoords();
+          float sum = 0.0;
+          bool found = false;
+          for (int i = 0; i < ${e}; i++) {
+            int flattenedIndex = 0;
+            for (int j = 0; j < ${t}; j++) {
+              int index = round(${p});
+              flattenedIndex += index * ${d};
+            }
+            if (flattenedIndex == coords[0]) {
+              sum += ${h};
+              found = true;
+            }
+          }
+          setOutput(mix(getDefaultValue(), sum, float(found)));
+        }
+      `}}class FO{constructor(e,t){this.variableNames=["x","segmentIds"];const s=e.windowSize,n=e.batchSize,i=e.inSize,r=e.numSegments,o=r*Math.ceil(i/s);this.outputShape=[n,o];const a="0.0",l="sumValue",c=Math.floor(s/4)*4,p=s%4,u=`
+        sumValue += dot(values, segFilter);
+    `;let h="";i%s>0&&(h=`
+        if (inIdx < 0 || inIdx >= ${i}) {
+          return initializationValue;
+        }
+      `);let d="";i%s>0&&(d=`
+        if (inIdx < 0 || inIdx >= ${i}) {
+          return -1.0;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${a};
+
+      float getValue(int batch, int inIdx) {
+        ${h}
+        return getX(batch, inIdx);
+      }
+
+      float getSegmentIdAtIndex(int inIdx) {
+        ${d}
+        return getSegmentIds(inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = int(floor(float(outIdx) / float(
+          ${r})) * float(${s}));
+        int currentSeg = int(mod(float(outIdx), float(${r})));
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${c}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
+          );
+
+          ${u}
+        }
+
+        int inIdx = inOffset + ${c};
+        if (${p===1}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            0,
+            0,
+            0
+          );
+
+          ${u}
+        } else if (${p===2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+              0,
+              0
+          );
+
+          ${u}
+        } else if (${p===3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            0
+          );
+
+          ${u}
+        }
+        setOutput(${l});
+      }
+    `}}class MO{constructor(e,t,s){this.variableNames=["c","a","b"],this.outputShape=t;let n,i;if(s>4)throw Error(`Where for rank ${s} is not yet supported`);if(s===1)i="resRC",n="resRC";else{const o=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&a.push(`${o[c]}`);n=a.join(),i=l.join()}const r=Re(s);this.userCode=`
+      void main() {
+        ${r} resRC = getOutputCoords();
+        float cVal = getC(${n});
+        if (cVal >= 1.0) {
+          setOutput(getA(${i}));
+        } else {
+          setOutput(getB(${i}));
+        }
+      }
+    `}}class UO{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Re(this.rank),s=`uniform int start[${this.rank}];`,n=UV(this.rank);let i;const r=e.map((o,a)=>`sourceLoc.${Bx[a]} = start[${a}] + coords.${Bx[a]};`);i=`
+        ${t} sourceLoc;
+        ${t} coords = getOutputCoords();
+        ${r.join(`
+`)}
+      `,this.userCode=`
+      ${s}
+      void main() {
+        ${i}
+        setOutput(getSource(${n}));
+      }
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,s)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(s,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}const Bx=["x","y","z","w","u","v"];function UV(e){if(e===1)return"sourceLoc";if(e<=6)return Bx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class $O{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Re(this.rank),s=_t("coords",this.rank),n=_t("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,r=`getChannel(getSource(${n.join()}), ${i})`,o=`
+      result.x = ${r};
+      if (++${s[this.rank-1]} < ${e[this.rank-1]}) {
+        ++${n[this.rank-1]};
+        result.y = ${r};
+        --${n[this.rank-1]};
+      }
+    `,a=this.rank===1?"":`
+      --${s[this.rank-1]};
+      if (++${s[this.rank-2]} < ${e[this.rank-2]}) {
+        ++${n[this.rank-2]};
+        result.z = ${r};
+        if (++${s[this.rank-1]} < ${e[this.rank-1]}) {
+          ++${n[this.rank-1]};
+          result.w = ${r};
+        }
+      }
+    `,l=this.rank<=4?`sourceLoc = coords +
+            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${s[p]} + start[${p}];`).join(`
+`);this.userCode=`
+      uniform int start[${this.rank}];
+      void main() {
+        ${t} coords = getOutputCoords();
+        ${t} sourceLoc;
+        ${l}
+        vec4 result = vec4(0.);
+        ${o}
+        ${a}
+        setOutput(result);
+      }
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,s)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(s,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class WO{constructor(e,t,s){this.variableNames=["x"],this.outputShape=s;const n=s.length,i=Re(s.length),r=Re(s.length);let o="";if(n===1)o="coords * strides + begin";else{let a=0;o=s.map((l,c)=>(a++,s.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${a-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+      ${i} begin = ${i}(${e});
+      ${i} strides = ${i}(${t});
+
+      void main() {
+        ${r} coords = getOutputCoords();
+        setOutput(getX(${o}));
+      }
+    `}}class jO{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,s){const n=PO(t,s),i=BO(e,n,s);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const r=zO(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,s);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=r,this.log();const a=this.freeTextures[i].shift();return this.usedTextures[i].push(a),a}let o;return n===Xt.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Xt.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Xt.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Xt.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Xt.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(o),this.numUsedTextures++,this._numBytesAllocated+=r,this.log(),o}releaseTexture(e,t,s,n){if(this.freeTextures==null)return;const i=PO(s,n),r=BO(t,i,n);r in this.freeTextures||(this.freeTextures[r]=[]);const o=zO(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,n),a=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");a!==-1&&this._numBytesAllocated>a?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[r].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;const l=this.usedTextures[r],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function $V(e,t){const s=e;if(t===s.R32F)return 4;if(t===s.R16F)return 2;if(t===s.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===s.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function zO(e,t,s,n,i){const r=WV(t,n);let o;if(i){const[l,c]=wi(e[0],e[1]);o=l*c}else{const[l,c]=Ta(e[0],e[1]);o=l*c}const a=$V(s,r);return o*a}function WV(e,t){switch(e){case Xt.PACKED_2X2_FLOAT32:return $x(t);case Xt.PACKED_2X2_FLOAT16:return Wx(t);case Xt.UNPACKED_FLOAT32:return Fx(t);case Xt.UNPACKED_FLOAT16:return Mx(t);case Xt.PACKED_4X1_UNSIGNED_BYTE:return Ux(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function zV(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Xt.PACKED_2X2_FLOAT32:Xt.UNPACKED_FLOAT32:e?Xt.PACKED_2X2_FLOAT16:Xt.UNPACKED_FLOAT16}function PO(e,t){if(e===is.UPLOAD)return Xt.PACKED_2X2_FLOAT32;if(e===is.RENDER||e==null)return zV(t);if(e===is.DOWNLOAD||e===is.PIXELS)return Xt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function BO(e,t,s){return`${e[0]}_${e[1]}_${t}_${s}`}class VO{constructor(e,t){this.variableNames=["A"];const s=new Array(e.length);for(let r=0;r<s.length;r++)s[r]=e[r]*t[r];this.outputShape=s,this.rank=s.length;const n=Re(this.rank),i=PV(e);this.userCode=`
+      void main() {
+        ${n} resRC = getOutputCoords();
+        setOutput(getA(${i}));
+      }
+    `}}function PV(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;const s=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let i=0;i<e.length;i++)n.push(`imod(${s[i]}, ${e[i]})`);return n.join()}class $e{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+      float unaryOperation(float x) {
+        ${t}
+      }
+
+      void main() {
+        float x = getAAtOutCoords();
+        float y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}}const Qi="if (isnan(x)) return x;",GO="return x;",jx="return abs(x);",Vx=Qi+`
+  return (x < 0.0) ? 0.0 : x;
+`,Gx=Qi+`
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`,qx="return (x >= 0.0) ? x : (exp(x) - 1.0);",qO=`
+  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
+  // see: https://arxiv.org/abs/1706.02515
+  float scaleAlpha = ${U.SELU_SCALEALPHA};
+  float scale = ${U.SELU_SCALE};
+  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
+`;function HO(e=0){return Qi+`
+    return x > 0.0 ? 1.0 : float(${e});
+  `}const Hx="return -x;",Yx="return ceil(x);",Kx="return floor(x);",YO=`
+  if (isnan(x)) { return 0.0; }
+  return sign(x);
+`,KO="return float(isnan(x));",XO="return float(isinf(x));",JO="return float(!isnan(x) && !isinf(x));",ZO=`
+  // OpenGL ES does not support round function.
+  // The algorithm is based on banker's rounding.
+  float base = floor(x);
+  if ((x - base) < 0.5) {
+    return floor(x);
+  } else if ((x - base) > 0.5) {
+    return ceil(x);
+  } else {
+    if (mod(base, 2.0) == 0.0) {
+      return base;
+    } else {
+      return base + 1.0;
+    }
+  }
+`,Xx="return exp(x);",Jx="return exp(x) - 1.0;",QO=`if (x < 0.0) return NAN;
+  return log(x);`,e1="return log(1.0 + x);",t1="return sqrt(x);",s1="return inversesqrt(x);",n1="return 1.0 / (1.0 + exp(-1.0 * x));",i1=`
+  float epsilon = 1.1920928955078125e-7;
+  float threshold = log(epsilon) + 2.0;
+
+  bool too_large = x > -threshold;
+  bool too_small = x < threshold;
+
+  float result;
+  float exp_x = exp(x);
+
+  if (too_large){
+    result = x;
+  }
+  else if (too_small){
+    result = exp_x;
+  }
+  else{
+    result = log(exp_x + 1.0);
+  }
+  return result;
+`,r1=Qi+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return asin(x);
+`,o1=Qi+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return acos(x);
+`,a1=Qi+`
+  return atan(x);
+`,l1=`
+  float e2x = exp(x);
+  return (e2x - 1.0 / e2x) / 2.0;
+`,c1=`
+  float e2x = exp(-x);
+  return (e2x + 1.0 / e2x) / 2.0;
+`,p1=`
+  float e2x = exp(-2.0 * abs(x));
+  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
+`,u1=Qi+"return log(x + sqrt(x * x + 1.0));",h1=Qi+`
+  if (x < 1.0) return NAN;
+  return log(x + sqrt(x * x - 1.0));`,d1=Qi+`
+  if ((x < -1.0) || (x > 1.0)) return NAN;
+  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,m1=`
+  // Error function is calculated approximately with elementary function.
+  // See "Handbook of Mathematical Functions with Formulas,
+  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
+  float p = ${U.ERF_P};
+  float a1 = ${U.ERF_A1};
+  float a2 = ${U.ERF_A2};
+  float a3 = ${U.ERF_A3};
+  float a4 = ${U.ERF_A4};
+  float a5 = ${U.ERF_A5};
+
+  float sign = sign(x);
+  x = abs(x);
+  float t = 1.0 / (1.0 + p * x);
+  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
+`,f1="return 1.0 / x;",g1="return float(!(x >= 1.0));",y1="return float(int(x));",Wu="return x;";const b1="return x;",w1=`
+  vec4 result = log(x);
+  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
+  result.r = isNaN.r == 1.0 ? NAN : result.r;
+  result.g = isNaN.g == 1.0 ? NAN : result.g;
+  result.b = isNaN.b == 1.0 ? NAN : result.b;
+  result.a = isNaN.a == 1.0 ? NAN : result.a;
+
+  return result;
+`,Zx=`
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,Qx=`
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,eL=`
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`;class lc{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+      vec4 unaryOperation(vec4 x) {
+        ${t}
+      }
+
+      void main() {
+        vec4 x = getAAtOutCoords();
+        vec4 y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}}class x1{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,s=_t("rc",t),n=Re(t),i=BR(t,s),r=s.slice(-2),o=t<=1?"rc":`vec2(${r.join(",")})`;this.userCode=`
+      void main() {
+        ${n} rc = getOutputCoords();
+        vec4 packedInput = getA(${i});
+
+        setOutput(getChannel(packedInput, ${o}));
+      }
+    `}}const{segment_util:L1}=U,BV=vt.split,jV=vt.tile,VV=vt.topkImpl,GV=vt.whereImpl,qV=1e-7,HV=1e-4,mg={};function YV(e){return e in mg||(mg[e]={}),mg[e]}function fg(e,t=!1){if(e==="linear")return t?b1:GO;if(e==="relu")return t?Zx:Vx;if(e==="elu")return t?eL:qx;if(e==="relu6")return t?Qx:Gx;if(e==="prelu")return t?Cx:Nx;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const KV=128,XV=600;function JV(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*XV/1024/1024}const S1=1e3;class tL extends Eo{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=on(W().getNumber("WEBGL_VERSION"));this.binaryCache=YV(W().getNumber("WEBGL_VERSION")),this.gpgpu=new zx(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new jO(this.gpgpu),this.numMBBeforeWarning=JV(),this.texData=new Fc(this,Us())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,s){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),s==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const n={};return this.texData.set(n,{shape:t,dtype:s,values:e,usage:is.UPLOAD,refCount:1}),n}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,s,n){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:s,dtype:n,values:t,usage:is.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const s=this.texData.get(t);s.refCount--,s.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:s,dtype:n,complexTensors:i,slice:r,shape:o,isPacked:a}=t;if(r!=null){let u;a?u=new lc(o,Wu):u=new $e(o,Wu);const h=this.runWebGLProgram(u,[{dataId:e,shape:o,dtype:n}],n),d=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),d}if(s!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return s;const l=this.activeTimers!=null;let c;l&&(c=N.now());let p;if(n==="complex64"){const u=i.real.dataSync(),h=i.imag.dataSync();p=U.mergeRealAndImagArrays(u,h)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=N.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){const d=this.pendingRead.get(e);return new Promise(m=>d.push(m))}const t=this.texData.get(e),{values:s,shape:n,slice:i,dtype:r,complexTensors:o,isPacked:a}=t;if(i!=null){let d;a?d=new lc(n,Wu):d=new $e(n,Wu);const m=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:r}],r),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(s!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(r!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);const d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture,...xo(n))}this.pendingRead.set(e,[]),r!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(r==="complex64"){const d=await Promise.all([o.real.data(),o.imag.data()]),m=d[0],f=d[1];p=U.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{const d=N.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}c!=null&&this.disposeIntermediateTensorInfo(c);const u=this.convertAndCacheOnCPU(e,p),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(d=>d(u)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),u}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const s=e[t];if(!oR(s))throw W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${s} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${s} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:s,isPacked:n}=this.texData.get(e),i=N.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const u=this.decode(e),h=this.texData.get(u.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(h.texture,...xo(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(u),d}const r=W().getBool("WEBGL_PACK")&&n===!0,o=r?sg(t):t,a=r?new G2(o):new V2(o),l=this.runWebGLProgram(a,[{shape:o,dtype:s,dataId:e}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(l),p}async time(e){const t=this.activeTimers,s=[];let n=!1;this.programTimersStack==null?(this.programTimersStack=s,n=!0):this.activeTimers.push(s),this.activeTimers=s,e();const i=N.flatten(this.activeTimers.map(a=>a.query)).filter(a=>a!=null),r=N.flatten(this.activeTimers.map(a=>a.name)).filter(a=>a!=null);this.activeTimers=t,n&&(this.programTimersStack=null);const o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const a=await Promise.all(i);o.kernelMs=N.sum(a),o.getExtraProfileInfo=()=>a.map((l,c)=>({name:r[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:N.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=N.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:s,texShape:n,usage:i,isPacked:r,slice:o}=this.texData.get(e),a=o&&o.origDataId||e,l=this.dataRefCount.get(a);l>1?this.dataRefCount.set(a,l-1):(this.dataRefCount.delete(a),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,s),this.textureManager.releaseTexture(t,n,i,r)));const c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Us().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=KV){const s=this.getCPUBackend();return!this.warnedAboutCPUBackend&&s==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),s!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&N.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const s=this.makeOutput(e.shape,"complex64"),n=this.texData.get(s.dataId);return n.complexTensors={real:Us().keep(e.clone()),imag:Us().keep(t.clone())},s}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,s){if(this.shouldExecuteOnCPU([e])){const r=MR(this.texData.get(e.dataId).values,t,s,e.shape,e.dtype);return this.makeOutput(s,e.dtype,r)}if(N.sizeFromShape(s)===0)return ze([],s,e.dtype);const{isPacked:n}=this.texData.get(e.dataId),i=Ms.isSliceContinous(e.shape,t,s);if(n||!i){const r=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $O(s):new UO(s),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,s)}shallowSlice(e,t,s){const n=this.texData.get(e.dataId),i=this.makeOutput(s,e.dtype),r=this.texData.get(i.dataId);Object.assign(r,n),r.shape=s,r.dtype=e.dtype;let o=Ms.computeFlatOffset(t,e.strides);n.slice&&(o+=n.slice.flatOffset),r.slice={flatOffset:o,origDataId:n.slice&&n.slice.origDataId||e.dataId};const a=this.dataRefCount.get(r.slice.origDataId)||1;return this.dataRefCount.set(r.slice.origDataId,a+1),i}stridedSlice(e,t,s,n){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,s,n));if(i)return i;const r=Ms.computeOutShape(t,s,n);if(r.some(a=>a===0))return ze([],r);const o=new WO(t,n,r);return this.compileAndRun(o,[e])}reverse(e,t){const s=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DO(e.shape,t):new kO(e.shape,t);return this.compileAndRun(s,[e])}concat(e,t){if(e[0].dtype==="complex64"){const o=e.map(l=>Zs(l)),a=e.map(l=>yn(l));return Gt(this.concat(o,t),this.concat(a,t))}if(e.length===1)return e[0];if(e.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.concat(e.slice(0,o),t),l=this.concat(e.slice(o),t);return this.concat([a,l],t)}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const o=new R2(e.map(a=>a.shape),t);return this.compileAndRun(o,e)}const s=U.computeOutShape(e.map(o=>o.shape),t),n=e.map(o=>o.as2D(-1,N.sizeFromShape(o.shape.slice(t)))),i=new C2(n.map(o=>o.shape)),r=this.compileAndRun(i,n);return r.reshape(s)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Hx,e.dtype);const s=new $e(e.shape,Hx);return this.compileAndRun(s,[e])}batchMatMul(e,t,s,n){const i=s?e.shape[2]:e.shape[1],r=n?t.shape[1]:t.shape[2],o=s?e.shape[1]:e.shape[2],[a,,]=e.shape;if((i===1||r===1)&&o>S1){s&&(e=se(e,[0,2,1])),n&&(t=se(t,[0,2,1]));const p=r===1?e:e.as3D(a,o,1),u=r===1?2:1,h=r===1?t.as3D(a,1,o):t;return this.multiply(p,h).sum(u,!0)}const l=Ft(e.dtype,t.dtype),c=new pg(e.shape,[a,i,r],s,n);return this.compileAndRun(c,[e,t],l)}fusedBatchMatMul({a:e,b:t,transposeA:s,transposeB:n,bias:i,activation:r,preluActivationWeights:o}){const a=s?e.shape[2]:e.shape[1],l=n?t.shape[1]:t.shape[2],[c,,]=e.shape,p=Ft(e.dtype,t.dtype),u=i!=null,h=o!=null,d=r?fg(r,!0):null,m=new pg(e.shape,[c,a,l],s,n,u,d,h),f=[e,t];return i&&f.push(i),o&&f.push(o),this.compileAndRun(m,f,p)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new Tx(vx.REAL,e.shape,t.shape),a=new Tx(vx.IMAG,e.shape,t.shape),l=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,r.complexTensors.real),this.makeComplexComponentTensorInfo(t,r.complexTensors.imag)],c=this.compileAndRun(o,l),p=this.compileAndRun(a,l),u=this.complex(c,p);return c.dispose(),p.dispose(),u}const s=Ft(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),[o,a]=DR(e.shape,t.shape,i.values,r.values,s);return this.makeOutput(a,s,o)}if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,Ax,e.dtype);const n=new kt(Ax,e.shape,t.shape);return this.compileAndRun(n,[e,t],e.dtype)}localResponseNormalization4D(e,t,s,n,i){const r=W().getBool("WEBGL_PACK_NORMALIZATION")?new xO(e.shape,t,s,n,i):new bO(e.shape,t,s,n,i);return this.compileAndRun(r,[e])}LRNGrad(e,t,s,n,i,r,o){const a=new wO(t.shape,n,i,r,o);return this.compileAndRun(a,[t,s,e])}tile(e,t){if(e.dtype==="string"){const n=this.readSync(e.dataId),i=n.map(o=>N.decodeString(o)),r=ge(e.shape,e.dtype,i);return jV(r,t)}const s=new VO(e.shape,t);return this.compileAndRun(s,[e])}pad(e,t,s){const n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NO(e.shape,t,s):new AO(e.shape,t,s);return this.compileAndRun(n,[e])}gather(e,t,s){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,s));if(n)return n;const i=new K2(e.shape,t.size,s);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,s){N.assert(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const n=t.reduce((c,p)=>c*p),i=U.getReshaped(e.shape,t,n),r=U.getPermuted(i.length,t.length),o=U.getReshapedPermuted(e.shape,t,n),a=U.getSliceBeginCoords(s,t.length),l=U.getSliceSize(o,s,t.length);return se(e.reshape(i),r).reshape(o).slice(a,l)}spaceToBatchND(e,t,s){N.assert(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const n=t.reduce((p,u)=>p*u),i=[[0,0]];i.push(...s);for(let p=1+t.length;p<e.shape.length;++p)i.push([0,0]);const r=e.pad(i),o=U.getReshaped(r.shape,t,n,!1),a=U.getPermuted(o.length,t.length,!1),l=U.getReshapedPermuted(r.shape,t,n,!1),c=se(r.reshape(o),a);return O(c,l)}reduce(e,t,s){const n=e.shape[0],i=e.shape[1],r=U.computeOptimalWindowSize(i),o=Math.ceil(i/r),a={windowSize:r,inSize:i,batchSize:n,outSize:o},l=new hg(a,t),c=this.compileAndRun(l,[e],s);return c.shape[1]===1?c:this.reduce(c,t,s)}argReduce(e,t,s=null){let n=e.shape[0],i=e.shape[1];s!=null&&(n=s.shape[0],i=s.shape[1]);const r=U.computeOptimalWindowSize(i),o={windowSize:r,inSize:i,batchSize:n,outSize:Math.ceil(i/r)},a=new PR(o,t,s==null),l=[e];s!=null&&l.push(s);const c=this.compileAndRun(a,l,"int32");return c.shape[1]===1?c:this.argReduce(e,t,c)}argReducePacked(e,t,s=null){const n=s!=null?s.shape:e.shape,i=n[n.length-1],r=U.computeOptimalWindowSize(i),o=new HR(n,r,t,s==null),a=s==null?[e]:[e,s],l=this.compileAndRun(o,a,"int32");return l.rank===e.rank?this.argReducePacked(e,t,l):l}sum(e,t){U.assertAxesAreInnerMostDims("sum",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=N.sizeFromShape(n),r=e.as2D(-1,i),o=Mp(e.dtype);return this.reduce(r,"sum",o).reshape(s)}prod(e,t){const s=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(s)return s;const[n,i]=U.computeOutAndReduceShapes(e.shape,t),r=N.sizeFromShape(i),o=e.as2D(-1,r),a=Mp(e.dtype);return this.reduce(o,"prod",a).reshape(n)}unsortedSegmentSum(e,t,s){let n=0;const i=U.getAxesPermutation([n],e.rank);let r=e;i!=null&&(r=se(e,i),n=U.getInnerMostAxes(1,e.rank)[0]);const o=L1.computeOutShape(r.shape,n,s),a=N.sizeFromShape([r.shape[n]]),l=r.as2D(-1,a),c=Mp(e.dtype);let p=this.segOpCompute(l,"unsortedSegmentSum",t,c,s).reshape(o);return i!=null&&(p=se(p,U.getUndoAxesPermutation(i))),p}segOpCompute(e,t,s,n,i){const r=e.shape[0],o=e.shape[1],a=L1.segOpComputeOptimalWindowSize(o,i),l={windowSize:a,inSize:o,batchSize:r,numSegments:i},c=new FO(l,t),p=this.compileAndRun(c,[e,s],n);return p.shape[1]===i?p:(s=$i(0,i).tile([o/a]),this.segOpCompute(p,t,s,n,i))}argMinMaxReduce(e,t,s){const n=[t];if(U.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),n,e.rank),!W().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,r]=U.computeOutAndReduceShapes(e.shape,n),o=N.sizeFromShape(r),a=e.as2D(-1,o);return this.argReduce(a,s).reshape(i)}return this.argReducePacked(e,s)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,s,n){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let r=e;for(let o=0;o<=Math.ceil(Math.log2(i))-1;o++){const a=new _x(e.shape,!1,n),l=a.getCustomSetupFunc(o),c=r;r=this.compileAndRun(a,[r],r.dtype,l),c.dispose()}if(s){const o=new _x(e.shape,s,n),a=r;r=this.compileAndRun(o,[r]),a.dispose()}return r}equal(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,m2,"bool");const s=new kt(QR,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}notEqual(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,f2,"bool");const s=new kt(e2,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}less(e,t){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(s)return s;if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,g2,"bool");const n=new kt(t2,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}lessEqual(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,y2,"bool");const s=new kt(s2,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greater(e,t){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(s)return s;if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,b2,"bool");const n=new kt(n2,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greaterEqual(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,w2,"bool");const s=new kt(i2,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}logicalNot(e){const t=new $e(e.shape,g1);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,x2,"bool");const s=new kt(r2,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}logicalOr(e,t){if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,L2,"bool");const s=new kt(o2,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}select(e,t,s){const n=new MO(e.rank,t.shape,t.rank);return this.compileAndRun(n,[e,t,s],Ft(t.dtype,s.dtype))}where(e){U.warn("tf.where() in webgl locks the UI thread. 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this.compileAndRun(t,[e])}sqrt(e){const t=new $e(e.shape,t1);return this.compileAndRun(t,[e])}rsqrt(e){if(this.shouldExecuteOnCPU([e])){const s=FR(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,s)}const t=new $e(e.shape,s1);return this.compileAndRun(t,[e])}reciprocal(e){const t=new $e(e.shape,f1);return this.compileAndRun(t,[e])}relu(e){let t;return W().getBool("WEBGL_PACK")?t=new lc(e.shape,Zx):t=new $e(e.shape,Vx),this.compileAndRun(t,[e])}relu6(e){let t;return W().getBool("WEBGL_PACK")?t=new lc(e.shape,Qx):t=new $e(e.shape,Gx),this.compileAndRun(t,[e])}prelu(e,t){const s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(Cx,e.shape,t.shape):new kt(Nx,e.shape,t.shape);return this.compileAndRun(s,[e,t])}elu(e){if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,eL,e.dtype);const t=new $e(e.shape,qx);return this.compileAndRun(t,[e])}eluDer(e,t){const s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(d2,e.shape,t.shape):new 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this.compileAndRun(t,[e])}asin(e){const t=new $e(e.shape,r1);return this.compileAndRun(t,[e])}acos(e){const t=new $e(e.shape,o1);return this.compileAndRun(t,[e])}atan(e){const t=new $e(e.shape,a1);return this.compileAndRun(t,[e])}sinh(e){const t=new $e(e.shape,l1);return this.compileAndRun(t,[e])}cosh(e){const t=new $e(e.shape,c1);return this.compileAndRun(t,[e])}tanh(e){const t=new $e(e.shape,p1);return this.compileAndRun(t,[e])}asinh(e){const t=new $e(e.shape,u1);return this.compileAndRun(t,[e])}acosh(e){const t=new $e(e.shape,h1);return this.compileAndRun(t,[e])}atanh(e){const t=new $e(e.shape,d1);return this.compileAndRun(t,[e])}erf(e){const t=new $e(e.shape,m1);return this.compileAndRun(t,[e])}step(e,t){const s=new $e(e.shape,HO(t));return this.compileAndRun(s,[e])}conv2dByMatMul(e,t,s,n,i,r){const o=e.shape,a=this.texData.get(e.dataId),l=s.inChannels,c=o[0]*o[1]*o[2],p=s.outChannels,u=s.dataFormat==="channelsLast",h=!1,d=!1,m=(c===1||p===1)&&l>S1,f=o[2]%2!==0&&!!a.isPacked;if(m||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!f){const _=u?o[0]*o[1]*o[2]:o[0]*o[2]*o[3],E=O(e,[1,_,s.inChannels]),F=O(t,[1,s.inChannels,s.outChannels]),D=this.fusedBatchMatMul({a:E,b:F,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r});return O(D,s.outShape)}const g=u?o[0]*o[1]*(o[2]+1):o[0]*o[2]*(o[3]+1),y={dataId:e.dataId,shape:[1,g,s.inChannels],dtype:e.dtype},w=a.shape;a.shape=a.shape.slice(),a.shape[a.shape.length-2]++,N.assert(sc(a.shape,y.shape),()=>`packed reshape ${a.shape} to ${y.shape} isn't free`);const x=O(t,[1,s.inChannels,s.outChannels]),T=this.fusedBatchMatMul({a:y,b:x,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r}),A=this.texData.get(T.dataId);return N.assert(A.isPacked,()=>"batchMatMul result is 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this.conv2dWithIm2Row(e,t,s,n,i,r);const o=n!=null,a=r!=null,l=i?fg(i,!1):null,c=new Rx(s,o,l,a),p=[e,t];return n&&p.push(n),r&&p.push(r),this.compileAndRun(c,p)}conv2d(e,t,s){if(s.filterHeight===1&&s.filterWidth===1&&s.dilationHeight===1&&s.dilationWidth===1&&s.strideHeight===1&&s.strideWidth===1&&(s.padInfo.type==="SAME"||s.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,s);if(W().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,s);const n=new Rx(s);return this.compileAndRun(n,[e,t])}conv2dDerInput(e,t,s){const n=new E2(s);return this.compileAndRun(n,[e,t])}conv2dDerFilter(e,t,s){const n=new O2(s);return this.compileAndRun(n,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){const o=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&s.strideWidth<=2&&s.outChannels/s.inChannels===1,a=i?fg(i,o):null,l=[e,t],c=n!=null,p=r!=null;c&&l.push(n),p&&l.push(r);let u;return o?(u=new 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a}avgPool3d(e,t){const s=new ug(t,"avg",!1);return this.compileAndRun(s,[e],"float32")}avgPool3dBackprop(e,t,s){const n=new KR(s);return this.compileAndRun(n,[e],t.dtype)}maxPool3d(e,t){const s=new ug(t,"max",!1);return this.compileAndRun(s,[e],"float32")}maxPool3dBackprop(e,t,s,n){const i=!0,r=new ug(n,"max",i),o=this.compileAndRun(r,[t]),a=new SO(n),l=this.compileAndRun(a,[e,o],t.dtype);return o.dispose(),l}resizeBilinear(e,t,s,n){const i=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new OO(e.shape,t,s,n):new RO(e.shape,t,s,n);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,s){const n=new CO(e,t,s);return this.compileAndRun(n,[e])}resizeNearestNeighbor(e,t,s,n){const i=new _O(e.shape,t,s,n);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,s){const n=new EO(e,t,s);return this.compileAndRun(n,[e])}multinomial(e,t,s,n){const i=t?e:ts(e),r=i.shape[0],o=i.shape[1],a=new IO(r,o,s),l=a.getCustomSetupFunc(n);return this.compileAndRun(a,[i],"int32",l)}oneHot(e,t,s,n){const i=new vO(e.size,t,s,n);return this.compileAndRun(i,[e])}diag(e){const t=new j2(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,s,n,i,r){const o=new U2(e.shape,t.shape,n,i,r);return this.compileAndRun(o,[e,t,s],"float32")}depthToSpace(e,t,s){N.assert(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const n=e.shape[0],i=s==="NHWC"?e.shape[1]:e.shape[2],r=s==="NHWC"?e.shape[2]:e.shape[3],o=s==="NHWC"?e.shape[3]:e.shape[1],a=i*t,l=r*t,c=o/(t*t),p=s==="NHWC"?[n,a,l,c]:[n,c,a,l],u=new B2(p,t,s);return this.compileAndRun(u,[e])}split(e,t,s){return BV(e,t,s)}scatterND(e,t,s){const{sliceRank:n,numUpdates:i,sliceSize:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=[a/r,r],c=e.reshape([i,n]),p=t.reshape([i,r]);if(a===0)return U.reshapeTensor(ze([]),s);const u=j(0),h=new Px(i,n,c.rank,p.rank,o,l),d=this.compileAndRun(h,[p,c,u]);return d.reshape(s)}sparseToDense(e,t,s,n){const{sliceRank:i,numUpdates:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=!1,c=new Px(r,i,e.rank,t.rank,o,[a,1],l),p=this.compileAndRun(c,[t,e,n]);return p.reshape(s)}fft(e){const t=!1;return this.fftImpl(e,t)}ifft(e){const t=!0;return this.fftImpl(e,t)}fftImpl(e,t){const s=this.texData.get(e.dataId),n=new Dx(kx.REAL,e.shape,t),i=new Dx(kx.IMAG,e.shape,t),r=[this.makeComplexComponentTensorInfo(e,s.complexTensors.real),this.makeComplexComponentTensorInfo(e,s.complexTensors.imag)],o=this.compileAndRun(n,r),a=this.compileAndRun(i,r),l=this.complex(o,a).as2D(e.shape[0],e.shape[1]);return o.dispose(),a.dispose(),l}gatherND(e,t){const s=t.shape,n=s[s.length-1],[i,r,o,a]=U.prepareAndValidate(e,t),l=t.reshape([r,n]),c=e.reshape([e.size/o,o]),p=new X2(n,a,[r,o]),u=this.compileAndRun(p,[c,l]);return u.reshape(i)}fill(e,t,s){if(s=s||N.inferDtype(t),s==="string"){const n=N.getArrayFromDType(s,N.sizeFromShape(e));return n.fill(t),Us().makeTensor(n,e,s,this)}else{const n=new Y2(e,t),i=n.getCustomSetupFunc(t);return this.compileAndRun(n,[],s,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,s){return U.linspaceImpl(e,t,s)}makeTensorInfo(e,t,s){const n=this.write(s,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,s){const{dataId:n}=this.makeTensorInfo(e,t,s);return Us().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){const t=new x1(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new TO(e.shape),s=!0;return this.runWebGLProgram(t,[e],e.dtype,null,s)}packedReshape(e,t){const s=[Lo(e.shape),...So(e.shape)],n={dtype:e.dtype,shape:s,dataId:e.dataId},i=[Lo(t),...So(t)],r=new dg(i,s),o=!0,a=this.runWebGLProgram(r,[n],e.dtype,null,o);return{dataId:a.dataId,shape:t,dtype:a.dtype}}decode(e){const t=this.texData.get(e),{isPacked:s,shape:n,dtype:i}=t,r=sg(n);let o;s?o=new P2(r):o=new z2(r);const a=!0,l=this.runWebGLProgram(o,[{shape:r,dtype:i,dataId:e}],i,null,a);return{dtype:i,shape:n,dataId:l.dataId}}runWebGLProgram(e,t,s,n,i=!1){const r=this.makeTensorInfo(e.outputShape,s),o=this.texData.get(r.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===wo.DENSE){const m=xo(e.outputShape);o.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),N.sizeFromShape(r.shape)===0)return o.values=N.getTypedArrayFromDType(r.dtype,0),r;const a=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&N.sizeFromShape(m.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!==!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),a.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!sc(f.shape,m.shape)){const g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),a.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(r.dataId);const c={shape:r.shape,texData:o,isUniform:!1},p=gO(e,l,c),u=this.getAndSaveBinary(p,()=>dO(this.gpgpu,e,l,c)),h=this.activeTimers!=null;let d;if(h&&(d=this.startTimer()),fO(this.gpgpu,u,l,c,n),a.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&i===!1){const m=this.unpackTensor(r);return this.disposeIntermediateTensorInfo(r),m}return r}compileAndRun(e,t,s,n,i=!1){s=s||t[0].dtype;const r=this.runWebGLProgram(e,t,s,n,i);return Us().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!W().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=C(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=W().getBool("DEBUG");W().set("DEBUG",!1);const t=this.abs(j(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?qV:HV}uploadToGPU(e){const t=this.texData.get(e),{shape:s,dtype:n,values:i,texture:r,usage:o,isPacked:a}=t;if(r!=null)return;const l=this.activeTimers!=null;let c;l&&(c=N.now());let p=t.texShape;if(p==null&&(p=wR(s,a),t.texShape=p),i!=null){const u=sg(s);let h,d=p[1],m=p[0];const f=i instanceof Uint8Array;a?([d,m]=wi(p[0],p[1]),h=new H2(u,[m,d],f)):h=new q2(u,[m,d],f);const g=this.makeTensorInfo([m,d],n);f?this.texData.get(g.dataId).usage=is.PIXELS:this.texData.get(g.dataId).usage=is.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),d,m,i);const y=!0,w=this.runWebGLProgram(h,[g],n,null,y),x=this.texData.get(w.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(w.dataId),t.values=null,l&&(this.uploadWaitMs+=N.now()-c)}else{const u=this.acquireTexture(p,o,n,a);t.texture=u}}convertAndCacheOnCPU(e,t){const s=this.texData.get(e),{dtype:n}=s;return this.releaseGPUData(e),t!=null&&(s.values=ZV(t,n)),s.values}acquireTexture(e,t,s,n){if(this.numBytesInGPU+=this.computeBytes(e,s),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*N.bytesPerElement(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(s){if(W().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function ZV(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const s=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<s.length;++n)s[n]=Math.round(e[n]);return s}else throw new Error(`Unknown dtype ${t}`)}const sL="2.6.0";function I1(){W().set("WEBGL_FORCE_F16_TEXTURES",!0)}rl.isBrowser()&&Vp("webgl",()=>new tL,2);const f5e={forceHalfFloat:I1};const gg="if (isnan(x)) return x;",v1=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,T1=`
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;function Io(e){return({inputs:t,backend:s})=>{const{x:n}=t,i=s,r=new $e(n.shape,e);return i.runWebGLProgram(r,[n],n.dtype)}}function cc(e,t,s,n){return({inputs:i,backend:r})=>{const{a:o,b:a}=i,l=r,c=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Li(t,o.shape,a.shape,!!s):new kt(e,o.shape,a.shape),p=n||o.dtype,u=l.runWebGLProgram(c,[o,a],p);return u}}const QV=v1+`
+  return atan(a, b);
+`,eG=`
+  vec4 result = atan(a, b);
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+T1+`
+  return result;
+`,tG=cc(QV,eG),A1={kernelName:ko,backendName:"webgl",kernelFunc:tG};function zu(e){const{inputs:t,backend:s}=e,{x:n}=t;return s.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}const N1={kernelName:Ci,backendName:"webgl",kernelFunc:zu};function sG(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;xi(i,"avgPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))return zu({inputs:{x:i},backend:s});const u=new Zi(p,"avg",!1);return s.runWebGLProgram(u,[i],"float32")}const C1={kernelName:Ti,backendName:"webgl",kernelFunc:sG};function nG(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r}=t,o=r;xi([i,r],"avgPoolBackprop");const{filterSize:a,strides:l,pad:c}=n,p=U.computePool2DInfo(o.shape,a,l,1,c),u=new YR(p);return s.runWebGLProgram(u,[i],o.dtype)}const R1={kernelName:Do,backendName:"webgl",kernelFunc:nG};class O1{constructor(e,t,s,n,i,r){this.outputShape=[],this.variableNames=["x","mean","variance"],U.assertAndGetBroadcastShape(e,t),U.assertAndGetBroadcastShape(e,s);let o="0.0";n!=null&&(U.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let a="1.0";i!=null&&(U.assertAndGetBroadcastShape(e,i),this.variableNames.push("scale"),a="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+      void main() {
+        float x = getXAtOutCoords();
+        float mean = getMeanAtOutCoords();
+        float variance = getVarianceAtOutCoords();
+        float offset = ${o};
+        float scale = ${a};
+        float inv = scale * inversesqrt(variance + float(${r}));
+        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
+      }
+    `}}class E1{constructor(e,t,s,n,i,r){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],U.assertAndGetBroadcastShape(e,t),U.assertAndGetBroadcastShape(e,s);let o="vec4(0.0)";n!=null&&(U.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let a="vec4(1.0)";i!=null&&(U.assertAndGetBroadcastShape(e,i),this.variableNames.push("scale"),a="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+      void main() {
+        vec4 offset = ${o};
+        vec4 scale = ${a};
+
+        vec4 x = getXAtOutCoords();
+        vec4 mean = getMeanAtOutCoords();
+        vec4 variance = getVarianceAtOutCoords();
+
+        vec4 inv = scale * inversesqrt(variance + vec4(${r}));
+
+        setOutput((x - mean) * inv + offset);
+      }
+    `}}const iG=({inputs:e,backend:t,attrs:s})=>{const{x:n,mean:i,variance:r,offset:o,scale:a}=e;N.assert(i.shape.length===r.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),N.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),N.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=s;l==null&&(l=.001);const c=[n,i,r];let p=null;o!=null&&(p=o.shape,c.push(o));let u=null;a!=null&&(u=a.shape,c.push(a));const h=W().getBool("WEBGL_PACK_NORMALIZATION")?new E1(n.shape,i.shape,r.shape,p,u,l):new O1(n.shape,i.shape,r.shape,p,u,l),d=t.runWebGLProgram(h,c,c[0].dtype);return d},_1={kernelName:Ni,backendName:"webgl",kernelFunc:iG};const rG=gg+`
+  return cos(x);
+`,oG=Io(rG),k1={kernelName:Qn,backendName:"webgl",kernelFunc:oG};const aG=`
+if (a == b) {
+  return 1.0;
+};
+return a / b;`,lG=`
+  // vec4 one = vec4(equal(a, b));
+  // return one + (vec4(1.0) - one) * a / b;
+  vec4 result = a / b;
+  if(a.x == b.x) {
+    result.x = 1.;
+  }
+  if(a.y == b.y) {
+    result.y = 1.;
+  }
+  if(a.z == b.z) {
+    result.z = 1.;
+  }
+  if(a.w == b.w) {
+    result.w = 1.;
+  }
+
+  return result;
+`,cG=cc(aG,lG,!0),D1={kernelName:ei,backendName:"webgl",kernelFunc:cG};class F1{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+
+          int coordX = ${t} - x;
+          float outputValue;
+          if(coordX >= 0 && coordX < ${t}) {
+            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
+          } else {
+            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}}const M1={kernelName:Uo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:s}=e,n=t,i=new F1(s.shape),r=n.runWebGLProgram(i,[s],s.dtype);return r}};class U1{constructor(e){this.variableNames=["A"];const t=ct(),[s,n]=e;this.outputShape=e,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${s}.0);
+
+        vec4 values = ${t.texture2D}(A, uv);
+        float value;
+        if (depth == 0) {
+          value = values.r;
+        } else if (depth == 1) {
+          value = values.g;
+        } else if (depth == 2) {
+          value = values.b;
+        } else if (depth == 3) {
+          value = values.a;
+        }
+
+        setOutput(floor(value * 255.0 + 0.5));
+      }
+    `}}class $1{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=ct(),[s,n]=e;this.outputShape=e,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+
+        vec4 result = vec4(0.);
+
+        for(int row=0; row<=1; row++) {
+          for(int col=0; col<=1; col++) {
+            texC = coords[1] + row;
+            depth = coords[2] + col;
+
+            vec2 uv = (vec2(texC, texR) + halfCR) /
+                       vec2(${n}.0, ${s}.0);
+            vec4 values = ${t.texture2D}(A, uv);
+            float value;
+            if (depth == 0) {
+              value = values.r;
+            } else if (depth == 1) {
+              value = values.g;
+            } else if (depth == 2) {
+              value = values.b;
+            } else if (depth == 3) {
+              value = values.a;
+            }
+
+            result[row * 2 + col] = floor(value * 255.0 + 0.5);
+          }
+        }
+
+        ${t.output} = result;
+      }
+    `}}const W1={kernelName:Xa,backendName:"webgl",kernelFunc:pG};let pc;function pG(e){const{inputs:t,backend:s,attrs:n}=e;let{pixels:i}=t;const{numChannels:r}=n,o=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[l,c]=o?[i.videoWidth,i.videoHeight]:[i.width,i.height],p=[c,l],u=[c,l,r];(a||o)&&(pc==null&&(pc=document.createElement("canvas").getContext("2d")),pc.canvas.width=l,pc.canvas.height=c,pc.drawImage(i,0,0,l,c),i=pc.canvas);const h=s.makeTensorInfo(p,"int32");s.texData.get(h.dataId).usage=is.PIXELS,s.gpgpu.uploadPixelDataToTexture(s.getTexture(h.dataId),i);const d=W().getBool("WEBGL_PACK")?new $1(u):new U1(u),m=s.runWebGLProgram(d,[h],"int32");return s.disposeData(h.dataId),m}function uG(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const s=t.length?t[t.length-1].outSize:e[1],n=U.computeOptimalWindowSize(s);t.push({inSize:s,windowSize:n,outSize:Math.ceil(s/n)})}return t}function z1(e,t,s,n){const i=uG(e.shape);let r=e;for(let o=0;o<i.length;o++){const{inSize:a,windowSize:l,outSize:c}=i[o],p=new hg({windowSize:l,inSize:a,batchSize:e.shape[0],outSize:c},s),u=r;r=n.runWebGLProgram(p,[r],t),u.dataId!==e.dataId&&n.disposeData(u.dataId)}return r}function P1(e,t,s){const n=[Lo(e.shape),...So(e.shape)],i={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Lo(t),...So(t)],o=new dg(r,n),a=!0,l=s.runWebGLProgram(o,[i],e.dtype,null,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function yg(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t,{shape:r}=n,o=s,a=N.sizeFromShape(i.shape),l=N.inferFromImplicitShape(r,a),c=N.sizeFromShape(l);N.assert(a===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${i.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);const p=o.texData.get(i.dataId);return p.isPacked&&!sc(i.shape,l)&&!(p.texture!==null&&sc(p.shape,l))?P1(i,l,o):(o.incRef(i.dataId),{dataId:i.dataId,shape:l,dtype:i.dtype})}const B1={kernelName:Ei,backendName:"webgl",kernelFunc:yg};function j1(e,t,s,n){const i=N.sizeFromShape(t),r=N.sizeFromShape(e.shape),o=r/i,a=yg({inputs:{x:e},attrs:{shape:[o,i]},backend:n}),l=z1(a,e.dtype,"max",n),c=yg({inputs:{x:l},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(l),c}class V1{constructor(e,t){this.variableNames=["A"];const s=new Array(e.length);for(let r=0;r<s.length;r++)s[r]=e[t[r]];this.outputShape=s,this.rank=s.length;const n=Re(this.rank),i=hG(t);this.userCode=`
+    void main() {
+      ${n} resRC = getOutputCoords();
+      setOutput(getA(${i}));
+    }
+    `}}function hG(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const s=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let i=0;i<e.length;i++)n[e[i]]=s[i];return n.join()}class G1{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const s=new Array(e.length);for(let c=0;c<s.length;c++)s[c]=e[t[c]];if(this.outputShape=s,this.rank=s.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const n=Re(this.rank),i=Ix("rc",this.rank),r=new Array(this.rank);for(let c=0;c<t.length;c++)r[t[c]]=i[c];const o=`vec2(${r.slice(-2).join()})`,a=`++${i[this.rank-1]} < ${s[this.rank-1]}`,l=`getChannel(getA(${r.join()}), ${o})`;this.userCode=`
+    void main() {
+      ${n} rc = getOutputCoords();
+      vec4 result = vec4(0.);
+      result[0] = ${l};
+      if(${a}) {
+        result[1] = ${l};
+      }
+      --${i[this.rank-1]};
+      if(++${i[this.rank-2]} < ${s[this.rank-2]}) {
+        result[2] = ${l};
+        if(${a}) {
+          result[3] = ${l};
+        }
+      }
+      setOutput(result);
+    }
+    `}}function bg(e,t,s){const n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G1(e.shape,t):new V1(e.shape,t);return s.runWebGLProgram(n,[e],e.dtype)}const q1={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{reductionIndices:i,keepDims:r}=t,o=s,a=n.shape.length,l=N.parseAxisParam(i,n.shape);let c=l;const p=U.getAxesPermutation(c,a),u=p!=null,h=o.shouldExecuteOnCPU([n]);let d=n;if(u){if(h){const w=o.texData.get(d.dataId),x=w.values,T=new Array(a);for(let E=0;E<T.length;E++)T[E]=n.shape[p[E]];const A=ig(x,n.shape,n.dtype,p,T);d=o.makeTensorInfo(T,n.dtype);const _=o.texData.get(d.dataId);_.values=A}else d=bg(n,p,o);c=U.getInnerMostAxes(c.length,a)}U.assertAxesAreInnerMostDims("max",c,a);const[m,f]=U.computeOutAndReduceShapes(d.shape,c);let g=m;r&&(g=U.expandShapeToKeepDim(m,l));let y;if(h){const w=o.texData.get(d.dataId),x=w.values,T=kR(x,N.sizeFromShape(f),g,n.dtype);y=o.makeTensorInfo(g,n.dtype);const A=o.texData.get(y.dataId);A.values=T}else y=j1(d,f,g,o);return u&&o.disposeIntermediateTensorInfo(d),y}};function dG(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;xi(i,"maxPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))return zu({inputs:{x:i},backend:s});const u=new Zi(p,"max",!1);return s.runWebGLProgram(u,[i],i.dtype)}const H1={kernelName:Oi,backendName:"webgl",kernelFunc:dG};function mG(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r,output:o}=t,a=r;xi([r,o],"maxPoolBackprop");const{filterSize:l,strides:c,pad:p,dimRoundingMode:u}=n,h=U.computePool2DInfo(a.shape,l,c,1,p,u),d=!0,m=new Zi(h,"max",d),f=s.runWebGLProgram(m,[a],a.dtype),g=new LO(h),y=s.runWebGLProgram(g,[i,f],a.dtype);return s.disposeIntermediateTensorInfo(f),y}const Y1={kernelName:$o,backendName:"webgl",kernelFunc:mG};function K1(e,t,s,n){let i=new Zi(s,"max",!1);const r=n.runWebGLProgram(i,[e],"float32");i=new Zi(s,"max",!0,!0,t);const o=n.runWebGLProgram(i,[e],"float32");return[r,o]}const X1={kernelName:Wo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{filterSize:i,strides:r,pad:o,includeBatchInIndex:a}=t,l=s;N.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);const c=[1,1];N.assert(U.eitherStridesOrDilationsAreOne(r,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${r} and dilations '${c}'`);const p=U.computePool2DInfo(n.shape,i,r,c,o),[u,h]=K1(n,a,p,l);return[u,h]}};const J1={kernelName:cp,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a}=s,l=t,c=l.readSync(n.dataId),p=l.readSync(i.dataId),u=r,h=o,d=a;return vt.nonMaxSuppressionV3Impl(c,p,u,h,d)}};const fG=vt.nonMaxSuppressionV4Impl,Z1={kernelName:zo,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a,padToMaxOutputSize:l}=s,c=t,p=c.readSync(n.dataId),u=c.readSync(i.dataId),{selectedIndices:h,validOutputs:d}=fG(p,u,r,o,a,l);return[h,d]}};const gG=vt.nonMaxSuppressionV5Impl,Q1={kernelName:Po,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a,softNmsSigma:l}=s,c=t,p=c.readSync(n.dataId),u=c.readSync(i.dataId),h=r,d=o,m=a,f=l,{selectedIndices:g,selectedScores:y}=gG(p,u,h,d,m,f);return[g,y]}};class eE{constructor(e,t,s,n){this.variableNames=["Image"],this.outputShape=[];const i=e[1],r=e[2],o=Math.sin(t).toFixed(3),a=Math.cos(t).toFixed(3);this.outputShape=e;const[l,c]=U.getImageCenter(n,i,r),p=l.toFixed(3),u=c.toFixed(3);let h="";typeof s=="number"?h=`float outputValue = ${s.toFixed(2)};`:h=`
+        vec3 fill = vec3(${s.join(",")});
+        float outputValue = fill[coords[3]];`,this.userCode=`
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+          int y = coords[1];
+          float coordXFloat = (float(x) - ${p}) * ${a} - (float(y) - ${u}) * ${o};
+          float coordYFloat = (float(x) - ${p}) * ${o} + (float(y) - ${u}) * ${a};
+          int coordX = int(round(coordXFloat + ${p}));
+          int coordY = int(round(coordYFloat + ${u}));
+          ${h}
+          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${i}) {
+            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}}const tE={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,{radians:i,fillValue:r,center:o}=t,a=s,l=new eE(n.shape,i,r,o),c=a.runWebGLProgram(l,[n],n.dtype);return c}};const yG=gg+`
+  return sin(x);
+`,bG=Io(yG),sE={kernelName:ti,backendName:"webgl",kernelFunc:bG};const wG="return x * x;",xG=Io(wG),nE={kernelName:Go,backendName:"webgl",kernelFunc:xG};const iE="return (a - b) * (a - b);",LG=cc(iE,iE),rE={kernelName:si,backendName:"webgl",kernelFunc:LG};const SG="return tan(x);",IG=Io(SG),oE={kernelName:ni,backendName:"webgl",kernelFunc:IG};const aE={kernelName:_i,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{perm:i}=t,r=s,o=n.shape.length,a=new Array(o);for(let c=0;c<a.length;c++)a[c]=n.shape[i[c]];let l;if(r.shouldExecuteOnCPU([n])){const c=r.texData.get(n.dataId),p=c.values,u=ig(p,n.shape,n.dtype,i,a);l=r.makeTensorInfo(a,n.dtype);const h=r.texData.get(l.dataId);h.values=u}else l=bg(n,i,r);return l}};function vG(e){const{inputs:t,attrs:s,backend:n}=e,{axis:i}=s,{x:r}=t;xi(r,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const 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_e(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 _e(Math.floor(this.x),Math.floor(this.y))}}class it{constructor(e,t=!0){const s=e||{},n=[s.left,s.top,s.right,s.bottom].every(Yn),i=[s.x,s.y,s.width,s.height].every(Yn);if(!i&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(s)}`);const[r,o,a,l]=i?[s.x,s.y,s.width,s.height]:[s.left,s.top,s.right-s.left,s.bottom-s.top];it.assertIsValidBox({x:r,y:o,width:a,height:l},"Box.constructor",t),this._x=r,this._y=o,this._width=a,this._height=l}static isRect(e){return!!e&&[e.x,e.y,e.width,e.height].every(Yn)}static assertIsValidBox(e,t,s=!1){if(!it.isRect(e))throw new Error(`${t} - invalid box: ${JSON.stringify(e)}, expected object with properties x, y, width, height`);if(!s&&(e.width<0||e.height<0))throw new Error(`${t} - width (${e.width}) and height (${e.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 _e(this.left,this.top)}get topRight(){return new _e(this.right,this.top)}get bottomLeft(){return new _e(this.left,this.bottom)}get bottomRight(){return new _e(this.right,this.bottom)}round(){const[e,t,s,n]=[this.x,this.y,this.width,this.height].map(i=>Math.round(i));return new it({x:e,y:t,width:s,height:n})}floor(){const[e,t,s,n]=[this.x,this.y,this.width,this.height].map(i=>Math.floor(i));return new it({x:e,y:t,width:s,height:n})}toSquare(){let{x:e,y:t,width:s,height:n}=this;const i=Math.abs(s-n);return s<n&&(e-=i/2,s+=i),n<s&&(t-=i/2,n+=i),new it({x:e,y:t,width:s,height:n})}rescale(e){const t=xg(e)?e.width:e,s=xg(e)?e.height:e;return new it({x:this.x*t,y:this.y*s,width:this.width*t,height:this.height*s})}pad(e,t){let[s,n,i,r]=[this.x-e/2,this.y-t/2,this.width+e,this.height+t];return new it({x:s,y:n,width:i,height:r})}clipAtImageBorders(e,t){const{x:s,y:n,right:i,bottom:r}=this,o=Math.max(s,0),a=Math.max(n,0),l=i-o,c=r-a,p=Math.min(l,e-o),u=Math.min(c,t-a);return new it({x:o,y:a,width:p,height:u}).floor()}shift(e,t){const{width:s,height:n}=this,i=this.x+e,r=this.y+t;return new it({x:i,y:r,width:s,height:n})}padAtBorders(e,t){const s=this.width+1,n=this.height+1;let i=1,r=1,o=s,a=n,l=this.left,c=this.top,p=this.right,u=this.bottom;return p>t&&(o=-p+t+s,p=t),u>e&&(a=-u+e+n,u=e),l<1&&(a=2-l,l=1),c<1&&(a=2-c,c=1),{dy:r,edy:a,dx:i,edx:o,y:c,ey:u,x:l,ex:p,w:s,h:n}}calibrate(e){return new it({left:this.left+e.left*this.width,top:this.top+e.top*this.height,right:this.right+e.right*this.width,bottom:this.bottom+e.bottom*this.height}).toSquare().round()}}class Ga extends it{constructor(e,t,s,n,i=!1){super({left:e,top:t,right:s,bottom:n},i)}}class Oo{constructor(e,t,s,n,i){this._imageDims=new Ts(i.width,i.height),this._score=e,this._classScore=t,this._className=s,this._box=new it(n).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 it(this._box).rescale(this.imageDims.reverse())}forSize(e,t){return new Oo(this.score,this.classScore,this.className,this.relativeBox,{width:e,height:t})}}class ht extends Oo{constructor(e,t,s){super(e,e,"",t,s)}forSize(e,t){const{score:s,relativeBox:n,imageDims:i}=super.forSize(e,t);return new ht(s,n,i)}}function oh(e,t,s=!0){const n=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),i=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),r=n*i;return s?r/(e.area+t.area-r):r/Math.min(e.area,t.area)}function rh(e){const t=e.map(a=>a.x),s=e.map(a=>a.y),n=t.reduce((a,l)=>l<a?l:a,Infinity),i=s.reduce((a,l)=>l<a?l:a,Infinity),r=t.reduce((a,l)=>a<l?l:a,0),o=s.reduce((a,l)=>a<l?l:a,0);return new Ga(n,i,r,o)}function ih(e,t,s,n=!0){let i=t.map((o,a)=>({score:o,boxIndex:a})).sort((o,a)=>o.score-a.score).map(o=>o.boxIndex);const r=[];for(;i.length>0;){const o=i.pop();r.push(o);const a=i,l=[];for(let c=0;c<a.length;c++){const p=a[c],u=e[o],h=e[p];l.push(oh(u,h,n))}i=i.filter((c,p)=>l[p]<=s)}return r}function un(e,t){return C(()=>{const[s,n,i]=t,r=Wt([...e.shape.slice(0,3),1],s),o=Wt([...e.shape.slice(0,3),1],n),a=Wt([...e.shape.slice(0,3),1],i),l=be([r,o,a],3);return X(e,l)})}function nh(e,t=!1){return C(()=>{const[s,n]=e.shape.slice(1);if(s===n)return e;const i=Math.abs(s-n),r=Math.round(i*(t?.5:1)),o=s>n?2:1,a=h=>{const d=e.shape.slice();return d[o]=h,Wt(d,0)},l=a(r),c=i-l.shape[o],p=t&&c?a(c):null,u=[p,e,l].filter(h=>!!h).map(h=>G(h,"float32"));return be(u,o)})}function VL(e){const t=e.slice();for(let s=t.length-1;s>0;s--){const n=Math.floor(Math.random()*(s+1)),i=t[s];t[s]=t[n],t[n]=i}return t}function $a(e){return 1/(1+Math.exp(-e))}function GL(e){return Math.log(e/(1-e))}class ja extends it{constructor(e,t,s,n,i=!1){super({x:e,y:t,width:s,height:n},i)}}const CG=.5,RG=.43,OG=.45;class Hs{constructor(e,t,s=new _e(0,0)){const{width:n,height:i}=t;this._imgDims=new Ts(n,i),this._shift=s,this._positions=e.map(r=>r.mul(new _e(n,i)).add(s))}get shift(){return new _e(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 _e(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 _e(e,t))}shiftByPoint(e){return this.shiftBy(e.x,e.y)}align(e,t={}){if(e){const i=e instanceof ht?e.box.floor():new it(e);return this.shiftBy(i.x,i.y).align(null,t)}const{useDlibAlignment:s,minBoxPadding:n}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},t);return s?this.alignDlib():this.alignMinBbox(n)}alignDlib(){const e=this.getRefPointsForAlignment(),[t,s,n]=e,i=p=>n.sub(p).magnitude(),r=(i(t)+i(s))/2,o=Math.floor(r/OG),a=Ra(e),l=Math.floor(Math.max(0,a.x-CG*o)),c=Math.floor(Math.max(0,a.y-RG*o));return new ja(l,c,Math.min(o,this.imageWidth+l),Math.min(o,this.imageHeight+c))}alignMinBbox(e){const t=rh(this.positions);return t.pad(t.width*e,t.height*e)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class wS extends Hs{getRefPointsForAlignment(){const e=this.positions;return[e[0],e[1],Ra([e[3],e[4]])]}}class Va extends Hs{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(Ra)}}class Dc{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}${e?` (${Ca(this.distance)})`:""}`}}class kc extends it{constructor(e,t){super(e);this._label=t}static assertIsValidLabeledBox(e,t){if(it.assertIsValidBox(e,t),!Yn(e.label))throw new Error(`${t} - expected property label (${e.label}) to be a number`)}get label(){return this._label}}class hr{constructor(e,t){if(!(typeof e=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(t)||t.some(s=>!(s 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(s=>new Float32Array(s));return new hr(e.label,t)}}class bS extends kc{constructor(e,t,s,n){super(e,t);this._score=s,this._classScore=n}static assertIsValidPredictedBox(e,t){if(kc.assertIsValidLabeledBox(e,t),!uc(e.score)||!uc(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 Mn(e){return e.detection instanceof ht}function ar(e,t){const s={detection:t};return Object.assign({},e,s)}function oL(){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 Lg(e){let t="";if(!e)try{e=require("fs")}catch(n){t=n.toString()}const s=e?function(n){return new Promise((i,r)=>{e.readFile(n,function(o,a){return o?r(o):i(a)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)};return{readFile:s}}function aL(){const e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,s=function(){if(e)return new e;throw new 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pL(e){Jt=e}function uL(){if(lL())return pL(oL());if(cL.isNodejs())return pL(aL())}function _G(e){if(Jt||uL(),!Jt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:t=Jt.Canvas,Image:s=Jt.Image}=e;Jt.Canvas=t,Jt.Image=s,Jt.createCanvasElement=e.createCanvasElement||(()=>new t),Jt.createImageElement=e.createImageElement||(()=>new s),Jt.ImageData=e.ImageData||Jt.ImageData,Jt.Video=e.Video||Jt.Video,Jt.fetch=e.fetch||Jt.fetch,Jt.readFile=e.readFile||Jt.readFile}const Ve={getEnv:EG,setEnv:pL,initialize:uL,createBrowserEnv:oL,createFileSystem:Lg,createNodejsEnv:aL,monkeyPatch:_G,isBrowser:lL,isNodejs:cL.isNodejs};uL();function cr(e){return!Ve.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function us(e){const{Canvas:t,CanvasRenderingContext2D:s}=Ve.getEnv();if(e instanceof s)return e;const n=cr(e);if(!(n instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const i=n.getContext("2d");if(!i)throw new Error("resolveContext2d - canvas 2d context is null");return i}var Ii;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Ii||(Ii={}));class Sg{constructor(e={}){const{anchorPosition:t,backgroundColor:s,fontColor:n,fontSize:i,fontStyle:r,padding:o}=e;this.anchorPosition=t||Ii.TOP_LEFT,this.backgroundColor=s||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=i||14,this.fontStyle=r||"Georgia",this.padding=o||4}}class hc{constructor(e,t,s={}){this.text=typeof e=="string"?[e]:e instanceof hc?e.text:e,this.anchor=t,this.options=new Sg(s)}measureWidth(e){const{padding:t}=this.options;return this.text.map(s=>e.measureText(s).width).reduce((s,n)=>s<n?n:s,0)+2*t}measureHeight(){const{fontSize:e,padding:t}=this.options;return this.text.length*e+2*t}getUpperLeft(e,t){const{anchorPosition:s}=this.options,n=s===Ii.BOTTOM_RIGHT||s===Ii.TOP_RIGHT,i=s===Ii.BOTTOM_LEFT||s===Ii.BOTTOM_RIGHT,r=this.measureWidth(e),o=this.measureHeight(),a=n?this.anchor.x-r:this.anchor.x,l=i?this.anchor.y-o:this.anchor.y;if(t){const{width:c,height:p}=t,u=Math.max(Math.min(a,c-r),0),h=Math.max(Math.min(l,p-o),0);return{x:u,y:h}}return{x:a,y:l}}draw(e){const t=cr(e),s=us(t),{backgroundColor:n,fontColor:i,fontSize:r,fontStyle:o,padding:a}=this.options;s.font=`${r}px ${o}`;const l=this.measureWidth(s),c=this.measureHeight();s.fillStyle=n;const p=this.getUpperLeft(s,t);s.fillRect(p.x,p.y,l,c),s.fillStyle=i,this.text.forEach((u,h)=>{const d=a+p.x,m=a+p.y+(h+1)*r;s.fillText(u,d,m)})}}class pE{constructor(e={}){const{boxColor:t,lineWidth:s,label:n,drawLabelOptions:i}=e;this.boxColor=t||"rgba(0, 0, 255, 1)",this.lineWidth=s||2,this.label=n;const r={anchorPosition:Ii.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Sg(Object.assign({},r,i))}}class hL{constructor(e,t={}){this.box=new it(e),this.options=new pE(t)}draw(e){const t=us(e),{boxColor:s,lineWidth:n}=this.options,{x:i,y:r,width:o,height:a}=this.box;t.strokeStyle=s,t.lineWidth=n,t.strokeRect(i,r,o,a);const{label:l}=this.options;l&&new hc([l],{x:i-n/2,y:r},this.options.drawLabelOptions).draw(e)}}function kG(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof ht?n.score:Mn(n)?n.detection.score:void 0,r=n instanceof ht?n.box:Mn(n)?n.detection.box:new it(n),o=i?`${Ca(i)}`:void 0;new hL(r,{label:o}).draw(e)})}function Pa(e){const{Image:t,Video:s}=Ve.getEnv();return e instanceof t&&e.complete||e instanceof s&&e.readyState>=3}function Ih(e){return new Promise((t,s)=>{if(e instanceof Ve.getEnv().Canvas||Pa(e))return t(null);function n(r){if(!r.currentTarget)return;r.currentTarget.removeEventListener("load",n),r.currentTarget.removeEventListener("error",i),t(r)}function i(r){if(!r.currentTarget)return;r.currentTarget.removeEventListener("load",n),r.currentTarget.removeEventListener("error",i),s(r)}e.addEventListener("load",n),e.addEventListener("error",i)})}function Sh(e){return new Promise((t,s)=>{if(!(e instanceof Blob))return s("bufferToImage - expected buf to be of type: Blob");const n=new FileReader;n.onload=()=>{if(typeof n.result!="string")return s("bufferToImage - expected reader.result to be a string, in onload");const i=Ve.getEnv().createImageElement();i.onload=()=>t(i),i.onerror=s,i.src=n.result},n.onerror=s,n.readAsDataURL(e)})}function pr(e){const{Image:t,Video:s}=Ve.getEnv();return e instanceof t?new Ts(e.naturalWidth,e.naturalHeight):e instanceof s?new Ts(e.videoWidth,e.videoHeight):new Ts(e.width,e.height)}function Ro({width:e,height:t}){const{createCanvasElement:s}=Ve.getEnv(),n=s();return n.width=e,n.height=t,n}function Ba(e,t){const{ImageData:s}=Ve.getEnv();if(!(e instanceof s)&&!Pa(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:n,height:i}=t||pr(e),r=Ro({width:n,height:i});return e instanceof s?us(r).putImageData(e,0,0):us(r).drawImage(e,0,0,n,i),r}async function xh(e,t){const s=t||Ve.getEnv().createCanvasElement(),[n,i,r]=e.shape.slice(ln(e)?1:0),o=C(()=>e.as3D(n,i,r).toInt());return await Gr.toPixels(o,s),o.dispose(),s}function _c(e){const{Image:t,Canvas:s,Video:n}=Ve.getEnv();return e instanceof t||e instanceof s||e instanceof n}function wh(e,t,s=!1){const{Image:n,Canvas:i}=Ve.getEnv();if(!(e instanceof n||e instanceof i))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const r=pr(e),o=t/Math.max(r.height,r.width),a=o*r.width,l=o*r.height,c=Ro({width:t,height:t}),p=e instanceof i?e:Ba(e),u=Math.abs(a-l)/2,h=s&&a<l?u:0,d=s&&l<a?u:0;return us(c).drawImage(p,h,d,a,l),c}class vi{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((s,n)=>{if(tr(s)){this._imageTensors[n]=s,this._inputDimensions[n]=s.shape;return}if(ln(s)){const r=s.shape[0];if(r!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${r} passed, but not supported in input array`);this._imageTensors[n]=s,this._inputDimensions[n]=s.shape.slice(1);return}const i=s instanceof Ve.getEnv().Canvas?s:Ba(s);this._canvases[n]=i,this._inputDimensions[n]=[i.height,i.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 Si(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),s=this.getInputHeight(e);return rL({width:t,height:s},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,C(()=>{const s=Si(this.batchSize,0,1).map(i=>{const r=this.getInput(i);if(r instanceof me){let o=ln(r)?r:r.expandDims();return o=nh(o,t),(o.shape[1]!==e||o.shape[2]!==e)&&(o=en.resizeBilinear(o,[e,e])),o.as3D(e,e,3)}if(r instanceof Ve.getEnv().Canvas)return Gr.fromPixels(wh(r,e,t));throw new Error(`toBatchTensor - at batchIdx ${i}, expected input to be instanceof tf.Tensor or instanceof 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nt(e);if(o.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const a=o.getInput(0);n=a instanceof s?a:await xh(a)}const i=us(n),r=t.map(o=>o instanceof ht?o.forSize(n.width,n.height).box.floor():o).map(o=>o.clipAtImageBorders(n.width,n.height));return r.map(({x:o,y:a,width:l,height:c})=>{const p=Ro({width:l,height:c});return us(p).putImageData(i.getImageData(o,a,l,c),0,0),p})}async function Co(e,t){if(!tr(e)&&!ln(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ln(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return C(()=>{const[s,n,i]=e.shape.slice(ln(e)?1:0),r=t.map(a=>a instanceof ht?a.forSize(n,s).box:a).map(a=>a.clipAtImageBorders(n,s)),o=r.map(({x:a,y:l,width:c,height:p})=>to(e.as3D(s,n,i),[l,a,0],[p,c,i]));return o})}async function ur(e,t){const s=Ve.getEnv().fetch,n=await s(e,t);if(!(n.status<400))throw new Error(`failed to fetch: (${n.status}) ${n.statusText}, from url: ${n.url}`);return n}async function yS(e){const t=await ur(e),s=await t.blob();if(!s.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${s.type}, for url: ${t.url}`);return Sh(s)}async function Lh(e){return(await ur(e)).json()}async function gS(e){return new Float32Array(await(await ur(e)).arrayBuffer())}function Ig(e,t){const s=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:s};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${s}`};const n=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(n,"");const i=e.split("/").filter(a=>a),r=e.endsWith(".json")?i[i.length-1]:s;let o=n+(e.endsWith(".json")?i.slice(0,i.length-1):i).join("/");return o=e.startsWith("/")?`/${o}`:o,{modelBaseUri:o,manifestUri:o==="/"?`/${r}`:`${o}/${r}`}}async function bh(e,t){const{manifestUri:s,modelBaseUri:n}=Ig(e,t);let i=await Lh(s);return Rt.loadWeights(i,n)}function fS(e,t,s=!1){const{width:n,height:i}=s?pr(t):t;return e.width=n,e.height=i,{width:n,height:i}}class Zt{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:s}=this.traversePropertyPath(e);return t[s]}reassignParamFromPath(e,t){const{obj:s,objProp:n}=this.traversePropertyPath(e);s[n].dispose(),s[n]=t}getParamList(){return this._paramMappings.map(({paramPath:e})=>({path:e,tensor:this.getParamFromPath(e)}))}getTrainableParams(){return this.getParamList().filter(e=>e.tensor instanceof ci)}getFrozenParams(){return this.getParamList().filter(e=>!(e.tensor instanceof ci))}variable(){this.getFrozenParams().forEach(({path:e,tensor:t})=>{this.reassignParamFromPath(e,t.variable())})}freeze(){this.getTrainableParams().forEach(({path:e,tensor:t})=>{const s=ze(t.dataSync());t.dispose(),this.reassignParamFromPath(e,s)})}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 bh(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}=Ve.getEnv(),{manifestUri:s,modelBaseUri:n}=Ig(e,this.getDefaultModelName()),i=l=>Promise.all(l.map(c=>t(c).then(p=>p.buffer))),r=Rt.weightsLoaderFactory(i),o=JSON.parse((await t(s)).toString()),a=await r(o,n);this.loadFromWeightMap(a)}loadFromWeightMap(e){const{paramMappings:t,params:s}=this.extractParamsFromWeigthMap(e);this._paramMappings=t,this._params=s}extractWeights(e){const{paramMappings:t,params:s}=this.extractParams(e);this._paramMappings=t,this._params=s}traversePropertyPath(e){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const t=e.split("/").reduce((i,r)=>{if(!i.nextObj.hasOwnProperty(r))throw new Error(`traversePropertyPath - object does not have property ${r}, for path ${e}`);return{obj:i.nextObj,objProp:r,nextObj:i.nextObj[r]}},{nextObj:this.params}),{obj:s,objProp:n}=t;if(!s||!n||!(s[n]instanceof me))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:s,objProp:n}}}function _s(e,t,s){return C(()=>{let n=eo(e,t.depthwise_filter,t.pointwise_filter,s,"same");return n=$(n,t.bias),n})}function vg(e,t,s=!1){return C(()=>{const n=Fe(s?$(rt(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):_s(e,t.conv0,[2,2])),i=_s(n,t.conv1,[1,1]),r=Fe($(n,i)),o=_s(r,t.conv2,[1,1]);return Fe($(n,$(i,o)))})}function Pu(e,t,s=!1,n=!0){return C(()=>{const i=Fe(s?$(rt(e,t.conv0.filters,n?[2,2]:[1,1],"same"),t.conv0.bias):_s(e,t.conv0,n?[2,2]:[1,1])),r=_s(i,t.conv1,[1,1]),o=Fe($(i,r)),a=_s(o,t.conv2,[1,1]),l=Fe($(i,$(r,a))),c=_s(l,t.conv3,[1,1]);return Fe($(i,$(r,$(a,c))))})}function Oa(e,t,s="same",n=!1){return C(()=>{const i=$(rt(e,t.filters,[1,1],s),t.bias);return n?Fe(i):i})}function Is(e,t){Object.keys(e).forEach(s=>{t.some(n=>n.originalPath===s)||e[s].dispose()})}function dc(e,t){return function(s,n,i,r){const o=ss(e(s*n*i*i),[i,i,s,n]),a=Oe(e(n));return t.push({paramPath:`${r}/filters`},{paramPath:`${r}/bias`}),{filters:o,bias:a}}}function Tg(e,t){return function(s,n,i){const r=ls(e(s*n),[s,n]),o=Oe(e(n));return t.push({paramPath:`${i}/weights`},{paramPath:`${i}/bias`}),{weights:r,bias:o}}}class dL{constructor(e,t,s){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=s}}function mc(e,t){return function(s,n,i){const r=ss(e(3*3*s),[3,3,s,1]),o=ss(e(s*n),[1,1,s,n]),a=Oe(e(n));return t.push({paramPath:`${i}/depthwise_filter`},{paramPath:`${i}/pointwise_filter`},{paramPath:`${i}/bias`}),new dL(r,o,a)}}function fc(e){return function(t){const s=e(`${t}/depthwise_filter`,4),n=e(`${t}/pointwise_filter`,4),i=e(`${t}/bias`,1);return new dL(s,n,i)}}function Vs(e,t){return function(s,n,i){const r=e[s];if(!Na(r,n))throw new Error(`expected weightMap[${s}] to be a Tensor${n}D, instead have ${r}`);return t.push({originalPath:s,paramPath:i||s}),r}}function vs(e){let t=e;function s(i){const r=t.slice(0,i);return t=t.slice(i),r}function n(){return t}return{extractWeights:s,getRemainingWeights:n}}function Ag(e,t){const s=dc(e,t),n=mc(e,t);function i(o,a,l,c=!1){const p=c?s(o,a,3,`${l}/conv0`):n(o,a,`${l}/conv0`),u=n(a,a,`${l}/conv1`),h=n(a,a,`${l}/conv2`);return{conv0:p,conv1:u,conv2:h}}function r(o,a,l,c=!1){const{conv0:p,conv1:u,conv2:h}=i(o,a,l,c),d=n(a,a,`${l}/conv3`);return{conv0:p,conv1:u,conv2:h,conv3:d}}return{extractDenseBlock3Params:i,extractDenseBlock4Params:r}}function uE(e){const t=[],{extractWeights:s,getRemainingWeights:n}=vs(e),{extractDenseBlock4Params:i}=Ag(s,t),r=i(3,32,"dense0",!0),o=i(32,64,"dense1"),a=i(64,128,"dense2"),l=i(128,256,"dense3");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{dense0:r,dense1:o,dense2:a,dense3:l}}}function Ng(e){return function(t){const s=e(`${t}/filters`,4),n=e(`${t}/bias`,1);return{filters:s,bias:n}}}function Cg(e,t){const s=Vs(e,t),n=Ng(s),i=fc(s);function r(a,l=!1){const c=l?n(`${a}/conv0`):i(`${a}/conv0`),p=i(`${a}/conv1`),u=i(`${a}/conv2`);return{conv0:c,conv1:p,conv2:u}}function o(a,l=!1){const c=l?n(`${a}/conv0`):i(`${a}/conv0`),p=i(`${a}/conv1`),u=i(`${a}/conv2`),h=i(`${a}/conv3`);return{conv0:c,conv1:p,conv2:u,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:o}}function hE(e){const t=[],{extractDenseBlock4Params:s}=Cg(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2"),dense3:s("dense3")};return Is(e,t),{params:n,paramMappings:t}}class Rg extends Zt{constructor(){super("FaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=un(s,n).div(j(255));let r=Pu(i,t.dense0,!0);return r=Pu(r,t.dense1),r=Pu(r,t.dense2),r=Pu(r,t.dense3),r=ds(r,[7,7],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await nt(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(e){return hE(e)}extractParams(e){return uE(e)}}function Bu(e,t){return C(()=>$(Te(e,t.weights),t.bias))}function dE(e,t,s){const n=[],{extractWeights:i,getRemainingWeights:r}=vs(e),o=Tg(i,n),a=o(t,s,"fc");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{fc:a}}}function mE(e){const t=[],s=Vs(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:n("fc")};return Is(e,t),{params:i,paramMappings:t}}function Og(e){const t={},s={};return Object.keys(e).forEach(n=>{const i=n.startsWith("fc")?s:t;i[n]=e[n]}),{featureExtractorMap:t,classifierMap:s}}class Eg extends Zt{constructor(e,t){super(e);this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return C(()=>{const s=e instanceof vi?this.faceFeatureExtractor.forwardInput(e):e;return Bu(s.as2D(s.shape[0],-1),t.fc)})}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:s}=this.extractClassifierParams(e);this._params=t,this._paramMappings=s}extractClassifierParams(e){return dE(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Og(e);return this.faceFeatureExtractor.loadFromWeightMap(t),mE(s)}extractParams(e){const t=this.getClassifierChannelsIn(),s=this.getClassifierChannelsOut(),n=s*t+s,i=e.slice(0,e.length-n),r=e.slice(e.length-n);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(r)}}const fh=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class lr{constructor(e){if(e.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);fh.forEach((t,s)=>{this[t]=e[s]})}asSortedArray(){return fh.map(e=>({expression:e,probability:this[e]})).sort((e,t)=>t.probability-e.probability)}}class gh extends Eg{constructor(e=new Rg){super("FaceExpressionNet",e)}forwardInput(e){return C(()=>ts(this.runNet(e)))}async forward(e){return this.forwardInput(await nt(e))}async predictExpressions(e){const t=await nt(e),s=await this.forwardInput(t),n=await Promise.all(qe(s).map(async r=>{const o=await r.data();return r.dispose(),o}));s.dispose();const i=n.map(r=>new lr(r));return t.isBatchInput?i:i[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function dh(e){return e.expressions instanceof lr}function Rc(e,t){const s={expressions:t};return Object.assign({},e,s)}function DG(e,t,s=.1,n){const i=Array.isArray(t)?t:[t];i.forEach(r=>{const o=r instanceof lr?r:dh(r)?r.expressions:void 0;if(!o)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const a=o.asSortedArray(),l=a.filter(u=>u.probability>s),c=Mn(r)?r.detection.box.bottomLeft:n||new _e(0,0),p=new hc(l.map(u=>`${u.expression} (${Ca(u.probability)})`),c);p.draw(e)})}function or(e){return Mn(e)&&e.landmarks instanceof Hs&&e.unshiftedLandmarks instanceof Hs&&e.alignedRect instanceof ht}function Ao(e,t){const{box:s}=e.detection,n=t.shiftBy(s.x,s.y),i=n.align(),{imageDims:r}=e.detection,o=new ht(e.detection.score,i.rescale(r.reverse()),r),a={landmarks:n,unshiftedLandmarks:t,alignedRect:o};return Object.assign({},e,a)}class fE{constructor(e={}){const{drawLines:t=!0,drawPoints:s=!0,lineWidth:n,lineColor:i,pointSize:r,pointColor:o}=e;this.drawLines=t,this.drawPoints=s,this.lineWidth=n||1,this.pointSize=r||2,this.lineColor=i||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}}class gE{constructor(e,t={}){this.faceLandmarks=e,this.options=new fE(t)}draw(e){const t=us(e),{drawLines:s,drawPoints:n,lineWidth:i,lineColor:r,pointSize:o,pointColor:a}=this.options;if(s&&this.faceLandmarks instanceof Va&&(t.strokeStyle=r,t.lineWidth=i,er(t,this.faceLandmarks.getJawOutline()),er(t,this.faceLandmarks.getLeftEyeBrow()),er(t,this.faceLandmarks.getRightEyeBrow()),er(t,this.faceLandmarks.getNose()),er(t,this.faceLandmarks.getLeftEye(),!0),er(t,this.faceLandmarks.getRightEye(),!0),er(t,this.faceLandmarks.getMouth(),!0)),n){t.strokeStyle=a,t.fillStyle=a;const l=c=>{t.beginPath(),t.arc(c.x,c.y,o,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(l)}}}function FG(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof Hs?n:or(n)?n.landmarks:void 0;if(!i)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new gE(i).draw(e)})}const Zu={};Ee(Zu,{AnchorPosition:()=>Ii,DrawBox:()=>hL,DrawBoxOptions:()=>pE,DrawFaceLandmarks:()=>gE,DrawFaceLandmarksOptions:()=>fE,DrawTextField:()=>hc,DrawTextFieldOptions:()=>Sg,drawContour:()=>er,drawDetections:()=>kG,drawFaceExpressions:()=>DG,drawFaceLandmarks:()=>FG});function MG(e,t){const s=dc(e,t),n=mc(e,t);function i(o,a,l){const c=n(o,a,`${l}/separable_conv0`),p=n(a,a,`${l}/separable_conv1`),u=s(o,a,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:p,expansion_conv:u}}function r(o,a){const l=n(o,o,`${a}/separable_conv0`),c=n(o,o,`${a}/separable_conv1`),p=n(o,o,`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:s,extractSeparableConvParams:n,extractReductionBlockParams:i,extractMainBlockParams:r}}function yE(e,t){const s=[],{extractWeights:n,getRemainingWeights:i}=vs(e),{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:a,extractMainBlockParams:l}=MG(n,s),c=r(3,32,3,"entry_flow/conv_in"),p=a(32,64,"entry_flow/reduction_block_0"),u=a(64,128,"entry_flow/reduction_block_1"),h={conv_in:c,reduction_block_0:p,reduction_block_1:u},d={};Si(t,0,1).forEach(y=>{d[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});const m=a(128,256,"exit_flow/reduction_block"),f=o(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:s,params:{entry_flow:h,middle_flow:d,exit_flow:g}}}function UG(e,t){const s=Vs(e,t),n=Ng(s),i=fc(s);function r(a){const l=i(`${a}/separable_conv0`),c=i(`${a}/separable_conv1`),p=n(`${a}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:p}}function o(a){const l=i(`${a}/separable_conv0`),c=i(`${a}/separable_conv1`),p=i(`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}}function bE(e,t){const s=[],{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}=UG(e,s),a=n("entry_flow/conv_in"),l=r("entry_flow/reduction_block_0"),c=r("entry_flow/reduction_block_1"),p={conv_in:a,reduction_block_0:l,reduction_block_1:c},u={};Si(t,0,1).forEach(f=>{u[`main_block_${f}`]=o(`middle_flow/main_block_${f}`)});const h=r("exit_flow/reduction_block"),d=i("exit_flow/separable_conv"),m={reduction_block:h,separable_conv:d};return Is(e,s),{params:{entry_flow:p,middle_flow:u,exit_flow:m},paramMappings:s}}function wE(e,t,s){return $(rt(e,t.filters,s,"same"),t.bias)}function mL(e,t,s=!0){let n=s?Fe(e):e;return n=_s(n,t.separable_conv0,[1,1]),n=_s(Fe(n),t.separable_conv1,[1,1]),n=ft(n,[3,3],[2,2],"same"),n=$(n,wE(e,t.expansion_conv,[2,2])),n}function $G(e,t){let s=_s(Fe(e),t.separable_conv0,[1,1]);return s=_s(Fe(s),t.separable_conv1,[1,1]),s=_s(Fe(s),t.separable_conv2,[1,1]),s=$(s,e),s}class xE extends Zt{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=un(s,n).div(j(256));let r=Fe(wE(i,t.entry_flow.conv_in,[2,2]));return r=mL(r,t.entry_flow.reduction_block_0,!1),r=mL(r,t.entry_flow.reduction_block_1),Si(this._numMainBlocks,0,1).forEach(o=>{r=$G(r,t.middle_flow[`main_block_${o}`])}),r=mL(r,t.exit_flow.reduction_block),r=Fe(_s(r,t.exit_flow.separable_conv,[1,1])),r})}async forward(e){return this.forwardInput(await nt(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(e){return bE(e,this._numMainBlocks)}extractParams(e){return yE(e,this._numMainBlocks)}}function LE(e){const t=[],{extractWeights:s,getRemainingWeights:n}=vs(e),i=Tg(s,t),r=i(512,1,"fc/age"),o=i(512,2,"fc/gender");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{fc:{age:r,gender:o}}}}function SE(e){const t=[],s=Vs(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:{age:n("fc/age"),gender:n("fc/gender")}};return Is(e,t),{params:i,paramMappings:t}}var Jn;(function(e){e.FEMALE="female",e.MALE="male"})(Jn||(Jn={}));class vh extends Zt{constructor(e=new xE(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return C(()=>{const s=e instanceof vi?this.faceFeatureExtractor.forwardInput(e):e,n=ds(s,[7,7],[2,2],"valid").as2D(s.shape[0],-1),i=Bu(n,t.fc.age).as1D(),r=Bu(n,t.fc.gender);return{age:i,gender:r}})}forwardInput(e){return C(()=>{const{age:t,gender:s}=this.runNet(e);return{age:t,gender:ts(s)}})}async forward(e){return this.forwardInput(await nt(e))}async predictAgeAndGender(e){const t=await nt(e),s=await this.forwardInput(t),n=qe(s.age),i=qe(s.gender),r=n.map((a,l)=>({ageTensor:a,genderTensor:i[l]})),o=await Promise.all(r.map(async({ageTensor:a,genderTensor:l})=>{const c=(await a.data())[0],p=(await l.data())[0],u=p>.5,h=u?Jn.MALE:Jn.FEMALE,d=u?p:1-p;return a.dispose(),l.dispose(),{age:c,gender:h,genderProbability:d}}));return s.age.dispose(),s.gender.dispose(),t.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:s}=this.extractClassifierParams(e);this._params=t,this._paramMappings=s}extractClassifierParams(e){return LE(e)}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Og(e);return this.faceFeatureExtractor.loadFromWeightMap(t),SE(s)}extractParams(e){const t=512*1+1+(512*2+2),s=e.slice(0,e.length-t),n=e.slice(e.length-t);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(n)}}class _g extends Eg{postProcess(e,t,s){const n=s.map(({width:r,height:o})=>{const a=t/Math.max(o,r);return{width:r*a,height:o*a}}),i=n.length;return C(()=>{const r=(p,u)=>Ge([Wt([68],p),Wt([68],u)],1).as2D(1,136).as1D(),o=(p,u)=>{const{width:h,height:d}=n[p];return u(h,d)?Math.abs(h-d)/2:0},a=p=>o(p,(u,h)=>u<h),l=p=>o(p,(u,h)=>h<u),c=e.mul(Wt([i,136],t)).sub(Ge(Array.from(Array(i),(p,u)=>r(a(u),l(u))))).div(Ge(Array.from(Array(i),(p,u)=>r(n[u].width,n[u].height))));return c})}forwardInput(e){return C(()=>{const t=this.runNet(e);return this.postProcess(t,e.inputSize,e.inputDimensions.map(([s,n])=>({height:s,width:n})))})}async forward(e){return this.forwardInput(await nt(e))}async detectLandmarks(e){const t=await nt(e),s=C(()=>qe(this.forwardInput(t))),n=await Promise.all(s.map(async(i,r)=>{const o=Array.from(await i.data()),a=o.filter((c,p)=>wg(p)),l=o.filter((c,p)=>!wg(p));return new Va(Array(68).fill(0).map((c,p)=>new _e(a[p],l[p])),{height:t.getInputHeight(r),width:t.getInputWidth(r)})}));return s.forEach(i=>i.dispose()),t.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}}class za extends _g{constructor(e=new Rg){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function IE(e){const t=[],{extractDenseBlock3Params:s}=Cg(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2")};return Is(e,t),{params:n,paramMappings:t}}function vE(e){const t=[],{extractWeights:s,getRemainingWeights:n}=vs(e),{extractDenseBlock3Params:i}=Ag(s,t),r=i(3,32,"dense0",!0),o=i(32,64,"dense1"),a=i(64,128,"dense2");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{dense0:r,dense1:o,dense2:a}}}class TE extends Zt{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=un(s,n).div(j(255));let r=vg(i,t.dense0,!0);return r=vg(r,t.dense1),r=vg(r,t.dense2),r=ds(r,[14,14],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await nt(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(e){return IE(e)}extractParams(e){return vE(e)}}class mh extends _g{constructor(e=new TE){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class mS extends za{}function AE(e,t){return $(R(e,t.weights),t.biases)}function fL(e,t,s,n,i="same"){const{filters:r,bias:o}=t.conv;let a=rt(e,r,s,i);return a=$(a,o),a=AE(a,t.scale),n?Fe(a):a}function NE(e,t){return fL(e,t,[1,1],!0)}function gL(e,t){return fL(e,t,[1,1],!1)}function kg(e,t){return fL(e,t,[2,2],!0,"valid")}function WG(e,t){function s(a,l,c){const p=e(a),u=p.length/(l*c*c);if(iL(u))throw new Error(`depth has to be an integer: ${u}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${c}`);return C(()=>se(ss(p,[l,u,c,c]),[2,3,1,0]))}function n(a,l,c,p){const u=s(a,l,c),h=Oe(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:u,bias:h}}function i(a,l){const c=Oe(e(a)),p=Oe(e(a));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:p}}function r(a,l,c,p){const u=n(a,l,c,`${p}/conv`),h=i(l,`${p}/scale`);return{conv:u,scale:h}}function o(a,l,c,p,u=!1){const h=r((u?.5:1)*a,l,c,`${p}/conv1`),d=r(a,l,c,`${p}/conv2`);return{conv1:h,conv2:d}}return{extractConvLayerParams:r,extractResidualLayerParams:o}}function CE(e){const{extractWeights:t,getRemainingWeights:s}=vs(e),n=[],{extractConvLayerParams:i,extractResidualLayerParams:r}=WG(t,n),o=i(4704,32,7,"conv32_down"),a=r(9216,32,3,"conv32_1"),l=r(9216,32,3,"conv32_2"),c=r(9216,32,3,"conv32_3"),p=r(36864,64,3,"conv64_down",!0),u=r(36864,64,3,"conv64_1"),h=r(36864,64,3,"conv64_2"),d=r(36864,64,3,"conv64_3"),m=r(147456,128,3,"conv128_down",!0),f=r(147456,128,3,"conv128_1"),g=r(147456,128,3,"conv128_2"),y=r(589824,256,3,"conv256_down",!0),w=r(589824,256,3,"conv256_1"),x=r(589824,256,3,"conv256_2"),T=r(589824,256,3,"conv256_down_out"),A=C(()=>se(ls(t(256*128),[128,256]),[1,0]));if(n.push({paramPath:"fc"}),s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);const _={conv32_down:o,conv32_1:a,conv32_2:l,conv32_3:c,conv64_down:p,conv64_1:u,conv64_2:h,conv64_3:d,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:w,conv256_2:x,conv256_down_out:T,fc:A};return{params:_,paramMappings:n}}function zG(e,t){const s=Vs(e,t);function n(o){const a=s(`${o}/scale/weights`,1),l=s(`${o}/scale/biases`,1);return{weights:a,biases:l}}function i(o){const a=s(`${o}/conv/filters`,4),l=s(`${o}/conv/bias`,1),c=n(o);return{conv:{filters:a,bias:l},scale:c}}function r(o){return{conv1:i(`${o}/conv1`),conv2:i(`${o}/conv2`)}}return{extractConvLayerParams:i,extractResidualLayerParams:r}}function RE(e){const t=[],{extractConvLayerParams:s,extractResidualLayerParams:n}=zG(e,t),i=s("conv32_down"),r=n("conv32_1"),o=n("conv32_2"),a=n("conv32_3"),l=n("conv64_down"),c=n("conv64_1"),p=n("conv64_2"),u=n("conv64_3"),h=n("conv128_down"),d=n("conv128_1"),m=n("conv128_2"),f=n("conv256_down"),g=n("conv256_1"),y=n("conv256_2"),w=n("conv256_down_out"),x=e.fc;if(t.push({originalPath:"fc",paramPath:"fc"}),!nL(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);const T={conv32_down:i,conv32_1:r,conv32_2:o,conv32_3:a,conv64_down:l,conv64_1:c,conv64_2:p,conv64_3:u,conv128_down:h,conv128_1:d,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:w,fc:x};return Is(e,t),{params:T,paramMappings:t}}function Kn(e,t){let s=NE(e,t.conv1);return s=gL(s,t.conv2),s=$(s,e),s=Fe(s),s}function ju(e,t){let s=kg(e,t.conv1);s=gL(s,t.conv2);let n=ds(e,2,2,"valid");const i=ye(n.shape),r=n.shape[3]!==s.shape[3],o=n.shape[1]!==s.shape[1]||n.shape[2]!==s.shape[2];if(o){const a=[...s.shape];a[1]=1;const l=ye(a);s=be([s,l],1);const c=[...s.shape];c[2]=1;const p=ye(c);s=be([s,p],2)}return n=r?be([n,i],3):n,s=$(n,s),s=Fe(s),s}class Wa extends Zt{constructor(){super("FaceRecognitionNet")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceRecognitionNet - load model before inference");return C(()=>{const s=G(e.toBatchTensor(150,!0),"float32"),n=[122.782,117.001,104.298],i=un(s,n).div(j(256));let r=kg(i,t.conv32_down);r=ft(r,3,2,"valid"),r=Kn(r,t.conv32_1),r=Kn(r,t.conv32_2),r=Kn(r,t.conv32_3),r=ju(r,t.conv64_down),r=Kn(r,t.conv64_1),r=Kn(r,t.conv64_2),r=Kn(r,t.conv64_3),r=ju(r,t.conv128_down),r=Kn(r,t.conv128_1),r=Kn(r,t.conv128_2),r=ju(r,t.conv256_down),r=Kn(r,t.conv256_1),r=Kn(r,t.conv256_2),r=ju(r,t.conv256_down_out);const o=r.mean([1,2]),a=Te(o,t.fc);return a})}async forward(e){return this.forwardInput(await nt(e))}async computeFaceDescriptor(e){const t=await nt(e),s=C(()=>qe(this.forwardInput(t))),n=await Promise.all(s.map(i=>i.data()));return s.forEach(i=>i.dispose()),t.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(e){return RE(e)}extractParams(e){return CE(e)}}function dS(e){const t=new Wa;return t.extractWeights(e),t}function Oc(e,t){const s={descriptor:t};return Object.assign({},e,s)}function hS(e){return typeof e.age=="number"}function Ec(e,t){const s={age:t};return Object.assign({},e,s)}function uS(e){return(e.gender===Jn.MALE||e.gender===Jn.FEMALE)&&uc(e.genderProbability)}function Cc(e,t,s){const n={gender:t,genderProbability:s};return Object.assign({},e,n)}function PG(e,t){function s(l,c){const p=ss(e(3*3*l),[3,3,l,1]),u=Oe(e(l)),h=Oe(e(l)),d=Oe(e(l)),m=Oe(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/batch_norm_scale`},{paramPath:`${c}/batch_norm_offset`},{paramPath:`${c}/batch_norm_mean`},{paramPath:`${c}/batch_norm_variance`}),{filters:p,batch_norm_scale:u,batch_norm_offset:h,batch_norm_mean:d,batch_norm_variance:m}}function n(l,c,p,u,h){const d=ss(e(l*c*p*p),[p,p,l,c]),m=Oe(e(c));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/${h?"batch_norm_offset":"bias"}`}),{filters:d,bias:m}}function i(l,c,p,u){const{filters:h,bias:d}=n(l,c,p,u,!0);return{filters:h,batch_norm_offset:d}}function r(l,c,p){const u=s(l,`${p}/depthwise_conv`),h=i(l,c,1,`${p}/pointwise_conv`);return{depthwise_conv:u,pointwise_conv:h}}function o(){const l=i(3,32,3,"mobilenetv1/conv_0"),c=r(32,64,"mobilenetv1/conv_1"),p=r(64,128,"mobilenetv1/conv_2"),u=r(128,128,"mobilenetv1/conv_3"),h=r(128,256,"mobilenetv1/conv_4"),d=r(256,256,"mobilenetv1/conv_5"),m=r(256,512,"mobilenetv1/conv_6"),f=r(512,512,"mobilenetv1/conv_7"),g=r(512,512,"mobilenetv1/conv_8"),y=r(512,512,"mobilenetv1/conv_9"),w=r(512,512,"mobilenetv1/conv_10"),x=r(512,512,"mobilenetv1/conv_11"),T=r(512,1024,"mobilenetv1/conv_12"),A=r(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:p,conv_3:u,conv_4:h,conv_5:d,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:w,conv_11:x,conv_12:T,conv_13:A}}function a(){const l=i(1024,256,1,"prediction_layer/conv_0"),c=i(256,512,3,"prediction_layer/conv_1"),p=i(512,128,1,"prediction_layer/conv_2"),u=i(128,256,3,"prediction_layer/conv_3"),h=i(256,128,1,"prediction_layer/conv_4"),d=i(128,256,3,"prediction_layer/conv_5"),m=i(256,64,1,"prediction_layer/conv_6"),f=i(64,128,3,"prediction_layer/conv_7"),g=n(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=n(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),w=n(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=n(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),T=n(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),A=n(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),_=n(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),E=n(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),F=n(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),D=n(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),M=n(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),P=n(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),B={box_encoding_predictor:g,class_predictor:y},Y={box_encoding_predictor:w,class_predictor:x},q={box_encoding_predictor:T,class_predictor:A},K={box_encoding_predictor:_,class_predictor:E},H={box_encoding_predictor:F,class_predictor:D},Q={box_encoding_predictor:M,class_predictor:P};return{conv_0:l,conv_1:c,conv_2:p,conv_3:u,conv_4:h,conv_5:d,conv_6:m,conv_7:f,box_predictor_0:B,box_predictor_1:Y,box_predictor_2:q,box_predictor_3:K,box_predictor_4:H,box_predictor_5:Q}}return{extractMobilenetV1Params:o,extractPredictionLayerParams:a}}function OE(e){const t=[],{extractWeights:s,getRemainingWeights:n}=vs(e),{extractMobilenetV1Params:i,extractPredictionLayerParams:r}=PG(s,t),o=i(),a=r(),l=ll(s(5118*4),[1,5118,4]),c={extra_dim:l};if(t.push({paramPath:"output_layer/extra_dim"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{mobilenetv1:o,prediction_layer:a,output_layer:c},paramMappings:t}}function BG(e,t){const s=Vs(e,t);function n(c,p,u){const h=s(`${c}/Conv2d_${p}_pointwise/weights`,4,`${u}/filters`),d=s(`${c}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${u}/batch_norm_offset`);return{filters:h,batch_norm_offset:d}}function i(c){const p=`mobilenetv1/conv_${c}`,u=`MobilenetV1/Conv2d_${c}_depthwise`,h=`${p}/depthwise_conv`,d=`${p}/pointwise_conv`,m=s(`${u}/depthwise_weights`,4,`${h}/filters`),f=s(`${u}/BatchNorm/gamma`,1,`${h}/batch_norm_scale`),g=s(`${u}/BatchNorm/beta`,1,`${h}/batch_norm_offset`),y=s(`${u}/BatchNorm/moving_mean`,1,`${h}/batch_norm_mean`),w=s(`${u}/BatchNorm/moving_variance`,1,`${h}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:w},pointwise_conv:n("MobilenetV1",c,d)}}function r(){return{conv_0:n("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:i(1),conv_2:i(2),conv_3:i(3),conv_4:i(4),conv_5:i(5),conv_6:i(6),conv_7:i(7),conv_8:i(8),conv_9:i(9),conv_10:i(10),conv_11:i(11),conv_12:i(12),conv_13:i(13)}}function o(c,p){const u=s(`${c}/weights`,4,`${p}/filters`),h=s(`${c}/biases`,1,`${p}/bias`);return{filters:u,bias:h}}function a(c){const p=o(`Prediction/BoxPredictor_${c}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${c}/box_encoding_predictor`),u=o(`Prediction/BoxPredictor_${c}/ClassPredictor`,`prediction_layer/box_predictor_${c}/class_predictor`);return{box_encoding_predictor:p,class_predictor:u}}function l(){return{conv_0:n("Prediction",0,"prediction_layer/conv_0"),conv_1:n("Prediction",1,"prediction_layer/conv_1"),conv_2:n("Prediction",2,"prediction_layer/conv_2"),conv_3:n("Prediction",3,"prediction_layer/conv_3"),conv_4:n("Prediction",4,"prediction_layer/conv_4"),conv_5:n("Prediction",5,"prediction_layer/conv_5"),conv_6:n("Prediction",6,"prediction_layer/conv_6"),conv_7:n("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:a(0),box_predictor_1:a(1),box_predictor_2:a(2),box_predictor_3:a(3),box_predictor_4:a(4),box_predictor_5:a(5)}}return{extractMobilenetV1Params:r,extractPredictionLayerParams:l}}function EE(e){const t=[],{extractMobilenetV1Params:s,extractPredictionLayerParams:n}=BG(e,t),i=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!tr(i))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${i}`);const r={mobilenetv1:s(),prediction_layer:n(),output_layer:{extra_dim:i}};return Is(e,t),{params:r,paramMappings:t}}function Dn(e,t,s){return C(()=>{let n=rt(e,t.filters,s,"same");return n=$(n,t.batch_norm_offset),xt(n,0,6)})}const jG=.0010000000474974513;function VG(e,t,s){return C(()=>{let n=fn(e,t.filters,s,"same");return n=Xs(n,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,jG),xt(n,0,6)})}function GG(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function _E(e,t){return C(()=>{let s,n=Dn(e,t.conv_0,[2,2]);const i=[t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13];if(i.forEach((r,o)=>{const a=o+1,l=GG(a);n=VG(n,r.depthwise_conv,l),n=Dn(n,r.pointwise_conv,[1,1]),a===11&&(s=n)}),s===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:n,conv11:s}})}function kE(e,t,s,n,i){const r=e.shape[0],o=Math.min(s,r),a=t.map((p,u)=>({score:p,boxIndex:u})).filter(p=>p.score>i).sort((p,u)=>u.score-p.score),l=p=>p<=n?1:0,c=[];return a.forEach(p=>{if(c.length>=o)return;const u=p.score;for(let h=c.length-1;h>=0;--h){const d=qG(e,p.boxIndex,c[h]);if(d===0)continue;if(p.score*=l(d),p.score<=i)break}u===p.score&&c.push(p.boxIndex)}),c}function qG(e,t,s){const n=e.arraySync(),i=Math.min(n[t][0],n[t][2]),r=Math.min(n[t][1],n[t][3]),o=Math.max(n[t][0],n[t][2]),a=Math.max(n[t][1],n[t][3]),l=Math.min(n[s][0],n[s][2]),c=Math.min(n[s][1],n[s][3]),p=Math.max(n[s][0],n[s][2]),u=Math.max(n[s][1],n[s][3]),h=(o-i)*(a-r),d=(p-l)*(u-c);if(h<=0||d<=0)return 0;const m=Math.max(i,l),f=Math.max(r,c),g=Math.min(o,p),y=Math.min(a,u),w=Math.max(g-m,0)*Math.max(y-f,0);return w/(h+d-w)}function HG(e){const t=qe(se(e,[1,0])),s=[X(t[2],t[0]),X(t[3],t[1])],n=[$(t[0],Z(s[0],j(2))),$(t[1],Z(s[1],j(2)))];return{sizes:s,centers:n}}function YG(e,t){const{sizes:s,centers:n}=HG(e),i=qe(se(t,[1,0])),r=Z(R(ut(Z(i[2],j(5))),s[0]),j(2)),o=$(R(Z(i[0],j(10)),s[0]),n[0]),a=Z(R(ut(Z(i[3],j(5))),s[1]),j(2)),l=$(R(Z(i[1],j(10)),s[1]),n[1]);return se(Ge([X(o,r),X(l,a),$(o,r),$(l,a)]),[1,0])}function DE(e,t,s){return C(()=>{const n=e.shape[0];let i=YG(O($s(s.extra_dim,[n,1,1]),[-1,4]),O(e,[-1,4]));i=O(i,[n,i.shape[0]/n,4]);const r=os(he(t,[0,0,1],[-1,-1,-1]));let o=he(r,[0,0,0],[-1,-1,1]);o=O(o,[n,o.shape[1]]);const a=qe(i),l=qe(o);return{boxes:a,scores:l}})}function Ea(e,t){return C(()=>{const s=e.shape[0],n=O(Oa(e,t.box_encoding_predictor),[s,-1,1,4]),i=O(Oa(e,t.class_predictor),[s,-1,3]);return{boxPredictionEncoding:n,classPrediction:i}})}function FE(e,t,s){return C(()=>{const n=Dn(e,s.conv_0,[1,1]),i=Dn(n,s.conv_1,[2,2]),r=Dn(i,s.conv_2,[1,1]),o=Dn(r,s.conv_3,[2,2]),a=Dn(o,s.conv_4,[1,1]),l=Dn(a,s.conv_5,[2,2]),c=Dn(l,s.conv_6,[1,1]),p=Dn(c,s.conv_7,[2,2]),u=Ea(t,s.box_predictor_0),h=Ea(e,s.box_predictor_1),d=Ea(i,s.box_predictor_2),m=Ea(o,s.box_predictor_3),f=Ea(l,s.box_predictor_4),g=Ea(p,s.box_predictor_5),y=be([u.boxPredictionEncoding,h.boxPredictionEncoding,d.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),w=be([u.classPrediction,h.classPrediction,d.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:w}})}class pn{constructor({minConfidence:e,maxResults:t}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=e||.5,this._maxResults=t||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}class To extends Zt{constructor(){super("SsdMobilenetv1")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("SsdMobilenetv1 - load model before inference");return C(()=>{const s=G(e.toBatchTensor(512,!1),"float32"),n=X(R(s,j(.007843137718737125)),j(1)),i=_E(n,t.mobilenetv1),{boxPredictions:r,classPredictions:o}=FE(i.out,i.conv11,t.prediction_layer);return DE(r,o,t.output_layer)})}async forward(e){return this.forwardInput(await nt(e))}async locateFaces(e,t={}){const{maxResults:s,minConfidence:n}=new pn(t),i=await nt(e),{boxes:r,scores:o}=this.forwardInput(i),a=r[0],l=o[0];for(let w=1;w<r.length;w++)r[w].dispose(),o[w].dispose();const c=Array.from(await l.data()),p=.5,u=kE(a,c,s,p,n),h=i.getReshapedInputDimensions(0),d=i.inputSize,m=d/h.width,f=d/h.height,g=a.arraySync(),y=u.map(w=>{const[x,T]=[Math.max(0,g[w][0]),Math.min(1,g[w][2])].map(E=>E*f),[A,_]=[Math.max(0,g[w][1]),Math.min(1,g[w][3])].map(E=>E*m);return new ht(c[w],new ja(A,x,_-A,T-x),{height:i.getInputHeight(0),width:i.getInputWidth(0)})});return a.dispose(),l.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(e){return EE(e)}extractParams(e){return OE(e)}}function jg(e){const t=new To;return t.extractWeights(e),t}function BL(e){return jg(e)}class jL extends To{}const ME=.4,UE=[new _e(.738768,.874946),new _e(2.42204,2.65704),new _e(4.30971,7.04493),new _e(10.246,4.59428),new _e(12.6868,11.8741)],$E=[new _e(1.603231,2.094468),new _e(6.041143,7.080126),new _e(2.882459,3.518061),new _e(4.266906,5.178857),new _e(9.041765,10.66308)],WE=[117.001,114.697,97.404],zE="tiny_yolov2_model",PE="tiny_yolov2_separable_conv_model";const Dg=e=>typeof e=="number";function eh(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(!Dg(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=>Dg(t.x)&&Dg(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(Dg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function gc(e){return C(()=>{const t=R(e,j(.10000000149011612));return $(Fe(X(e,t)),t)})}function sr(e,t){return C(()=>{let s=Pt(e,[[0,0],[1,1],[1,1],[0,0]]);return s=rt(s,t.conv.filters,[1,1],"valid"),s=X(s,t.bn.sub),s=R(s,t.bn.truediv),s=$(s,t.conv.bias),gc(s)})}function nr(e,t){return C(()=>{let s=Pt(e,[[0,0],[1,1],[1,1],[0,0]]);return s=eo(s,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),s=$(s,t.bias),gc(s)})}function KG(e,t){const s=dc(e,t);function n(o,a){const l=Oe(e(o)),c=Oe(e(o));return t.push({paramPath:`${a}/sub`},{paramPath:`${a}/truediv`}),{sub:l,truediv:c}}function i(o,a,l){const c=s(o,a,3,`${l}/conv`),p=n(a,`${l}/bn`);return{conv:c,bn:p}}const r=mc(e,t);return{extractConvParams:s,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}}function BE(e,t,s,n){const{extractWeights:i,getRemainingWeights:r}=vs(e),o=[],{extractConvParams:a,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=KG(i,o);let p;if(t.withSeparableConvs){const[u,h,d,m,f,g,y,w,x]=n,T=t.isFirstLayerConv2d?a(u,h,3,"conv0"):c(u,h,"conv0"),A=c(h,d,"conv1"),_=c(d,m,"conv2"),E=c(m,f,"conv3"),F=c(f,g,"conv4"),D=c(g,y,"conv5"),M=w?c(y,w,"conv6"):void 0,P=x?c(w,x,"conv7"):void 0,B=a(x||w||y,5*s,1,"conv8");p={conv0:T,conv1:A,conv2:_,conv3:E,conv4:F,conv5:D,conv6:M,conv7:P,conv8:B}}else{const[u,h,d,m,f,g,y,w,x]=n,T=l(u,h,"conv0"),A=l(h,d,"conv1"),_=l(d,m,"conv2"),E=l(m,f,"conv3"),F=l(f,g,"conv4"),D=l(g,y,"conv5"),M=l(y,w,"conv6"),P=l(w,x,"conv7"),B=a(x,5*s,1,"conv8");p={conv0:T,conv1:A,conv2:_,conv3:E,conv4:F,conv5:D,conv6:M,conv7:P,conv8:B}}if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:p,paramMappings:o}}function XG(e,t){const s=Vs(e,t);function n(a){const l=s(`${a}/sub`,1),c=s(`${a}/truediv`,1);return{sub:l,truediv:c}}function i(a){const l=s(`${a}/filters`,4),c=s(`${a}/bias`,1);return{filters:l,bias:c}}function r(a){const l=i(`${a}/conv`),c=n(`${a}/bn`);return{conv:l,bn:c}}const o=fc(s);return{extractConvParams:i,extractConvWithBatchNormParams:r,extractSeparableConvParams:o}}function jE(e,t){const s=[],{extractConvParams:n,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}=XG(e,s);let o;if(t.withSeparableConvs){const a=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?n("conv0"):r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:a>7?r("conv6"):void 0,conv7:a>8?r("conv7"):void 0,conv8:n("conv8")}}else o={conv0:i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:i("conv6"),conv7:i("conv7"),conv8:n("conv8")};return Is(e,s),{params:o,paramMappings:s}}var th;(function(e){e[e.XS=224]="XS",e[e.SM=320]="SM",e[e.MD=416]="MD",e[e.LG=608]="LG"})(th||(th={}));class Xn{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 yc extends Zt{constructor(e){super("TinyYolov2");eh(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 s=sr(e,t.conv0);return s=ft(s,[2,2],[2,2],"same"),s=sr(s,t.conv1),s=ft(s,[2,2],[2,2],"same"),s=sr(s,t.conv2),s=ft(s,[2,2],[2,2],"same"),s=sr(s,t.conv3),s=ft(s,[2,2],[2,2],"same"),s=sr(s,t.conv4),s=ft(s,[2,2],[2,2],"same"),s=sr(s,t.conv5),s=ft(s,[2,2],[1,1],"same"),s=sr(s,t.conv6),s=sr(s,t.conv7),Oa(s,t.conv8,"valid",!1)}runMobilenet(e,t){let s=this.config.isFirstLayerConv2d?gc(Oa(e,t.conv0,"valid",!1)):nr(e,t.conv0);return s=ft(s,[2,2],[2,2],"same"),s=nr(s,t.conv1),s=ft(s,[2,2],[2,2],"same"),s=nr(s,t.conv2),s=ft(s,[2,2],[2,2],"same"),s=nr(s,t.conv3),s=ft(s,[2,2],[2,2],"same"),s=nr(s,t.conv4),s=ft(s,[2,2],[2,2],"same"),s=nr(s,t.conv5),s=ft(s,[2,2],[1,1],"same"),s=t.conv6?nr(s,t.conv6):s,s=t.conv7?nr(s,t.conv7):s,Oa(s,t.conv8,"valid",!1)}forwardInput(e,t){const{params:s}=this;if(!s)throw new Error("TinyYolov2 - load model before inference");return C(()=>{let n=G(e.toBatchTensor(t,!1),"float32");return n=this.config.meanRgb?un(n,this.config.meanRgb):n,n=n.div(j(256)),this.config.withSeparableConvs?this.runMobilenet(n,s):this.runTinyYolov2(n,s)})}async forward(e,t){return await this.forwardInput(await nt(e),t)}async detect(e,t={}){const{inputSize:s,scoreThreshold:n}=new Xn(t),i=await nt(e),r=await this.forwardInput(i,s),o=C(()=>qe(r)[0].expandDims()),a={width:i.getInputWidth(0),height:i.getInputHeight(0)},l=await this.extractBoxes(o,i.getReshapedInputDimensions(0),n);r.dispose(),o.dispose();const c=l.map(f=>f.box),p=l.map(f=>f.score),u=l.map(f=>f.classScore),h=l.map(f=>this.config.classes[f.label]),d=ih(c.map(f=>f.rescale(s)),p,this.config.iouThreshold,!0),m=d.map(f=>new Oo(p[f],u[f],h[f],c[f],a));return m}getDefaultModelName(){return""}extractParamsFromWeigthMap(e){return jE(e,this.config)}extractParams(e){const t=this.config.filterSizes||yc.DEFAULT_FILTER_SIZES,s=t?t.length:void 0;if(s!==7&&s!==8&&s!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${s} filterSizes in config`);return BE(e,this.config,this.boxEncodingSize,t)}async extractBoxes(e,t,s){const{width:n,height:i}=t,r=Math.max(n,i),o=r/n,a=r/i,l=e.shape[1],c=this.config.anchors.length,[p,u,h]=C(()=>{const g=e.reshape([l,l,c,this.boxEncodingSize]),y=g.slice([0,0,0,0],[l,l,c,4]),w=g.slice([0,0,0,4],[l,l,c,1]),x=this.withClassScores?ts(g.slice([0,0,0,5],[l,l,c,this.config.classes.length]),3):j(0);return[y,w,x]}),d=[],m=await u.array(),f=await p.array();for(let g=0;g<l;g++)for(let y=0;y<l;y++)for(let w=0;w<c;w++){const x=$a(m[g][y][w][0]);if(!s||x>s){const T=(y+$a(f[g][y][w][0]))/l*o,A=(g+$a(f[g][y][w][1]))/l*a,_=Math.exp(f[g][y][w][2])*this.config.anchors[w].x/l*o,E=Math.exp(f[g][y][w][3])*this.config.anchors[w].y/l*a,F=T-_/2,D=A-E/2,M={row:g,col:y,anchor:w},{classScore:P,label:B}=this.withClassScores?await this.extractPredictedClass(h,M):{classScore:1,label:0};d.push({box:new Ga(F,D,F+_,D+E),score:x,classScore:x*P,label:B,...M})}}return p.dispose(),u.dispose(),h.dispose(),d}async extractPredictedClass(e,t){const{row:s,col:n,anchor:i}=t,r=await e.array();return Array(this.config.classes.length).fill(0).map((o,a)=>r[s][n][i][a]).map((o,a)=>({classScore:o,label:a})).reduce((o,a)=>o.classScore>a.classScore?o:a)}}yc.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Ma extends yc{constructor(e=!0){const t=Object.assign({},{withSeparableConvs:e,iouThreshold:ME,classes:["face"]},e?{anchors:$E,meanRgb:WE}:{anchors:UE,withClassScores:!0});super(t)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(e,t){const s=await this.detect(e,t);return s.map(n=>new ht(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?PE:zE}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}function zL(e,t=!0){const s=new Ma(t);return s.extractWeights(e),s}class sh extends Xn{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class hn{async then(e){return e(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}async function _a(e,t,s,n,i=({alignedRect:r})=>r){const r=e.map(l=>or(l)?i(l):l.detection),o=n||(t instanceof me?await Co(t,r):await No(t,r)),a=await s(o);return o.forEach(l=>l instanceof me&&l.dispose()),a}async function bc(e,t,s,n,i){return _a([e],t,async r=>s(r[0]),n,i)}const VE=.4,GE=[new _e(1.603231,2.094468),new _e(6.041143,7.080126),new _e(2.882459,3.518061),new _e(4.266906,5.178857),new _e(9.041765,10.66308)],qE=[117.001,114.697,97.404];class Ua extends yc{constructor(){const e={withSeparableConvs:!0,iouThreshold:VE,classes:["face"],anchors:GE,meanRgb:qE,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(e)}get anchors(){return this.config.anchors}async locateFaces(e,t){const s=await this.detect(e,t);return s.map(n=>new ht(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}const Be={ssdMobilenetv1:new To,tinyFaceDetector:new Ua,tinyYolov2:new Ma,faceLandmark68Net:new za,faceLandmark68TinyNet:new mh,faceRecognitionNet:new Wa,faceExpressionNet:new gh,ageGenderNet:new vh},Gg=(e,t)=>Be.ssdMobilenetv1.locateFaces(e,t),qL=(e,t)=>Be.tinyFaceDetector.locateFaces(e,t),HL=(e,t)=>Be.tinyYolov2.locateFaces(e,t),qg=e=>Be.faceLandmark68Net.detectLandmarks(e),YL=e=>Be.faceLandmark68TinyNet.detectLandmarks(e),KL=e=>Be.faceRecognitionNet.computeFaceDescriptor(e),XL=e=>Be.faceExpressionNet.predictExpressions(e),JL=e=>Be.ageGenderNet.predictAgeAndGender(e),Hg=e=>Be.ssdMobilenetv1.load(e),ZL=e=>Be.tinyFaceDetector.load(e),QL=e=>Be.tinyYolov2.load(e),eS=e=>Be.faceLandmark68Net.load(e),tS=e=>Be.faceLandmark68TinyNet.load(e),sS=e=>Be.faceRecognitionNet.load(e),nS=e=>Be.faceExpressionNet.load(e),iS=e=>Be.ageGenderNet.load(e),rS=Hg,oS=Gg,aS=qg;class HE extends hn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class qu extends HE{async run(){const e=await this.parentTask,t=await _a(e,this.input,async s=>await Promise.all(s.map(n=>Be.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return e.map((s,n)=>Rc(s,t[n]))}withAgeAndGender(){return new Vu(this,this.input)}}class Hu extends HE{async run(){const e=await this.parentTask;if(!e)return;const t=await bc(e,this.input,s=>Be.faceExpressionNet.predictExpressions(s),this.extractedFaces);return Rc(e,t)}withAgeAndGender(){return new Gu(this,this.input)}}class Lc extends qu{withAgeAndGender(){return new wc(this,this.input)}withFaceDescriptors(){return new ir(this,this.input)}}class Sc extends Hu{withAgeAndGender(){return new xc(this,this.input)}withFaceDescriptor(){return new rr(this,this.input)}}class YE extends hn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class Vu extends YE{async run(){const e=await this.parentTask,t=await _a(e,this.input,async s=>await Promise.all(s.map(n=>Be.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return e.map((s,n)=>{const{age:i,gender:r,genderProbability:o}=t[n];return Ec(Cc(s,r,o),i)})}withFaceExpressions(){return new qu(this,this.input)}}class Gu extends YE{async run(){const e=await this.parentTask;if(!e)return;const{age:t,gender:s,genderProbability:n}=await bc(e,this.input,i=>Be.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return Ec(Cc(e,s,n),t)}withFaceExpressions(){return new Hu(this,this.input)}}class wc extends Vu{withFaceExpressions(){return new Lc(this,this.input)}withFaceDescriptors(){return new ir(this,this.input)}}class xc extends Gu{withFaceExpressions(){return new Sc(this,this.input)}withFaceDescriptor(){return new rr(this,this.input)}}class hh extends hn{constructor(e,t){super();this.parentTask=e,this.input=t}}class ir extends hh{async run(){const e=await this.parentTask,t=await _a(e,this.input,s=>Promise.all(s.map(n=>Be.faceRecognitionNet.computeFaceDescriptor(n))),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return t.map((s,n)=>Oc(e[n],s))}withFaceExpressions(){return new Lc(this,this.input)}withAgeAndGender(){return new wc(this,this.input)}}class rr extends hh{async run(){const e=await this.parentTask;if(!e)return;const t=await bc(e,this.input,s=>Be.faceRecognitionNet.computeFaceDescriptor(s),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return Oc(e,t)}withFaceExpressions(){return new Sc(this,this.input)}withAgeAndGender(){return new xc(this,this.input)}}class ch extends hn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?Be.faceLandmark68TinyNet:Be.faceLandmark68Net}}class ph extends ch{async run(){const e=await this.parentTask,t=e.map(i=>i.detection),s=this.input instanceof me?await Co(this.input,t):await No(this.input,t),n=await Promise.all(s.map(i=>this.landmarkNet.detectLandmarks(i)));return s.forEach(i=>i instanceof me&&i.dispose()),e.map((i,r)=>Ao(i,n[r]))}withFaceExpressions(){return new Lc(this,this.input)}withAgeAndGender(){return new wc(this,this.input)}withFaceDescriptors(){return new ir(this,this.input)}}class uh extends ch{async run(){const e=await this.parentTask;if(!e)return;const{detection:t}=e,s=this.input instanceof me?await Co(this.input,[t]):await No(this.input,[t]),n=await this.landmarkNet.detectLandmarks(s[0]);return s.forEach(i=>i instanceof me&&i.dispose()),Ao(e,n)}withFaceExpressions(){return new Sc(this,this.input)}withAgeAndGender(){return new xc(this,this.input)}withFaceDescriptor(){return new rr(this,this.input)}}class ah extends hn{constructor(e,t=new pn){super();this.input=e,this.options=t}}class Nc extends ah{async run(){const{input:e,options:t}=this,s=t instanceof sh?n=>Be.tinyFaceDetector.locateFaces(n,t):t instanceof pn?n=>Be.ssdMobilenetv1.locateFaces(n,t):t instanceof Xn?n=>Be.tinyYolov2.locateFaces(n,t):null;if(!s)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return s(e)}runAndExtendWithFaceDetections(){return new Promise(async e=>{const t=await this.run();return e(t.map(s=>ar({},s)))})}withFaceLandmarks(e=!1){return new ph(this.runAndExtendWithFaceDetections(),this.input,e)}withFaceExpressions(){return new qu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}}class lh extends ah{async run(){const e=await new Nc(this.input,this.options);let t=e[0];return e.forEach(s=>{s.score>t.score&&(t=s)}),t}runAndExtendWithFaceDetection(){return new Promise(async e=>{const t=await this.run();return e(t?ar({},t):void 0)})}withFaceLandmarks(e=!1){return new uh(this.runAndExtendWithFaceDetection(),this.input,e)}withFaceExpressions(){return new Hu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Gu(this.runAndExtendWithFaceDetection(),this.input)}}function lS(e,t=new pn){return new lh(e,t)}function Ac(e,t=new pn){return new Nc(e,t)}async function Yg(e,t){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Ac(e,new pn(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function cS(e,t={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Ac(e,new Xn(t)).withFaceLandmarks().withFaceDescriptors()}const pS=Yg;function yh(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const s=Array.from(e),n=Array.from(t);return Math.sqrt(s.map((i,r)=>i-n[r]).reduce((i,r)=>i+Math.pow(r,2),0))}class Kg{constructor(e,t=.6){this._distanceThreshold=t;const s=Array.isArray(e)?e:[e];if(!s.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1;const i=()=>`person ${n++}`;this._labeledDescriptors=s.map(r=>{if(r instanceof hr)return r;if(r instanceof Float32Array)return new hr(i(),[r]);if(r.descriptor&&r.descriptor instanceof Float32Array)return new hr(i(),[r.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(s=>yh(s,e)).reduce((s,n)=>s+n,0)/(t.length||1)}matchDescriptor(e){return this.labeledDescriptors.map(({descriptors:t,label:s})=>new Dc(s,this.computeMeanDistance(e,t))).reduce((t,s)=>t.distance<s.distance?t:s)}findBestMatch(e){const t=this.matchDescriptor(e);return t.distance<this.distanceThreshold?t:new Dc("unknown",t.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(e=>e.toJSON())}}static fromJSON(e){const t=e.labeledDescriptors.map(s=>hr.fromJSON(s));return new Kg(t,e.distanceThreshold)}}function PL(e){const t=new Ua;return t.extractWeights(e),t}function Vg(e,t){const{width:s,height:n}=new Ts(t.width,t.height);if(s<=0||n<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:s,height:n})}`);if(Array.isArray(e))return e.map(i=>Vg(i,{width:s,height:n}));if(or(e)){const i=e.detection.forSize(s,n),r=e.unshiftedLandmarks.forSize(i.box.width,i.box.height);return Ao(ar(e,i),r)}return Mn(e)?ar(e,e.detection.forSize(s,n)):e instanceof Hs||e instanceof ht?e.forSize(s,n):e}var UL="0.5.3";class WL{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}return xS();})();
+/**
+ * @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
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT 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
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @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 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
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT 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. */
+//# sourceMappingURL=face-api.js.map
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
new file mode 100644
index 0000000..23bba94
--- /dev/null
+++ b/dist/face-api.js.map
@@ -0,0 +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", "disabled:string_decoder", "disabled:fs", "build/src/env/../../../src/env/isNodejs.ts", "build/src/../../src/index.ts", "node_modules/@tensorflow/tfjs-core/dist/backends/../../src/backends/backend.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", 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"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", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParamsFromWeigthMap.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Options.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Base.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2.ts", 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"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/Platform.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, 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+}
diff --git a/dist/face-api.min.js b/dist/face-api.min.js
deleted file mode 100644
index ab67e06..0000000
--- a/dist/face-api.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
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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 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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(`${this._name}.loadFromDisk - expected model file 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${t}`);return{obj:o,objProp:r}}}const ce=x(require("@tensorflow/tfjs"));function U(t,e,o){return ce.tidy(()=>{let r=ce.separableConv2d(t,e.depthwise_filter,e.pointwise_filter,o,"same");return r=ce.add(r,e.bias),r})}const L=x(require("@tensorflow/tfjs"));function Po(t,e,o=!1){return L.tidy(()=>{const r=L.relu(o?L.add(L.conv2d(t,e.conv0.filters,[2,2],"same"),e.conv0.bias):U(t,e.conv0,[2,2])),n=U(r,e.conv1,[1,1]),s=L.relu(L.add(r,n)),i=U(s,e.conv2,[1,1]);return L.relu(L.add(r,L.add(n,i)))})}function Ae(t,e,o=!1,r=!0){return L.tidy(()=>{const n=L.relu(o?L.add(L.conv2d(t,e.conv0.filters,r?[2,2]:[1,1],"same"),e.conv0.bias):U(t,e.conv0,r?[2,2]:[1,1])),s=U(n,e.conv1,[1,1]),i=L.relu(L.add(n,s)),a=U(i,e.conv2,[1,1]),c=L.relu(L.add(n,L.add(s,a))),p=U(c,e.conv3,[1,1]);return L.relu(L.add(n,L.add(s,L.add(a,p))))})}const At=x(require("@tensorflow/tfjs"));function Vt(t,e,o="same",r=!1){return At.tidy(()=>{const n=At.add(At.conv2d(t,e.filters,[1,1],o),e.bias);return r?At.relu(n):n})}function 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: ${s().length}`);return{paramMappings:r,params:{fc:a}}}function nn(t){const e=[],o=$(t,e);function r(s){const i=o(`${s}/weights`,2),a=o(`${s}/bias`,1);return{weights:i,bias:a}}const n={fc:r("fc")};return S(t,e),{params:n,paramMappings:e}}function jo(t){const e={},o={};return Object.keys(t).forEach(r=>{const n=r.startsWith("fc")?o:e;n[r]=t[r]}),{featureExtractorMap:e,classifierMap:o}}const sn=x(require("@tensorflow/tfjs"));class No extends R{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){const{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return sn.tidy(()=>{const o=t instanceof lt?this.faceFeatureExtractor.forwardInput(t):t;return 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 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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 W={conv32_down:n,conv32_1:s,conv32_2:i,conv32_3:a,conv64_down:c,conv64_1:p,conv64_2:m,conv64_3:f,conv128_down:d,conv128_1:h,conv128_2:b,conv256_down:u,conv256_1:v,conv256_2:_,conv256_down_out:g,fc:E};return S(t,e),{params:W,paramMappings:e}}const N=x(require("@tensorflow/tfjs"));function nt(t,e){let o=vn(t,e.conv1);return o=ir(o,e.conv2),o=N.add(o,t),o=N.relu(o),o}function Ne(t,e){let o=Uo(t,e.conv1);o=ir(o,e.conv2);let r=N.avgPool(t,2,2,"valid");const n=N.zeros(r.shape),s=r.shape[3]!==o.shape[3],i=r.shape[1]!==o.shape[1]||r.shape[2]!==o.shape[2];if(i){const a=[...o.shape];a[1]=1;const c=N.zeros(a);o=N.concat([o,c],1);const p=[...o.shape];p[2]=1;const m=N.zeros(p);o=N.concat([o,m],2)}return r=s?N.concat([r,n],3):r,o=N.add(r,o),o=N.relu(o),o}const Y=x(require("@tensorflow/tfjs"));class Qt extends R{constructor(){super("FaceRecognitionNet")}forwardInput(t){const{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return Y.tidy(()=>{const o=Y.cast(t.toBatchTensor(150,!0),"float32"),r=[122.782,117.001,104.298],n=q(o,r).div(Y.scalar(256));let s=Uo(n,e.conv32_down);s=Y.maxPool(s,3,2,"valid"),s=nt(s,e.conv32_1),s=nt(s,e.conv32_2),s=nt(s,e.conv32_3),s=Ne(s,e.conv64_down),s=nt(s,e.conv64_1),s=nt(s,e.conv64_2),s=nt(s,e.conv64_3),s=Ne(s,e.conv128_down),s=nt(s,e.conv128_1),s=nt(s,e.conv128_2),s=Ne(s,e.conv256_down),s=nt(s,e.conv256_1),s=nt(s,e.conv256_2),s=Ne(s,e.conv256_down_out);const i=s.mean([1,2]),a=Y.matMul(i,e.fc);return a})}async forward(t){return this.forwardInput(await M(t))}async computeFaceDescriptor(t){const e=await M(t),o=Y.tidy(()=>Y.unstack(this.forwardInput(e))),r=await Promise.all(o.map(n=>n.data()));return o.forEach(n=>n.dispose()),e.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(t){return bn(t)}extractParams(t){return _n(t)}}function Gr(t){const e=new Qt;return e.extractWeights(t),e}function Ee(t,e){const o={descriptor:e};return 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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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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
deleted file mode 100644
index f57a33e..0000000
--- a/dist/face-api.min.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["build/src/env/../../../src/env/isNodejs.ts", "build/src/../../src/index.ts", "build/src/draw/../../../src/draw/drawContour.ts", "build/src/classes/../../../src/classes/Dimensions.ts", "build/src/utils/../../../src/utils/index.ts", "build/src/classes/../../../src/classes/Point.ts", "build/src/classes/../../../src/classes/Box.ts", "build/src/classes/../../../src/classes/BoundingBox.ts", "build/src/classes/../../../src/classes/ObjectDetection.ts", "build/src/classes/../../../src/classes/FaceDetection.ts", "build/src/ops/../../../src/ops/iou.ts", "build/src/ops/../../../src/ops/minBbox.ts", "build/src/ops/../../../src/ops/nonMaxSuppression.ts", "build/src/ops/../../../src/ops/normalize.ts", "build/src/ops/../../../src/ops/padToSquare.ts", "build/src/ops/../../../src/ops/shuffleArray.ts", "build/src/ops/../../../src/ops/index.ts", "build/src/classes/../../../src/classes/Rect.ts", "build/src/classes/../../../src/classes/FaceLandmarks.ts", "build/src/classes/../../../src/classes/FaceLandmarks5.ts", "build/src/classes/../../../src/classes/FaceLandmarks68.ts", "build/src/classes/../../../src/classes/FaceMatch.ts", "build/src/classes/../../../src/classes/LabeledBox.ts", "build/src/classes/../../../src/classes/LabeledFaceDescriptors.ts", "build/src/classes/../../../src/classes/PredictedBox.ts", "build/src/factories/../../../src/factories/WithFaceDetection.ts", "build/src/env/../../../src/env/createBrowserEnv.ts", "build/src/env/../../../src/env/createFileSystem.ts", "build/src/env/../../../src/env/createNodejsEnv.ts", "build/src/env/../../../src/env/isBrowser.ts", "build/src/env/../../../src/env/index.ts", "build/src/dom/../../../src/dom/resolveInput.ts", "build/src/dom/../../../src/dom/getContext2dOrThrow.ts", "build/src/draw/../../../src/draw/DrawTextField.ts", "build/src/draw/../../../src/draw/DrawBox.ts", "build/src/draw/../../../src/draw/drawDetections.ts", "build/src/dom/../../../src/dom/isMediaLoaded.ts", "build/src/dom/../../../src/dom/awaitMediaLoaded.ts", "build/src/dom/../../../src/dom/bufferToImage.ts", "build/src/dom/../../../src/dom/getMediaDimensions.ts", "build/src/dom/../../../src/dom/createCanvas.ts", "build/src/dom/../../../src/dom/imageTensorToCanvas.ts", "build/src/dom/../../../src/dom/isMediaElement.ts", "build/src/dom/../../../src/dom/imageToSquare.ts", "build/src/dom/../../../src/dom/NetInput.ts", "build/src/dom/../../../src/dom/toNetInput.ts", "build/src/dom/../../../src/dom/extractFaces.ts", "build/src/dom/../../../src/dom/extractFaceTensors.ts", "build/src/dom/../../../src/dom/fetchOrThrow.ts", "build/src/dom/../../../src/dom/fetchImage.ts", "build/src/dom/../../../src/dom/fetchJson.ts", "build/src/dom/../../../src/dom/fetchNetWeights.ts", "build/src/common/../../../src/common/getModelUris.ts", "build/src/dom/../../../src/dom/loadWeightMap.ts", "build/src/dom/../../../src/dom/matchDimensions.ts", "build/src/../../src/NeuralNetwork.ts", "build/src/common/../../../src/common/depthwiseSeparableConv.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/denseBlock.ts", "build/src/common/../../../src/common/convLayer.ts", "build/src/common/../../../src/common/disposeUnusedWeightTensors.ts", "build/src/common/../../../src/common/extractConvParamsFactory.ts", "build/src/common/../../../src/common/extractFCParamsFactory.ts", "build/src/common/../../../src/common/types.ts", "build/src/common/../../../src/common/extractSeparableConvParamsFactory.ts", "build/src/common/../../../src/common/extractWeightEntryFactory.ts", "build/src/common/../../../src/common/extractWeightsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractorsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParams.ts", "build/src/common/../../../src/common/loadConvParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/loadParamsFactory.ts", 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-  "names": []
-}
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
deleted file mode 100644
index 24d19cd..0000000
--- a/dist/face-api.node.js
+++ /dev/null
@@ -1,4534 +0,0 @@
-var __defineProperty = Object.defineProperty;
-var __hasOwnProperty = Object.prototype.hasOwnProperty;
-var __commonJS = (callback, module) => () => {
-  if (!module) {
-    module = {exports: {}};
-    callback(module.exports, module);
-  }
-  return module.exports;
-};
-var __markAsModule = (target) => {
-  return __defineProperty(target, "__esModule", {value: true});
-};
-var __export = (target, all) => {
-  __markAsModule(target);
-  for (var name in all)
-    __defineProperty(target, name, {get: all[name], enumerable: true});
-};
-var __exportStar = (target, module) => {
-  __markAsModule(target);
-  if (typeof module === "object" || typeof module === "function") {
-    for (let key in module)
-      if (!__hasOwnProperty.call(target, key) && key !== "default")
-        __defineProperty(target, key, {get: () => module[key], enumerable: true});
-  }
-  return target;
-};
-var __toModule = (module) => {
-  if (module && module.__esModule)
-    return module;
-  return __exportStar(__defineProperty({}, "default", {value: module, enumerable: true}), module);
-};
-
-// build/src/env/isNodejs.js
-var require_isNodejs = __commonJS((exports, module) => {
-  __export(exports, {
-    isNodejs: () => isNodejs3
-  });
-  function isNodejs3() {
-    return typeof global === "object" && true && typeof module !== "undefined" && typeof process !== "undefined" && !!process.version;
-  }
-});
-
-// build/src/draw/drawContour.js
-function drawContour(ctx, points, isClosed = false) {
-  ctx.beginPath();
-  points.slice(1).forEach(({x, y}, prevIdx) => {
-    const from = points[prevIdx];
-    ctx.moveTo(from.x, from.y);
-    ctx.lineTo(x, y);
-  });
-  if (isClosed) {
-    const from = points[points.length - 1];
-    const to = points[0];
-    if (!from || !to) {
-      return;
-    }
-    ctx.moveTo(from.x, from.y);
-    ctx.lineTo(to.x, to.y);
-  }
-  ctx.stroke();
-}
-
-// build/src/classes/Dimensions.js
-class Dimensions {
-  constructor(width, height) {
-    if (!isValidNumber(width) || !isValidNumber(height)) {
-      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width, height})}`);
-    }
-    this._width = width;
-    this._height = height;
-  }
-  get width() {
-    return this._width;
-  }
-  get height() {
-    return this._height;
-  }
-  reverse() {
-    return new Dimensions(1 / this.width, 1 / this.height);
-  }
-}
-
-// build/src/utils/index.js
-const utils_exports = {};
-__export(utils_exports, {
-  computeReshapedDimensions: () => computeReshapedDimensions,
-  getCenterPoint: () => getCenterPoint,
-  isDimensions: () => isDimensions,
-  isEven: () => isEven,
-  isFloat: () => isFloat,
-  isTensor: () => isTensor,
-  isTensor1D: () => isTensor1D,
-  isTensor2D: () => isTensor2D,
-  isTensor3D: () => isTensor3D,
-  isTensor4D: () => isTensor4D,
-  isValidNumber: () => isValidNumber,
-  isValidProbablitiy: () => isValidProbablitiy,
-  range: () => range,
-  round: () => round
-});
-import {
-  Tensor
-} from "@tensorflow/tfjs";
-function isTensor(tensor2, dim) {
-  return tensor2 instanceof Tensor && tensor2.shape.length === dim;
-}
-function isTensor1D(tensor2) {
-  return isTensor(tensor2, 1);
-}
-function isTensor2D(tensor2) {
-  return isTensor(tensor2, 2);
-}
-function isTensor3D(tensor2) {
-  return isTensor(tensor2, 3);
-}
-function isTensor4D(tensor2) {
-  return isTensor(tensor2, 4);
-}
-function isFloat(num) {
-  return num % 1 !== 0;
-}
-function isEven(num) {
-  return num % 2 === 0;
-}
-function round(num, prec = 2) {
-  const f = Math.pow(10, prec);
-  return Math.floor(num * f) / f;
-}
-function isDimensions(obj) {
-  return obj && obj.width && obj.height;
-}
-function computeReshapedDimensions({width, height}, inputSize) {
-  const scale2 = inputSize / Math.max(height, width);
-  return new Dimensions(Math.round(width * scale2), Math.round(height * scale2));
-}
-function getCenterPoint(pts) {
-  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
-}
-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;
-}
-function isValidProbablitiy(num) {
-  return isValidNumber(num) && 0 <= num && num <= 1;
-}
-
-// build/src/classes/Point.js
-class Point {
-  constructor(x, y) {
-    this._x = x;
-    this._y = y;
-  }
-  get x() {
-    return this._x;
-  }
-  get y() {
-    return this._y;
-  }
-  add(pt) {
-    return new Point(this.x + pt.x, this.y + pt.y);
-  }
-  sub(pt) {
-    return new Point(this.x - pt.x, this.y - pt.y);
-  }
-  mul(pt) {
-    return new Point(this.x * pt.x, this.y * pt.y);
-  }
-  div(pt) {
-    return new Point(this.x / pt.x, this.y / pt.y);
-  }
-  abs() {
-    return new Point(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 Point(Math.floor(this.x), Math.floor(this.y));
-  }
-}
-
-// build/src/classes/Box.js
-class Box {
-  constructor(_box, allowNegativeDimensions = true) {
-    const box = _box || {};
-    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
-    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
-    if (!isRect && !isBbox) {
-      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);
-    }
-    const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top];
-    Box.assertIsValidBox({x, y, width, height}, "Box.constructor", allowNegativeDimensions);
-    this._x = x;
-    this._y = y;
-    this._width = width;
-    this._height = height;
-  }
-  static isRect(rect) {
-    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
-  }
-  static assertIsValidBox(box, callee, allowNegativeDimensions = false) {
-    if (!Box.isRect(box)) {
-      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);
-    }
-    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
-      throw new Error(`${callee} - width (${box.width}) and height (${box.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 Point(this.left, this.top);
-  }
-  get topRight() {
-    return new Point(this.right, this.top);
-  }
-  get bottomLeft() {
-    return new Point(this.left, this.bottom);
-  }
-  get bottomRight() {
-    return new Point(this.right, this.bottom);
-  }
-  round() {
-    const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val));
-    return new Box({x, y, width, height});
-  }
-  floor() {
-    const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val));
-    return new Box({x, y, width, height});
-  }
-  toSquare() {
-    let {x, y, width, height} = this;
-    const diff = Math.abs(width - height);
-    if (width < height) {
-      x -= diff / 2;
-      width += diff;
-    }
-    if (height < width) {
-      y -= diff / 2;
-      height += diff;
-    }
-    return new Box({x, y, width, height});
-  }
-  rescale(s) {
-    const scaleX = isDimensions(s) ? s.width : s;
-    const scaleY = isDimensions(s) ? s.height : s;
-    return new Box({
-      x: this.x * scaleX,
-      y: this.y * scaleY,
-      width: this.width * scaleX,
-      height: this.height * scaleY
-    });
-  }
-  pad(padX, padY) {
-    let [x, y, width, height] = [
-      this.x - padX / 2,
-      this.y - padY / 2,
-      this.width + padX,
-      this.height + padY
-    ];
-    return new Box({x, y, width, height});
-  }
-  clipAtImageBorders(imgWidth, imgHeight) {
-    const {x, y, right, bottom} = this;
-    const clippedX = Math.max(x, 0);
-    const clippedY = Math.max(y, 0);
-    const newWidth = right - clippedX;
-    const newHeight = bottom - clippedY;
-    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);
-    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);
-    return new Box({x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight}).floor();
-  }
-  shift(sx, sy) {
-    const {width, height} = this;
-    const x = this.x + sx;
-    const y = this.y + sy;
-    return new Box({x, y, width, height});
-  }
-  padAtBorders(imageHeight, imageWidth) {
-    const w = this.width + 1;
-    const h = this.height + 1;
-    let dx = 1;
-    let dy = 1;
-    let edx = w;
-    let edy = h;
-    let x = this.left;
-    let y = this.top;
-    let ex = this.right;
-    let ey = this.bottom;
-    if (ex > imageWidth) {
-      edx = -ex + imageWidth + w;
-      ex = imageWidth;
-    }
-    if (ey > imageHeight) {
-      edy = -ey + imageHeight + h;
-      ey = imageHeight;
-    }
-    if (x < 1) {
-      edy = 2 - x;
-      x = 1;
-    }
-    if (y < 1) {
-      edy = 2 - y;
-      y = 1;
-    }
-    return {dy, edy, dx, edx, y, ey, x, ex, w, h};
-  }
-  calibrate(region) {
-    return new Box({
-      left: this.left + region.left * this.width,
-      top: this.top + region.top * this.height,
-      right: this.right + region.right * this.width,
-      bottom: this.bottom + region.bottom * this.height
-    }).toSquare().round();
-  }
-}
-
-// build/src/classes/BoundingBox.js
-class BoundingBox extends Box {
-  constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-    super({left, top, right, bottom}, allowNegativeDimensions);
-  }
-}
-
-// build/src/classes/ObjectDetection.js
-class ObjectDetection {
-  constructor(score, classScore, className, relativeBox, imageDims) {
-    this._imageDims = new Dimensions(imageDims.width, imageDims.height);
-    this._score = score;
-    this._classScore = classScore;
-    this._className = className;
-    this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse());
-  }
-  forSize(width, height) {
-    return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, {width, height});
-  }
-}
-
-// build/src/classes/FaceDetection.js
-class FaceDetection extends ObjectDetection {
-  constructor(score, relativeBox, imageDims) {
-    super(score, score, "", relativeBox, imageDims);
-  }
-  forSize(width, height) {
-    const {score, relativeBox, imageDims} = super.forSize(width, height);
-    return new FaceDetection(score, relativeBox, imageDims);
-  }
-}
-
-// build/src/ops/iou.js
-function iou(box1, box2, isIOU = true) {
-  const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
-  const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
-  const interSection = width * height;
-  return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area);
-}
-
-// build/src/ops/minBbox.js
-function minBbox(pts) {
-  const xs = pts.map((pt) => pt.x);
-  const ys = pts.map((pt) => pt.y);
-  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 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) {
-    const curr = indicesSortedByScore.pop();
-    pick.push(curr);
-    const indices = indicesSortedByScore;
-    const outputs = [];
-    for (let i = 0; i < indices.length; i++) {
-      const idx = indices[i];
-      const currBox = boxes[curr];
-      const idxBox = boxes[idx];
-      outputs.push(iou(currBox, idxBox, isIOU));
-    }
-    indicesSortedByScore = indicesSortedByScore.filter((_, j) => outputs[j] <= iouThreshold);
-  }
-  return pick;
-}
-
-// build/src/ops/normalize.js
-import {
-  concat,
-  fill,
-  sub,
-  tidy
-} from "@tensorflow/tfjs";
-function normalize(x, meanRgb) {
-  return 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);
-  });
-}
-
-// 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 tidy2(() => {
-    const [height, width] = imgTensor.shape.slice(1);
-    if (height === width) {
-      return imgTensor;
-    }
-    const dimDiff = Math.abs(height - width);
-    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
-    const paddingAxis = height > width ? 2 : 1;
-    const createPaddingTensor = (paddingAmount2) => {
-      const paddingTensorShape = imgTensor.shape.slice();
-      paddingTensorShape[paddingAxis] = paddingAmount2;
-      return fill2(paddingTensorShape, 0);
-    };
-    const paddingTensorAppend = createPaddingTensor(paddingAmount);
-    const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
-    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;
-    const tensorsToStack = [
-      paddingTensorPrepend,
-      imgTensor,
-      paddingTensorAppend
-    ].filter((t) => !!t).map((t) => cast(t, "float32"));
-    return concat2(tensorsToStack, paddingAxis);
-  });
-}
-
-// build/src/ops/shuffleArray.js
-function shuffleArray(inputArray) {
-  const array = inputArray.slice();
-  for (let i = array.length - 1; i > 0; i--) {
-    const j = Math.floor(Math.random() * (i + 1));
-    const x = array[i];
-    array[i] = array[j];
-    array[j] = x;
-  }
-  return array;
-}
-
-// build/src/ops/index.js
-function sigmoid(x) {
-  return 1 / (1 + Math.exp(-x));
-}
-function inverseSigmoid(x) {
-  return Math.log(x / (1 - x));
-}
-
-// build/src/classes/Rect.js
-class Rect extends Box {
-  constructor(x, y, width, height, allowNegativeDimensions = false) {
-    super({x, y, width, height}, allowNegativeDimensions);
-  }
-}
-
-// build/src/classes/FaceLandmarks.js
-const relX = 0.5;
-const relY = 0.43;
-const relScale = 0.45;
-class FaceLandmarks {
-  constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) {
-    const {width, height} = imgDims;
-    this._imgDims = new Dimensions(width, height);
-    this._shift = shift;
-    this._positions = relativeFaceLandmarkPositions.map((pt) => pt.mul(new Point(width, height)).add(shift));
-  }
-  get shift() {
-    return new Point(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((pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)));
-  }
-  forSize(width, height) {
-    return new this.constructor(this.relativePositions, {width, height});
-  }
-  shiftBy(x, y) {
-    return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
-  }
-  shiftByPoint(pt) {
-    return this.shiftBy(pt.x, pt.y);
-  }
-  align(detection, options = {}) {
-    if (detection) {
-      const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection);
-      return this.shiftBy(box.x, box.y).align(null, options);
-    }
-    const {useDlibAlignment, minBoxPadding} = Object.assign({}, {useDlibAlignment: false, minBoxPadding: 0.2}, options);
-    if (useDlibAlignment) {
-      return this.alignDlib();
-    }
-    return this.alignMinBbox(minBoxPadding);
-  }
-  alignDlib() {
-    const centers = this.getRefPointsForAlignment();
-    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;
-    const distToMouth = (pt) => mouthCenter.sub(pt).magnitude();
-    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
-    const size = Math.floor(eyeToMouthDist / relScale);
-    const refPoint = getCenterPoint(centers);
-    const x = Math.floor(Math.max(0, refPoint.x - relX * size));
-    const y = Math.floor(Math.max(0, refPoint.y - relY * size));
-    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
-  }
-  alignMinBbox(padding) {
-    const box = minBbox(this.positions);
-    return box.pad(box.width * padding, box.height * padding);
-  }
-  getRefPointsForAlignment() {
-    throw new Error("getRefPointsForAlignment not implemented by base class");
-  }
-}
-
-// build/src/classes/FaceLandmarks5.js
-class FaceLandmarks5 extends FaceLandmarks {
-  getRefPointsForAlignment() {
-    const pts = this.positions;
-    return [
-      pts[0],
-      pts[1],
-      getCenterPoint([pts[3], pts[4]])
-    ];
-  }
-}
-
-// build/src/classes/FaceLandmarks68.js
-class FaceLandmarks68 extends FaceLandmarks {
-  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(getCenterPoint);
-  }
-}
-
-// build/src/classes/FaceMatch.js
-class FaceMatch {
-  constructor(label, distance) {
-    this._label = label;
-    this._distance = distance;
-  }
-  get label() {
-    return this._label;
-  }
-  get distance() {
-    return this._distance;
-  }
-  toString(withDistance = true) {
-    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`;
-  }
-}
-
-// build/src/classes/LabeledBox.js
-class LabeledBox extends Box {
-  constructor(box, label) {
-    super(box);
-    this._label = label;
-  }
-  static assertIsValidLabeledBox(box, callee) {
-    Box.assertIsValidBox(box, callee);
-    if (!isValidNumber(box.label)) {
-      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);
-    }
-  }
-  get label() {
-    return this._label;
-  }
-}
-
-// build/src/classes/LabeledFaceDescriptors.js
-class LabeledFaceDescriptors {
-  constructor(label, descriptors) {
-    if (!(typeof label === "string")) {
-      throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");
-    }
-    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {
-      throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");
-    }
-    this._label = label;
-    this._descriptors = descriptors;
-  }
-  get label() {
-    return this._label;
-  }
-  get descriptors() {
-    return this._descriptors;
-  }
-  toJSON() {
-    return {
-      label: this.label,
-      descriptors: this.descriptors.map((d) => Array.from(d))
-    };
-  }
-  static fromJSON(json) {
-    const descriptors = json.descriptors.map((d) => {
-      return new Float32Array(d);
-    });
-    return new LabeledFaceDescriptors(json.label, descriptors);
-  }
-}
-
-// build/src/classes/PredictedBox.js
-class PredictedBox extends LabeledBox {
-  constructor(box, label, score, classScore) {
-    super(box, label);
-    this._score = score;
-    this._classScore = classScore;
-  }
-  static assertIsValidPredictedBox(box, callee) {
-    LabeledBox.assertIsValidLabeledBox(box, callee);
-    if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) {
-      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);
-    }
-  }
-  get score() {
-    return this._score;
-  }
-  get classScore() {
-    return this._classScore;
-  }
-}
-
-// build/src/classes/index.js
-
-// build/src/factories/WithFaceDetection.js
-function isWithFaceDetection(obj) {
-  return obj["detection"] instanceof FaceDetection;
-}
-function extendWithFaceDetection(sourceObj, detection) {
-  const extension = {detection};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/env/createBrowserEnv.js
-function createBrowserEnv() {
-  const fetch = window["fetch"] || function() {
-    throw new Error("fetch - missing fetch implementation for browser environment");
-  };
-  const readFile = 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,
-    readFile
-  };
-}
-
-// build/src/env/createFileSystem.js
-function createFileSystem(fs) {
-  let requireFsError = "";
-  if (!fs) {
-    try {
-      fs = require("fs");
-    } catch (err) {
-      requireFsError = err.toString();
-    }
-  }
-  const readFile = fs ? function(filePath) {
-    return new Promise((res, rej) => {
-      fs.readFile(filePath, function(err, buffer) {
-        return err ? rej(err) : res(buffer);
-      });
-    });
-  } : function() {
-    throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);
-  };
-  return {
-    readFile
-  };
-}
-
-// build/src/env/createNodejsEnv.js
-function createNodejsEnv() {
-  const Canvas = global["Canvas"] || global["HTMLCanvasElement"];
-  const Image = global["Image"] || global["HTMLImageElement"];
-  const createCanvasElement = function() {
-    if (Canvas) {
-      return new Canvas();
-    }
-    throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment");
-  };
-  const createImageElement = function() {
-    if (Image) {
-      return new Image();
-    }
-    throw new Error("createImageElement - missing Image implementation for nodejs environment");
-  };
-  const fetch = global["fetch"] || function() {
-    throw new Error("fetch - missing fetch implementation for nodejs environment");
-  };
-  const fileSystem = createFileSystem();
-  return {
-    Canvas: Canvas || class {
-    },
-    CanvasRenderingContext2D: global["CanvasRenderingContext2D"] || class {
-    },
-    Image: Image || class {
-    },
-    ImageData: global["ImageData"] || class {
-    },
-    Video: global["HTMLVideoElement"] || class {
-    },
-    createCanvasElement,
-    createImageElement,
-    fetch,
-    ...fileSystem
-  };
-}
-
-// build/src/env/isBrowser.js
-function isBrowser() {
-  return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
-}
-
-// build/src/env/types.js
-
-// build/src/env/index.js
-const isNodejs = __toModule(require_isNodejs());
-let environment;
-function getEnv() {
-  if (!environment) {
-    throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
-  }
-  return environment;
-}
-function setEnv(env16) {
-  environment = env16;
-}
-function initialize() {
-  if (isBrowser()) {
-    return setEnv(createBrowserEnv());
-  }
-  if (isNodejs.isNodejs()) {
-    return setEnv(createNodejsEnv());
-  }
-}
-function monkeyPatch(env16) {
-  if (!environment) {
-    initialize();
-  }
-  if (!environment) {
-    throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
-  }
-  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 env = {
-  getEnv,
-  setEnv,
-  initialize,
-  createBrowserEnv,
-  createFileSystem,
-  createNodejsEnv,
-  monkeyPatch,
-  isBrowser,
-  isNodejs: isNodejs.isNodejs
-};
-initialize();
-
-// build/src/dom/resolveInput.js
-function resolveInput(arg) {
-  if (!env.isNodejs() && typeof arg === "string") {
-    return document.getElementById(arg);
-  }
-  return arg;
-}
-
-// build/src/dom/getContext2dOrThrow.js
-function getContext2dOrThrow(canvasArg) {
-  const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv();
-  if (canvasArg instanceof CanvasRenderingContext2D2) {
-    return canvasArg;
-  }
-  const canvas = resolveInput(canvasArg);
-  if (!(canvas instanceof Canvas)) {
-    throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");
-  }
-  const ctx = canvas.getContext("2d");
-  if (!ctx) {
-    throw new Error("resolveContext2d - canvas 2d context is null");
-  }
-  return ctx;
-}
-
-// build/src/draw/DrawTextField.js
-var AnchorPosition;
-(function(AnchorPosition2) {
-  AnchorPosition2["TOP_LEFT"] = "TOP_LEFT";
-  AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT";
-  AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT";
-  AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
-})(AnchorPosition || (AnchorPosition = {}));
-class DrawTextFieldOptions {
-  constructor(options = {}) {
-    const {anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding} = options;
-    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;
-    this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)";
-    this.fontColor = fontColor || "rgba(255, 255, 255, 1)";
-    this.fontSize = fontSize || 14;
-    this.fontStyle = fontStyle || "Georgia";
-    this.padding = padding || 4;
-  }
-}
-class DrawTextField {
-  constructor(text, anchor, options = {}) {
-    this.text = typeof text === "string" ? [text] : text instanceof DrawTextField ? text.text : text;
-    this.anchor = anchor;
-    this.options = new DrawTextFieldOptions(options);
-  }
-  measureWidth(ctx) {
-    const {padding} = this.options;
-    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding;
-  }
-  measureHeight() {
-    const {fontSize, padding} = this.options;
-    return this.text.length * fontSize + 2 * padding;
-  }
-  getUpperLeft(ctx, canvasDims) {
-    const {anchorPosition} = this.options;
-    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;
-    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;
-    const textFieldWidth = this.measureWidth(ctx);
-    const textFieldHeight = this.measureHeight();
-    const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x;
-    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;
-    if (canvasDims) {
-      const {width, height} = canvasDims;
-      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);
-      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);
-      return {x: newX, y: newY};
-    }
-    return {x, y};
-  }
-  draw(canvasArg) {
-    const canvas = resolveInput(canvasArg);
-    const ctx = getContext2dOrThrow(canvas);
-    const {backgroundColor, fontColor, fontSize, fontStyle, padding} = this.options;
-    ctx.font = `${fontSize}px ${fontStyle}`;
-    const maxTextWidth = this.measureWidth(ctx);
-    const textHeight = this.measureHeight();
-    ctx.fillStyle = backgroundColor;
-    const upperLeft = this.getUpperLeft(ctx, canvas);
-    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);
-    ctx.fillStyle = fontColor;
-    this.text.forEach((textLine, i) => {
-      const x = padding + upperLeft.x;
-      const y = padding + upperLeft.y + (i + 1) * fontSize;
-      ctx.fillText(textLine, x, y);
-    });
-  }
-}
-
-// build/src/draw/DrawBox.js
-class DrawBoxOptions {
-  constructor(options = {}) {
-    const {boxColor, lineWidth, label, drawLabelOptions} = options;
-    this.boxColor = boxColor || "rgba(0, 0, 255, 1)";
-    this.lineWidth = lineWidth || 2;
-    this.label = label;
-    const defaultDrawLabelOptions = {
-      anchorPosition: AnchorPosition.BOTTOM_LEFT,
-      backgroundColor: this.boxColor
-    };
-    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions));
-  }
-}
-class DrawBox {
-  constructor(box, options = {}) {
-    this.box = new Box(box);
-    this.options = new DrawBoxOptions(options);
-  }
-  draw(canvasArg) {
-    const ctx = getContext2dOrThrow(canvasArg);
-    const {boxColor, lineWidth} = this.options;
-    const {x, y, width, height} = this.box;
-    ctx.strokeStyle = boxColor;
-    ctx.lineWidth = lineWidth;
-    ctx.strokeRect(x, y, width, height);
-    const {label} = this.options;
-    if (label) {
-      new DrawTextField([label], {x: x - lineWidth / 2, y}, this.options.drawLabelOptions).draw(canvasArg);
-    }
-  }
-}
-
-// build/src/draw/drawDetections.js
-function drawDetections(canvasArg, detections) {
-  const detectionsArray = Array.isArray(detections) ? detections : [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 ? `${round(score)}` : void 0;
-    new DrawBox(box, {label}).draw(canvasArg);
-  });
-}
-
-// build/src/dom/isMediaLoaded.js
-function isMediaLoaded(media) {
-  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 env.getEnv().Canvas || isMediaLoaded(media)) {
-      return resolve(null);
-    }
-    function onLoad(e) {
-      if (!e.currentTarget)
-        return;
-      e.currentTarget.removeEventListener("load", onLoad);
-      e.currentTarget.removeEventListener("error", onError);
-      resolve(e);
-    }
-    function onError(e) {
-      if (!e.currentTarget)
-        return;
-      e.currentTarget.removeEventListener("load", onLoad);
-      e.currentTarget.removeEventListener("error", onError);
-      reject(e);
-    }
-    media.addEventListener("load", onLoad);
-    media.addEventListener("error", onError);
-  });
-}
-
-// build/src/dom/bufferToImage.js
-function bufferToImage(buf) {
-  return new Promise((resolve, reject) => {
-    if (!(buf instanceof Blob)) {
-      return reject("bufferToImage - expected buf to be of type: Blob");
-    }
-    const reader = new FileReader();
-    reader.onload = () => {
-      if (typeof reader.result !== "string") {
-        return reject("bufferToImage - expected reader.result to be a string, in onload");
-      }
-      const img = env.getEnv().createImageElement();
-      img.onload = () => resolve(img);
-      img.onerror = reject;
-      img.src = reader.result;
-    };
-    reader.onerror = reject;
-    reader.readAsDataURL(buf);
-  });
-}
-
-// build/src/dom/getMediaDimensions.js
-function getMediaDimensions(input) {
-  const {Image, Video} = env.getEnv();
-  if (input instanceof Image) {
-    return new Dimensions(input.naturalWidth, input.naturalHeight);
-  }
-  if (input instanceof Video) {
-    return new Dimensions(input.videoWidth, input.videoHeight);
-  }
-  return new Dimensions(input.width, input.height);
-}
-
-// build/src/dom/createCanvas.js
-function createCanvas({width, height}) {
-  const {createCanvasElement} = env.getEnv();
-  const canvas = createCanvasElement();
-  canvas.width = width;
-  canvas.height = height;
-  return canvas;
-}
-function createCanvasFromMedia(media, dims) {
-  const {ImageData: ImageData2} = env.getEnv();
-  if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
-    throw new Error("createCanvasFromMedia - media has not finished loading yet");
-  }
-  const {width, height} = dims || getMediaDimensions(media);
-  const canvas = createCanvas({width, height});
-  if (media instanceof ImageData2) {
-    getContext2dOrThrow(canvas).putImageData(media, 0, 0);
-  } else {
-    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-  }
-  return canvas;
-}
-
-// build/src/dom/imageTensorToCanvas.js
-import {
-  browser,
-  tidy as tidy3
-} from "@tensorflow/tfjs";
-async function imageTensorToCanvas(imgTensor, canvas) {
-  const targetCanvas = canvas || env.getEnv().createCanvasElement();
-  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-  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} = 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} = env.getEnv();
-  if (!(input instanceof Image || input instanceof Canvas)) {
-    throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
-  }
-  const dims = getMediaDimensions(input);
-  const scale2 = inputSize / Math.max(dims.height, dims.width);
-  const width = scale2 * dims.width;
-  const height = scale2 * dims.height;
-  const targetCanvas = createCanvas({width: inputSize, height: inputSize});
-  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
-  const offset = Math.abs(width - height) / 2;
-  const dx = centerImage && width < height ? offset : 0;
-  const dy = centerImage && height < width ? offset : 0;
-  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
-  return targetCanvas;
-}
-
-// 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 = [];
-    this._canvases = [];
-    this._treatAsBatchInput = false;
-    this._inputDimensions = [];
-    if (!Array.isArray(inputs)) {
-      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
-    }
-    this._treatAsBatchInput = treatAsBatchInput;
-    this._batchSize = inputs.length;
-    inputs.forEach((input, idx) => {
-      if (isTensor3D(input)) {
-        this._imageTensors[idx] = input;
-        this._inputDimensions[idx] = input.shape;
-        return;
-      }
-      if (isTensor4D(input)) {
-        const batchSize = input.shape[0];
-        if (batchSize !== 1) {
-          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-        }
-        this._imageTensors[idx] = input;
-        this._inputDimensions[idx] = input.shape.slice(1);
-        return;
-      }
-      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
-      this._canvases[idx] = canvas;
-      this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
-  }
-  getInput(batchIdx) {
-    return this.canvases[batchIdx] || this.imageTensors[batchIdx];
-  }
-  getInputDimensions(batchIdx) {
-    return this._inputDimensions[batchIdx];
-  }
-  getInputHeight(batchIdx) {
-    return this._inputDimensions[batchIdx][0];
-  }
-  getInputWidth(batchIdx) {
-    return this._inputDimensions[batchIdx][1];
-  }
-  getReshapedInputDimensions(batchIdx) {
-    if (typeof this.inputSize !== "number") {
-      throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");
-    }
-    const width = this.getInputWidth(batchIdx);
-    const height = this.getInputHeight(batchIdx);
-    return computeReshapedDimensions({width, height}, this.inputSize);
-  }
-  toBatchTensor(inputSize, isCenterInputs = true) {
-    this._inputSize = inputSize;
-    return tidy4(() => {
-      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {
-        const input = this.getInput(batchIdx);
-        if (input instanceof Tensor2) {
-          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]);
-          }
-          return imgTensor.as3D(inputSize, inputSize, 3);
-        }
-        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) => cast2(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
-      return batchTensor;
-    });
-  }
-}
-
-// build/src/dom/toNetInput.js
-async function toNetInput(inputs) {
-  if (inputs instanceof NetInput) {
-    return inputs;
-  }
-  let inputArgArray = Array.isArray(inputs) ? inputs : [inputs];
-  if (!inputArgArray.length) {
-    throw new Error("toNetInput - empty array passed as input");
-  }
-  const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : "";
-  const inputArray = inputArgArray.map(resolveInput);
-  inputArray.forEach((input, i) => {
-    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
-      if (typeof inputArgArray[i] === "string") {
-        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);
-      }
-      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);
-    }
-    if (isTensor4D(input)) {
-      const batchSize = input.shape[0];
-      if (batchSize !== 1) {
-        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
-      }
-    }
-  });
-  await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));
-  return new NetInput(inputArray, Array.isArray(inputs));
-}
-
-// build/src/dom/extractFaces.js
-async function extractFaces(input, detections) {
-  const {Canvas} = env.getEnv();
-  let canvas = input;
-  if (!(input instanceof Canvas)) {
-    const netInput = await toNetInput(input);
-    if (netInput.batchSize > 1) {
-      throw new Error("extractFaces - batchSize > 1 not supported");
-    }
-    const tensorOrCanvas = netInput.getInput(0);
-    canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);
-  }
-  const ctx = getContext2dOrThrow(canvas);
-  const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
-  return boxes.map(({x, y, width, height}) => {
-    const faceImg = createCanvas({width, height});
-    getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
-    return faceImg;
-  });
-}
-
-// 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");
-  }
-  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
-    throw new Error("extractFaceTensors - batchSize > 1 not supported");
-  }
-  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]));
-    return faceTensors;
-  });
-}
-
-// build/src/dom/fetchOrThrow.js
-async function fetchOrThrow(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}`);
-  }
-  return res;
-}
-
-// build/src/dom/fetchImage.js
-async function fetchImage(uri) {
-  const res = await fetchOrThrow(uri);
-  const blob = await res.blob();
-  if (!blob.type.startsWith("image/")) {
-    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);
-  }
-  return bufferToImage(blob);
-}
-
-// build/src/dom/fetchJson.js
-async function fetchJson(uri) {
-  return (await fetchOrThrow(uri)).json();
-}
-
-// build/src/dom/fetchNetWeights.js
-async function fetchNetWeights(uri) {
-  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-}
-
-// build/src/common/getModelUris.js
-function getModelUris(uri, defaultModelName) {
-  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;
-  if (!uri) {
-    return {
-      modelBaseUri: "",
-      manifestUri: defaultManifestFilename
-    };
-  }
-  if (uri === "/") {
-    return {
-      modelBaseUri: "/",
-      manifestUri: `/${defaultManifestFilename}`
-    };
-  }
-  const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : "";
-  uri = uri.replace(protocol, "");
-  const parts = uri.split("/").filter((s) => s);
-  const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename;
-  let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/");
-  modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri;
-  return {
-    modelBaseUri,
-    manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`
-  };
-}
-
-// 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.loadWeights(manifest, modelBaseUri);
-}
-
-// build/src/dom/matchDimensions.js
-function matchDimensions(input, reference, useMediaDimensions = false) {
-  const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
-  input.width = width;
-  input.height = height;
-  return {width, height};
-}
-
-// build/src/dom/types.js
-
-// 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;
-    this._params = void 0;
-    this._paramMappings = [];
-  }
-  get params() {
-    return this._params;
-  }
-  get paramMappings() {
-    return this._paramMappings;
-  }
-  get isLoaded() {
-    return !!this.params;
-  }
-  getParamFromPath(paramPath) {
-    const {obj, objProp} = this.traversePropertyPath(paramPath);
-    return obj[objProp];
-  }
-  reassignParamFromPath(paramPath, tensor2) {
-    const {obj, objProp} = this.traversePropertyPath(paramPath);
-    obj[objProp].dispose();
-    obj[objProp] = tensor2;
-  }
-  getParamList() {
-    return this._paramMappings.map(({paramPath}) => ({
-      path: paramPath,
-      tensor: this.getParamFromPath(paramPath)
-    }));
-  }
-  getTrainableParams() {
-    return this.getParamList().filter((param) => param.tensor instanceof Variable);
-  }
-  getFrozenParams() {
-    return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
-  }
-  variable() {
-    this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
-      this.reassignParamFromPath(path, tensor2.variable());
-    });
-  }
-  freeze() {
-    this.getTrainableParams().forEach(({path, tensor: variable}) => {
-      const tensor2 = tensor(variable.dataSync());
-      variable.dispose();
-      this.reassignParamFromPath(path, tensor2);
-    });
-  }
-  dispose(throwOnRedispose = true) {
-    this.getParamList().forEach((param) => {
-      if (throwOnRedispose && param.tensor.isDisposed) {
-        throw new Error(`param tensor has already been disposed for path ${param.path}`);
-      }
-      param.tensor.dispose();
-    });
-    this._params = void 0;
-  }
-  serializeParams() {
-    return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
-  }
-  async load(weightsOrUrl) {
-    if (weightsOrUrl instanceof Float32Array) {
-      this.extractWeights(weightsOrUrl);
-      return;
-    }
-    await this.loadFromUri(weightsOrUrl);
-  }
-  async loadFromUri(uri) {
-    if (uri && typeof uri !== "string") {
-      throw new Error(`${this._name}.loadFromUri - expected model uri`);
-    }
-    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());
-    this.loadFromWeightMap(weightMap);
-  }
-  async loadFromDisk(filePath) {
-    if (filePath && typeof filePath !== "string") {
-      throw new Error(`${this._name}.loadFromDisk - expected model file path`);
-    }
-    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 loadWeights = io2.weightsLoaderFactory(fetchWeightsFromDisk);
-    const manifest = JSON.parse((await readFile(manifestUri)).toString());
-    const weightMap = await loadWeights(manifest, modelBaseUri);
-    this.loadFromWeightMap(weightMap);
-  }
-  loadFromWeightMap(weightMap) {
-    const {paramMappings, params} = this.extractParamsFromWeigthMap(weightMap);
-    this._paramMappings = paramMappings;
-    this._params = params;
-  }
-  extractWeights(weights) {
-    const {paramMappings, params} = this.extractParams(weights);
-    this._paramMappings = paramMappings;
-    this._params = params;
-  }
-  traversePropertyPath(paramPath) {
-    if (!this.params) {
-      throw new Error(`traversePropertyPath - model has no loaded params`);
-    }
-    const result = paramPath.split("/").reduce((res, objProp2) => {
-      if (!res.nextObj.hasOwnProperty(objProp2)) {
-        throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);
-      }
-      return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
-    }, {nextObj: this.params});
-    const {obj, objProp} = result;
-    if (!obj || !objProp || !(obj[objProp] instanceof Tensor3)) {
-      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
-    }
-    return {obj, objProp};
-  }
-}
-
-// build/src/common/depthwiseSeparableConv.js
-import {
-  add,
-  separableConv2d,
-  tidy as tidy6
-} from "@tensorflow/tfjs";
-function depthwiseSeparableConv(x, params, stride) {
-  return tidy6(() => {
-    let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
-    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 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));
-    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    return relu(add2(out1, add2(out2, out3)));
-  });
-}
-function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-  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));
-    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-    const in4 = relu(add2(out1, add2(out2, out3)));
-    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-    return relu(add2(out1, add2(out2, add2(out3, out4))));
-  });
-}
-
-// 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 tidy8(() => {
-    const out = add3(conv2d2(x, params.filters, [1, 1], padding), params.bias);
-    return withRelu ? relu2(out) : out;
-  });
-}
-
-// build/src/common/disposeUnusedWeightTensors.js
-function disposeUnusedWeightTensors(weightMap, paramMappings) {
-  Object.keys(weightMap).forEach((path) => {
-    if (!paramMappings.some((pm) => pm.originalPath === path)) {
-      weightMap[path].dispose();
-    }
-  });
-}
-
-// 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]);
-    const bias = tensor1d(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  };
-}
-
-// build/src/common/extractFCParamsFactory.js
-import {
-  tensor1d as tensor1d2,
-  tensor2d
-} from "@tensorflow/tfjs";
-function extractFCParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tensor1d2(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
-    return {
-      weights: fc_weights,
-      bias: fc_bias
-    };
-  };
-}
-
-// build/src/common/types.js
-class SeparableConvParams {
-  constructor(depthwise_filter, pointwise_filter, bias) {
-    this.depthwise_filter = depthwise_filter;
-    this.pointwise_filter = pointwise_filter;
-    this.bias = bias;
-  }
-}
-
-// build/src/common/extractSeparableConvParamsFactory.js
-import {
-  tensor1d as tensor1d3,
-  tensor4d as tensor4d2
-} from "@tensorflow/tfjs";
-function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tensor4d2(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tensor4d2(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tensor1d3(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-function loadSeparableConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);
-    const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-
-// build/src/common/extractWeightEntryFactory.js
-function extractWeightEntryFactory(weightMap, paramMappings) {
-  return function(originalPath, paramRank, mappedPath) {
-    const tensor2 = weightMap[originalPath];
-    if (!isTensor(tensor2, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
-    }
-    paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor2;
-  };
-}
-
-// build/src/common/extractWeightsFactory.js
-function extractWeightsFactory(weights) {
-  let remainingWeights = weights;
-  function extractWeights(numWeights) {
-    const ret = remainingWeights.slice(0, numWeights);
-    remainingWeights = remainingWeights.slice(numWeights);
-    return ret;
-  }
-  function getRemainingWeights() {
-    return remainingWeights;
-  }
-  return {
-    extractWeights,
-    getRemainingWeights
-  };
-}
-
-// build/src/common/index.js
-
-// build/src/faceFeatureExtractor/extractorsFactory.js
-function extractorsFactory(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);
-    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);
-    const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);
-    return {conv0, conv1, conv2: conv22};
-  }
-  function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const {conv0, conv1, conv2: conv22} = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);
-    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);
-    return {conv0, conv1, conv2: conv22, conv3};
-  }
-  return {
-    extractDenseBlock3Params,
-    extractDenseBlock4Params
-  };
-}
-
-// build/src/faceFeatureExtractor/extractParams.js
-function extractParams(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractDenseBlock4Params} = extractorsFactory(extractWeights, paramMappings);
-  const dense0 = extractDenseBlock4Params(3, 32, "dense0", true);
-  const dense1 = extractDenseBlock4Params(32, 64, "dense1");
-  const dense2 = extractDenseBlock4Params(64, 128, "dense2");
-  const dense3 = extractDenseBlock4Params(128, 256, "dense3");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2, dense3}
-  };
-}
-
-// build/src/common/loadConvParamsFactory.js
-function loadConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  };
-}
-
-// build/src/faceFeatureExtractor/loadParamsFactory.js
-function loadParamsFactory(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractDenseBlock3Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-    const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-    return {conv0, conv1, conv2: conv22};
-  }
-  function extractDenseBlock4Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
-    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-    const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
-    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);
-    return {conv0, conv1, conv2: conv22, conv3};
-  }
-  return {
-    extractDenseBlock3Params,
-    extractDenseBlock4Params
-  };
-}
-
-// build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap(weightMap) {
-  const paramMappings = [];
-  const {extractDenseBlock4Params} = loadParamsFactory(weightMap, paramMappings);
-  const params = {
-    dense0: extractDenseBlock4Params("dense0", true),
-    dense1: extractDenseBlock4Params("dense1"),
-    dense2: extractDenseBlock4Params("dense2"),
-    dense3: extractDenseBlock4Params("dense3")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceFeatureExtractor/FaceFeatureExtractor.js
-import {
-  avgPool,
-  scalar,
-  tidy as tidy9
-} from "@tensorflow/tfjs";
-class FaceFeatureExtractor extends NeuralNetwork {
-  constructor() {
-    super("FaceFeatureExtractor");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceFeatureExtractor - load model before inference");
-    }
-    return tidy9(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(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");
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "face_feature_extractor_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams(weights);
-  }
-}
-
-// build/src/common/fullyConnectedLayer.js
-import {
-  add as add4,
-  matMul,
-  tidy as tidy10
-} from "@tensorflow/tfjs";
-function fullyConnectedLayer(x, params) {
-  return tidy10(() => add4(matMul(x, params.weights), params.bias));
-}
-
-// build/src/faceProcessor/extractParams.js
-function extractParams3(weights, channelsIn, channelsOut) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-  const fc = extractFCParams(channelsIn, channelsOut, "fc");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc}
-  };
-}
-
-// build/src/faceProcessor/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap3(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: extractFcParams("fc")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceProcessor/util.js
-function seperateWeightMaps(weightMap) {
-  const featureExtractorMap = {};
-  const classifierMap = {};
-  Object.keys(weightMap).forEach((key) => {
-    const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
-    map[key] = weightMap[key];
-  });
-  return {featureExtractorMap, classifierMap};
-}
-
-// build/src/faceProcessor/FaceProcessor.js
-import {
-  tidy as tidy11
-} from "@tensorflow/tfjs";
-class FaceProcessor extends NeuralNetwork {
-  constructor(_name, faceFeatureExtractor) {
-    super(_name);
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${this._name} - load model before inference`);
-    }
-    return tidy11(() => {
-      const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
-    });
-  }
-  dispose(throwOnRedispose = true) {
-    this.faceFeatureExtractor.dispose(throwOnRedispose);
-    super.dispose(throwOnRedispose);
-  }
-  loadClassifierParams(weights) {
-    const {params, paramMappings} = this.extractClassifierParams(weights);
-    this._params = params;
-    this._paramMappings = paramMappings;
-  }
-  extractClassifierParams(weights) {
-    return extractParams3(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-    return extractParamsFromWeigthMap3(classifierMap);
-  }
-  extractParams(weights) {
-    const cIn = this.getClassifierChannelsIn();
-    const cOut = this.getClassifierChannelsOut();
-    const classifierWeightSize = cOut * cIn + cOut;
-    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-    const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-    return this.extractClassifierParams(classifierWeights);
-  }
-}
-
-// build/src/faceExpressionNet/FaceExpressions.js
-const FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
-class FaceExpressions {
-  constructor(probabilities) {
-    if (probabilities.length !== 7) {
-      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);
-    }
-    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {
-      this[expression] = probabilities[idx];
-    });
-  }
-  asSortedArray() {
-    return FACE_EXPRESSION_LABELS.map((expression) => ({expression, probability: this[expression]})).sort((e0, e1) => e1.probability - e0.probability);
-  }
-}
-
-// 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 tidy12(() => softmax(this.runNet(input)));
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  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 data = await t.data();
-      t.dispose();
-      return data;
-    }));
-    out.dispose();
-    const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites));
-    return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-  }
-  getDefaultModelName() {
-    return "face_expression_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-  getClassifierChannelsOut() {
-    return 7;
-  }
-}
-
-// build/src/faceExpressionNet/index.js
-
-// build/src/factories/WithFaceExpressions.js
-function isWithFaceExpressions(obj) {
-  return obj["expressions"] instanceof FaceExpressions;
-}
-function extendWithFaceExpressions(sourceObj, expressions) {
-  const extension = {expressions};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/draw/drawFaceExpressions.js
-function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) {
-  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
-  faceExpressionsArray.forEach((e) => {
-    const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0;
-    if (!expr) {
-      throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");
-    }
-    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} (${round(expr2.probability)})`), anchor);
-    drawTextField.draw(canvasArg);
-  });
-}
-
-// build/src/factories/WithFaceLandmarks.js
-function isWithFaceLandmarks(obj) {
-  return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection;
-}
-function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
-  const {box: shift} = sourceObj.detection;
-  const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
-  const rect = landmarks.align();
-  const {imageDims} = sourceObj.detection;
-  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
-  const extension = {
-    landmarks,
-    unshiftedLandmarks,
-    alignedRect
-  };
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/draw/DrawFaceLandmarks.js
-class DrawFaceLandmarksOptions {
-  constructor(options = {}) {
-    const {drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor} = options;
-    this.drawLines = drawLines;
-    this.drawPoints = drawPoints;
-    this.lineWidth = lineWidth || 1;
-    this.pointSize = pointSize || 2;
-    this.lineColor = lineColor || "rgba(0, 255, 255, 1)";
-    this.pointColor = pointColor || "rgba(255, 0, 255, 1)";
-  }
-}
-class DrawFaceLandmarks {
-  constructor(faceLandmarks, options = {}) {
-    this.faceLandmarks = faceLandmarks;
-    this.options = new DrawFaceLandmarksOptions(options);
-  }
-  draw(canvasArg) {
-    const ctx = getContext2dOrThrow(canvasArg);
-    const {drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor} = this.options;
-    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {
-      ctx.strokeStyle = lineColor;
-      ctx.lineWidth = lineWidth;
-      drawContour(ctx, this.faceLandmarks.getJawOutline());
-      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
-      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
-      drawContour(ctx, this.faceLandmarks.getNose());
-      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
-      drawContour(ctx, this.faceLandmarks.getRightEye(), true);
-      drawContour(ctx, this.faceLandmarks.getMouth(), true);
-    }
-    if (drawPoints) {
-      ctx.strokeStyle = pointColor;
-      ctx.fillStyle = pointColor;
-      const drawPoint = (pt) => {
-        ctx.beginPath();
-        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
-        ctx.fill();
-      };
-      this.faceLandmarks.positions.forEach(drawPoint);
-    }
-  }
-}
-function drawFaceLandmarks(canvasArg, faceLandmarks) {
-  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
-  faceLandmarksArray.forEach((f) => {
-    const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0;
-    if (!landmarks) {
-      throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");
-    }
-    new DrawFaceLandmarks(landmarks).draw(canvasArg);
-  });
-}
-
-// build/src/draw/index.js
-const draw_exports = {};
-__export(draw_exports, {
-  AnchorPosition: () => AnchorPosition,
-  DrawBox: () => DrawBox,
-  DrawBoxOptions: () => DrawBoxOptions,
-  DrawFaceLandmarks: () => DrawFaceLandmarks,
-  DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions,
-  DrawTextField: () => DrawTextField,
-  DrawTextFieldOptions: () => DrawTextFieldOptions,
-  drawContour: () => drawContour,
-  drawDetections: () => drawDetections,
-  drawFaceExpressions: () => drawFaceExpressions,
-  drawFaceLandmarks: () => drawFaceLandmarks
-});
-
-// build/src/xception/extractParams.js
-function extractorsFactory3(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);
-    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);
-    return {separable_conv0, separable_conv1, expansion_conv};
-  }
-  function extractMainBlockParams(channels, mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);
-    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);
-    return {separable_conv0, separable_conv1, separable_conv2};
-  }
-  return {
-    extractConvParams,
-    extractSeparableConvParams,
-    extractReductionBlockParams,
-    extractMainBlockParams
-  };
-}
-function extractParams5(weights, numMainBlocks) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = extractorsFactory3(extractWeights, paramMappings);
-  const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in");
-  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0");
-  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1");
-  const entry_flow = {
-    conv_in: entry_flow_conv_in,
-    reduction_block_0: entry_flow_reduction_block_0,
-    reduction_block_1: entry_flow_reduction_block_1
-  };
-  const middle_flow = {};
-  range(numMainBlocks, 0, 1).forEach((idx) => {
-    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
-  });
-  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
-  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv");
-  const exit_flow = {
-    reduction_block: exit_flow_reduction_block,
-    separable_conv: exit_flow_separable_conv
-  };
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {entry_flow, middle_flow, exit_flow}
-  };
-}
-
-// build/src/xception/extractParamsFromWeigthMap.js
-function loadParamsFactory3(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractReductionBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);
-    return {separable_conv0, separable_conv1, expansion_conv};
-  }
-  function extractMainBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
-    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
-    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);
-    return {separable_conv0, separable_conv1, separable_conv2};
-  }
-  return {
-    extractConvParams,
-    extractSeparableConvParams,
-    extractReductionBlockParams,
-    extractMainBlockParams
-  };
-}
-function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
-  const paramMappings = [];
-  const {extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams} = loadParamsFactory3(weightMap, paramMappings);
-  const entry_flow_conv_in = extractConvParams("entry_flow/conv_in");
-  const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0");
-  const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1");
-  const entry_flow = {
-    conv_in: entry_flow_conv_in,
-    reduction_block_0: entry_flow_reduction_block_0,
-    reduction_block_1: entry_flow_reduction_block_1
-  };
-  const middle_flow = {};
-  range(numMainBlocks, 0, 1).forEach((idx) => {
-    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
-  });
-  const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
-  const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv");
-  const exit_flow = {
-    reduction_block: exit_flow_reduction_block,
-    separable_conv: exit_flow_separable_conv
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params: {entry_flow, middle_flow, exit_flow}, paramMappings};
-}
-
-// 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 add5(conv2d3(x, params.filters, stride, "same"), params.bias);
-}
-function reductionBlock(x, params, isActivateInput = true) {
-  let out = isActivateInput ? relu3(x) : x;
-  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-  out = depthwiseSeparableConv(relu3(out), params.separable_conv1, [1, 1]);
-  out = maxPool(out, [3, 3], [2, 2], "same");
-  out = add5(out, conv(x, params.expansion_conv, [2, 2]));
-  return out;
-}
-function mainBlock(x, params) {
-  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 {
-  constructor(numMainBlocks) {
-    super("TinyXception");
-    this._numMainBlocks = numMainBlocks;
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyXception - load model before inference");
-    }
-    return tidy13(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      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);
-      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 = relu3(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "tiny_xception_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap5(weightMap, this._numMainBlocks);
-  }
-  extractParams(weights) {
-    return extractParams5(weights, this._numMainBlocks);
-  }
-}
-
-// build/src/ageGenderNet/extractParams.js
-function extractParams7(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
-  const age = extractFCParams(512, 1, "fc/age");
-  const gender = extractFCParams(512, 2, "fc/gender");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc: {age, gender}}
-  };
-}
-
-// build/src/ageGenderNet/extractParamsFromWeigthMap.js
-function extractParamsFromWeigthMap7(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: {
-      age: extractFcParams("fc/age"),
-      gender: extractFcParams("fc/gender")
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/ageGenderNet/types.js
-var Gender;
-(function(Gender2) {
-  Gender2["FEMALE"] = "female";
-  Gender2["MALE"] = "male";
-})(Gender || (Gender = {}));
-
-// build/src/ageGenderNet/AgeGenderNet.js
-import {
-  avgPool as avgPool2,
-  softmax as softmax2,
-  tidy as tidy14,
-  unstack as unstack2
-} from "@tensorflow/tfjs";
-class AgeGenderNet extends NeuralNetwork {
-  constructor(faceFeatureExtractor = new TinyXception(2)) {
-    super("AgeGenderNet");
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${this._name} - load model before inference`);
-    }
-    return tidy14(() => {
-      const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input;
-      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 tidy14(() => {
-      const {age, gender} = this.runNet(input);
-      return {age, gender: softmax2(gender)};
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async predictAgeAndGender(input) {
-    const netInput = await toNetInput(input);
-    const out = await this.forwardInput(netInput);
-    const ages = unstack2(out.age);
-    const genders = unstack2(out.gender);
-    const ageAndGenderTensors = ages.map((ageTensor, i) => ({
-      ageTensor,
-      genderTensor: genders[i]
-    }));
-    const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ageTensor, genderTensor}) => {
-      const age = (await ageTensor.data())[0];
-      const probMale = (await genderTensor.data())[0];
-      const isMale = probMale > 0.5;
-      const gender = isMale ? Gender.MALE : Gender.FEMALE;
-      const genderProbability = isMale ? probMale : 1 - probMale;
-      ageTensor.dispose();
-      genderTensor.dispose();
-      return {age, gender, genderProbability};
-    }));
-    out.age.dispose();
-    out.gender.dispose();
-    return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
-  }
-  getDefaultModelName() {
-    return "age_gender_model";
-  }
-  dispose(throwOnRedispose = true) {
-    this.faceFeatureExtractor.dispose(throwOnRedispose);
-    super.dispose(throwOnRedispose);
-  }
-  loadClassifierParams(weights) {
-    const {params, paramMappings} = this.extractClassifierParams(weights);
-    this._params = params;
-    this._paramMappings = paramMappings;
-  }
-  extractClassifierParams(weights) {
-    return extractParams7(weights);
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
-    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-    return extractParamsFromWeigthMap7(classifierMap);
-  }
-  extractParams(weights) {
-    const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2);
-    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
-    const classifierWeights = weights.slice(weights.length - classifierWeightSize);
-    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
-    return this.extractClassifierParams(classifierWeights);
-  }
-}
-
-// 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}) => {
-      const scale2 = inputSize / Math.max(height, width);
-      return {
-        width: width * scale2,
-        height: height * scale2
-      };
-    });
-    const batchSize = inputDimensions.length;
-    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];
-        return cond(width, height) ? Math.abs(width - height) / 2 : 0;
-      };
-      const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
-      const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      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 tidy15(() => {
-      const out = this.runNet(input);
-      return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width})));
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async detectLandmarks(input) {
-    const netInput = await toNetInput(input);
-    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));
-      const yCoords = landmarksArray.filter((_, i) => !isEven(i));
-      return new FaceLandmarks68(Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), {
-        height: netInput.getInputHeight(batchIdx),
-        width: netInput.getInputWidth(batchIdx)
-      });
-    }));
-    landmarkTensors.forEach((t) => t.dispose());
-    return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0];
-  }
-  getClassifierChannelsOut() {
-    return 136;
-  }
-}
-
-// build/src/faceLandmarkNet/FaceLandmark68Net.js
-class FaceLandmark68Net extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-    super("FaceLandmark68Net", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-}
-
-// build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js
-function extractParamsFromWeigthMapTiny(weightMap) {
-  const paramMappings = [];
-  const {extractDenseBlock3Params} = loadParamsFactory(weightMap, paramMappings);
-  const params = {
-    dense0: extractDenseBlock3Params("dense0", true),
-    dense1: extractDenseBlock3Params("dense1"),
-    dense2: extractDenseBlock3Params("dense2")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/faceFeatureExtractor/extractParamsTiny.js
-function extractParamsTiny(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const {extractDenseBlock3Params} = extractorsFactory(extractWeights, paramMappings);
-  const dense0 = extractDenseBlock3Params(3, 32, "dense0", true);
-  const dense1 = extractDenseBlock3Params(32, 64, "dense1");
-  const dense2 = extractDenseBlock3Params(64, 128, "dense2");
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2}
-  };
-}
-
-// 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");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyFaceFeatureExtractor - load model before inference");
-    }
-    return tidy16(() => {
-      const batchTensor = input.toBatchTensor(112, true);
-      const meanRgb = [122.782, 117.001, 104.298];
-      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 = avgPool3(out, [14, 14], [2, 2], "valid");
-      return out;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  getDefaultModelName() {
-    return "face_feature_extractor_tiny_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMapTiny(weightMap);
-  }
-  extractParams(weights) {
-    return extractParamsTiny(weights);
-  }
-}
-
-// build/src/faceLandmarkNet/FaceLandmark68TinyNet.js
-class FaceLandmark68TinyNet extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-    super("FaceLandmark68TinyNet", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_tiny_model";
-  }
-  getClassifierChannelsIn() {
-    return 128;
-  }
-}
-
-// build/src/faceLandmarkNet/index.js
-class FaceLandmarkNet extends FaceLandmark68Net {
-}
-
-// build/src/faceRecognitionNet/scaleLayer.js
-import {
-  add as add6,
-  mul
-} from "@tensorflow/tfjs";
-function scale(x, params) {
-  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 = conv2d4(x, filters, strides, padding);
-  out = add7(out, bias);
-  out = scale(out, params.scale);
-  return withRelu ? relu4(out) : out;
-}
-function conv2(x, params) {
-  return convLayer2(x, params, [1, 1], true);
-}
-function convNoRelu(x, params) {
-  return convLayer2(x, params, [1, 1], false);
-}
-function convDown(x, params) {
-  return convLayer2(x, params, [2, 2], true, "valid");
-}
-
-// 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);
-    const depth = weights.length / (numFilters * filterSize * filterSize);
-    if (isFloat(depth)) {
-      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
-    }
-    return tidy17(() => transpose(tensor4d3(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
-  }
-  function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-    const bias = tensor1d4(extractWeights(numFilters));
-    paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  }
-  function extractScaleLayerParams(numWeights, mappedPrefix) {
-    const weights = tensor1d4(extractWeights(numWeights));
-    const biases = tensor1d4(extractWeights(numWeights));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
-    return {
-      weights,
-      biases
-    };
-  }
-  function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-    const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);
-    const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);
-    return {conv: conv3, scale: scale2};
-  }
-  function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) {
-    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);
-    const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);
-    return {conv1, conv2: conv22};
-  }
-  return {
-    extractConvLayerParams,
-    extractResidualLayerParams
-  };
-}
-function extractParams9(weights) {
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const paramMappings = [];
-  const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory5(extractWeights, paramMappings);
-  const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down");
-  const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1");
-  const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2");
-  const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3");
-  const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true);
-  const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1");
-  const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2");
-  const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3");
-  const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true);
-  const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1");
-  const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2");
-  const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true);
-  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 = 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: ${getRemainingWeights().length}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  return {params, paramMappings};
-}
-
-// build/src/faceRecognitionNet/extractParamsFromWeigthMap.js
-function extractorsFactory6(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractScaleLayerParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);
-    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);
-    return {weights, biases};
-  }
-  function extractConvLayerParams(prefix) {
-    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);
-    const scale2 = extractScaleLayerParams(prefix);
-    return {conv: {filters, bias}, scale: scale2};
-  }
-  function extractResidualLayerParams(prefix) {
-    return {
-      conv1: extractConvLayerParams(`${prefix}/conv1`),
-      conv2: extractConvLayerParams(`${prefix}/conv2`)
-    };
-  }
-  return {
-    extractConvLayerParams,
-    extractResidualLayerParams
-  };
-}
-function extractParamsFromWeigthMap9(weightMap) {
-  const paramMappings = [];
-  const {extractConvLayerParams, extractResidualLayerParams} = extractorsFactory6(weightMap, paramMappings);
-  const conv32_down = extractConvLayerParams("conv32_down");
-  const conv32_1 = extractResidualLayerParams("conv32_1");
-  const conv32_2 = extractResidualLayerParams("conv32_2");
-  const conv32_3 = extractResidualLayerParams("conv32_3");
-  const conv64_down = extractResidualLayerParams("conv64_down");
-  const conv64_1 = extractResidualLayerParams("conv64_1");
-  const conv64_2 = extractResidualLayerParams("conv64_2");
-  const conv64_3 = extractResidualLayerParams("conv64_3");
-  const conv128_down = extractResidualLayerParams("conv128_down");
-  const conv128_1 = extractResidualLayerParams("conv128_1");
-  const conv128_2 = extractResidualLayerParams("conv128_2");
-  const conv256_down = extractResidualLayerParams("conv256_down");
-  const conv256_1 = extractResidualLayerParams("conv256_1");
-  const conv256_2 = extractResidualLayerParams("conv256_2");
-  const conv256_down_out = extractResidualLayerParams("conv256_down_out");
-  const fc = weightMap["fc"];
-  paramMappings.push({originalPath: "fc", paramPath: "fc"});
-  if (!isTensor2D(fc)) {
-    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// 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 = 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 = 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 = concat3([out, zerosW], 1);
-    const padShapeY = [...out.shape];
-    padShapeY[2] = 1;
-    const zerosH = zeros(padShapeY);
-    out = concat3([out, zerosH], 2);
-  }
-  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");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceRecognitionNet - load model before inference");
-    }
-    return tidy18(() => {
-      const batchTensor = cast3(input.toBatchTensor(150, true), "float32");
-      const meanRgb = [122.782, 117.001, 104.298];
-      const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
-      let out = convDown(normalized, params.conv32_down);
-      out = maxPool2(out, 3, 2, "valid");
-      out = residual(out, params.conv32_1);
-      out = residual(out, params.conv32_2);
-      out = residual(out, params.conv32_3);
-      out = residualDown(out, params.conv64_down);
-      out = residual(out, params.conv64_1);
-      out = residual(out, params.conv64_2);
-      out = residual(out, params.conv64_3);
-      out = residualDown(out, params.conv128_down);
-      out = residual(out, params.conv128_1);
-      out = residual(out, params.conv128_2);
-      out = residualDown(out, params.conv256_down);
-      out = residual(out, params.conv256_1);
-      out = residual(out, params.conv256_2);
-      out = residualDown(out, params.conv256_down_out);
-      const globalAvg = out.mean([1, 2]);
-      const fullyConnected = matMul2(globalAvg, params.fc);
-      return fullyConnected;
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async computeFaceDescriptor(input) {
-    const netInput = await toNetInput(input);
-    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];
-  }
-  getDefaultModelName() {
-    return "face_recognition_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap9(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams9(weights);
-  }
-}
-
-// build/src/faceRecognitionNet/index.js
-function createFaceRecognitionNet(weights) {
-  const net = new FaceRecognitionNet();
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/factories/WithFaceDescriptor.js
-function extendWithFaceDescriptor(sourceObj, descriptor) {
-  const extension = {descriptor};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/factories/WithAge.js
-function isWithAge(obj) {
-  return typeof obj["age"] === "number";
-}
-function extendWithAge(sourceObj, age) {
-  const extension = {age};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// build/src/factories/WithGender.js
-function isWithGender(obj) {
-  return (obj["gender"] === Gender.MALE || obj["gender"] === Gender.FEMALE) && isValidProbablitiy(obj["genderProbability"]);
-}
-function extendWithGender(sourceObj, gender, genderProbability) {
-  const extension = {gender, genderProbability};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// 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 = 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,
-      batch_norm_scale,
-      batch_norm_offset,
-      batch_norm_mean,
-      batch_norm_variance
-    };
-  }
-  function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
-    const filters = tensor4d4(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-    const bias = tensor1d5(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
-    return {filters, bias};
-  }
-  function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
-    const {filters, bias} = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
-    return {
-      filters,
-      batch_norm_offset: bias
-    };
-  }
-  function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
-    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
-    return {depthwise_conv, pointwise_conv};
-  }
-  function extractMobilenetV1Params() {
-    const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0");
-    const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1");
-    const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2");
-    const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3");
-    const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4");
-    const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5");
-    const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6");
-    const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7");
-    const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8");
-    const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9");
-    const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10");
-    const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11");
-    const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12");
-    const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13");
-    return {
-      conv_0,
-      conv_1,
-      conv_2,
-      conv_3,
-      conv_4,
-      conv_5,
-      conv_6,
-      conv_7,
-      conv_8,
-      conv_9,
-      conv_10,
-      conv_11,
-      conv_12,
-      conv_13
-    };
-  }
-  function extractPredictionLayerParams() {
-    const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0");
-    const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1");
-    const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2");
-    const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3");
-    const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4");
-    const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5");
-    const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6");
-    const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7");
-    const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor");
-    const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor");
-    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor");
-    const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor");
-    const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor");
-    const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor");
-    const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor");
-    const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor");
-    const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor");
-    const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor");
-    const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor");
-    const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor");
-    const box_predictor_0 = {
-      box_encoding_predictor: box_encoding_0_predictor,
-      class_predictor: class_predictor_0
-    };
-    const box_predictor_1 = {
-      box_encoding_predictor: box_encoding_1_predictor,
-      class_predictor: class_predictor_1
-    };
-    const box_predictor_2 = {
-      box_encoding_predictor: box_encoding_2_predictor,
-      class_predictor: class_predictor_2
-    };
-    const box_predictor_3 = {
-      box_encoding_predictor: box_encoding_3_predictor,
-      class_predictor: class_predictor_3
-    };
-    const box_predictor_4 = {
-      box_encoding_predictor: box_encoding_4_predictor,
-      class_predictor: class_predictor_4
-    };
-    const box_predictor_5 = {
-      box_encoding_predictor: box_encoding_5_predictor,
-      class_predictor: class_predictor_5
-    };
-    return {
-      conv_0,
-      conv_1,
-      conv_2,
-      conv_3,
-      conv_4,
-      conv_5,
-      conv_6,
-      conv_7,
-      box_predictor_0,
-      box_predictor_1,
-      box_predictor_2,
-      box_predictor_3,
-      box_predictor_4,
-      box_predictor_5
-    };
-  }
-  return {
-    extractMobilenetV1Params,
-    extractPredictionLayerParams
-  };
-}
-function extractParams11(weights) {
-  const paramMappings = [];
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(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 output_layer = {
-    extra_dim
-  };
-  paramMappings.push({paramPath: "output_layer/extra_dim"});
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {
-    params: {
-      mobilenetv1,
-      prediction_layer,
-      output_layer
-    },
-    paramMappings
-  };
-}
-
-// build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js
-function extractorsFactory8(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);
-    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);
-    return {filters, batch_norm_offset};
-  }
-  function extractConvPairParams(idx) {
-    const mappedPrefix = `mobilenetv1/conv_${idx}`;
-    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;
-    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;
-    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;
-    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);
-    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);
-    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);
-    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);
-    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);
-    return {
-      depthwise_conv: {
-        filters,
-        batch_norm_scale,
-        batch_norm_offset,
-        batch_norm_mean,
-        batch_norm_variance
-      },
-      pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv)
-    };
-  }
-  function extractMobilenetV1Params() {
-    return {
-      conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"),
-      conv_1: extractConvPairParams(1),
-      conv_2: extractConvPairParams(2),
-      conv_3: extractConvPairParams(3),
-      conv_4: extractConvPairParams(4),
-      conv_5: extractConvPairParams(5),
-      conv_6: extractConvPairParams(6),
-      conv_7: extractConvPairParams(7),
-      conv_8: extractConvPairParams(8),
-      conv_9: extractConvPairParams(9),
-      conv_10: extractConvPairParams(10),
-      conv_11: extractConvPairParams(11),
-      conv_12: extractConvPairParams(12),
-      conv_13: extractConvPairParams(13)
-    };
-  }
-  function extractConvParams(prefix, mappedPrefix) {
-    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);
-    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);
-    return {filters, bias};
-  }
-  function extractBoxPredictorParams(idx) {
-    const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`);
-    const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`);
-    return {box_encoding_predictor, class_predictor};
-  }
-  function extractPredictionLayerParams() {
-    return {
-      conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"),
-      conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"),
-      conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"),
-      conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"),
-      conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"),
-      conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"),
-      conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"),
-      conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"),
-      box_predictor_0: extractBoxPredictorParams(0),
-      box_predictor_1: extractBoxPredictorParams(1),
-      box_predictor_2: extractBoxPredictorParams(2),
-      box_predictor_3: extractBoxPredictorParams(3),
-      box_predictor_4: extractBoxPredictorParams(4),
-      box_predictor_5: extractBoxPredictorParams(5)
-    };
-  }
-  return {
-    extractMobilenetV1Params,
-    extractPredictionLayerParams
-  };
-}
-function extractParamsFromWeigthMap11(weightMap) {
-  const paramMappings = [];
-  const {extractMobilenetV1Params, extractPredictionLayerParams} = extractorsFactory8(weightMap, paramMappings);
-  const extra_dim = weightMap["Output/extra_dim"];
-  paramMappings.push({originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim"});
-  if (!isTensor3D(extra_dim)) {
-    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);
-  }
-  const params = {
-    mobilenetv1: extractMobilenetV1Params(),
-    prediction_layer: extractPredictionLayerParams(),
-    output_layer: {
-      extra_dim
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// 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 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 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 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 tidy20(() => {
-    let conv11;
-    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
-    const convPairParams = [
-      params.conv_1,
-      params.conv_2,
-      params.conv_3,
-      params.conv_4,
-      params.conv_5,
-      params.conv_6,
-      params.conv_7,
-      params.conv_8,
-      params.conv_9,
-      params.conv_10,
-      params.conv_11,
-      params.conv_12,
-      params.conv_13
-    ];
-    convPairParams.forEach((param, i) => {
-      const layerIdx = i + 1;
-      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
-      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
-      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
-      if (layerIdx === 11) {
-        conv11 = out;
-      }
-    });
-    if (conv11 === null) {
-      throw new Error("mobileNetV1 - output of conv layer 11 is null");
-    }
-    return {
-      out,
-      conv11
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/nonMaxSuppression.js
-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);
-  const suppressFunc = (x) => x <= iouThreshold ? 1 : 0;
-  const selected = [];
-  candidates.forEach((c) => {
-    if (selected.length >= outputSize) {
-      return;
-    }
-    const originalScore = c.score;
-    for (let j = selected.length - 1; j >= 0; --j) {
-      const iou3 = IOU(boxes, c.boxIndex, selected[j]);
-      if (iou3 === 0) {
-        continue;
-      }
-      c.score *= suppressFunc(iou3);
-      if (c.score <= scoreThreshold) {
-        break;
-      }
-    }
-    if (originalScore === c.score) {
-      selected.push(c.boxIndex);
-    }
-  });
-  return selected;
-}
-function IOU(boxes, i, j) {
-  const boxesData = boxes.arraySync();
-  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);
-  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);
-  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
-  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
-  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
-  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
-  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
-  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][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);
-}
-
-// 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 = unstack5(transpose2(x, [1, 0]));
-  const sizes = [
-    sub2(vec[2], vec[0]),
-    sub2(vec[3], vec[1])
-  ];
-  const centers = [
-    add10(vec[0], div(sizes[0], scalar5(2))),
-    add10(vec[1], div(sizes[1], scalar5(2)))
-  ];
-  return {
-    sizes,
-    centers
-  };
-}
-function decodeBoxesLayer(x0, x1) {
-  const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
-  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 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 = 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 = unstack5(boxes);
-    const scoresByBatch = unstack5(scores);
-    return {
-      boxes: boxesByBatch,
-      scores: scoresByBatch
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/boxPredictionLayer.js
-import {
-  reshape as reshape2,
-  tidy as tidy22
-} from "@tensorflow/tfjs";
-function boxPredictionLayer(x, params) {
-  return tidy22(() => {
-    const batchSize = x.shape[0];
-    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
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/predictionLayer.js
-import {
-  concat as concat4,
-  tidy as tidy23
-} from "@tensorflow/tfjs";
-function predictionLayer(x, conv11, params) {
-  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]);
-    const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]);
-    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
-    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
-    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
-    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
-    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
-    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
-    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
-    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 = concat4([
-      boxPrediction0.boxPredictionEncoding,
-      boxPrediction1.boxPredictionEncoding,
-      boxPrediction2.boxPredictionEncoding,
-      boxPrediction3.boxPredictionEncoding,
-      boxPrediction4.boxPredictionEncoding,
-      boxPrediction5.boxPredictionEncoding
-    ], 1);
-    const classPredictions = concat4([
-      boxPrediction0.classPrediction,
-      boxPrediction1.classPrediction,
-      boxPrediction2.classPrediction,
-      boxPrediction3.classPrediction,
-      boxPrediction4.classPrediction,
-      boxPrediction5.classPrediction
-    ], 1);
-    return {
-      boxPredictions,
-      classPredictions
-    };
-  });
-}
-
-// build/src/ssdMobilenetv1/SsdMobilenetv1Options.js
-class SsdMobilenetv1Options {
-  constructor({minConfidence, maxResults} = {}) {
-    this._name = "SsdMobilenetv1Options";
-    this._minConfidence = minConfidence || 0.5;
-    this._maxResults = maxResults || 100;
-    if (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;
-  }
-}
-
-// 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");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("SsdMobilenetv1 - load model before inference");
-    }
-    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);
-    });
-  }
-  async forward(input) {
-    return this.forwardInput(await toNetInput(input));
-  }
-  async locateFaces(input, options = {}) {
-    const {maxResults, minConfidence} = new SsdMobilenetv1Options(options);
-    const netInput = await toNetInput(input);
-    const {boxes: _boxes, scores: _scores} = this.forwardInput(netInput);
-    const boxes = _boxes[0];
-    const scores = _scores[0];
-    for (let i = 1; i < _boxes.length; i++) {
-      _boxes[i].dispose();
-      _scores[i].dispose();
-    }
-    const scoresData = Array.from(await scores.data());
-    const iouThreshold = 0.5;
-    const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-    const reshapedDims = netInput.getReshapedInputDimensions(0);
-    const inputSize = netInput.inputSize;
-    const padX = inputSize / reshapedDims.width;
-    const padY = inputSize / reshapedDims.height;
-    const boxesData = boxes.arraySync();
-    const results = indices.map((idx) => {
-      const [top, bottom] = [
-        Math.max(0, boxesData[idx][0]),
-        Math.min(1, boxesData[idx][2])
-      ].map((val) => val * padY);
-      const [left, right] = [
-        Math.max(0, boxesData[idx][1]),
-        Math.min(1, boxesData[idx][3])
-      ].map((val) => val * padX);
-      return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
-        height: netInput.getInputHeight(0),
-        width: netInput.getInputWidth(0)
-      });
-    });
-    boxes.dispose();
-    scores.dispose();
-    return results;
-  }
-  getDefaultModelName() {
-    return "ssd_mobilenetv1_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap11(weightMap);
-  }
-  extractParams(weights) {
-    return extractParams11(weights);
-  }
-}
-
-// build/src/ssdMobilenetv1/index.js
-function createSsdMobilenetv1(weights) {
-  const net = new SsdMobilenetv1();
-  net.extractWeights(weights);
-  return net;
-}
-function createFaceDetectionNet(weights) {
-  return createSsdMobilenetv1(weights);
-}
-class FaceDetectionNet extends SsdMobilenetv1 {
-}
-
-// build/src/tinyYolov2/const.js
-const IOU_THRESHOLD = 0.4;
-const BOX_ANCHORS = [
-  new Point(0.738768, 0.874946),
-  new Point(2.42204, 2.65704),
-  new Point(4.30971, 7.04493),
-  new Point(10.246, 4.59428),
-  new Point(12.6868, 11.8741)
-];
-const BOX_ANCHORS_SEPARABLE = [
-  new Point(1.603231, 2.094468),
-  new Point(6.041143, 7.080126),
-  new Point(2.882459, 3.518061),
-  new Point(4.266906, 5.178857),
-  new Point(9.041765, 10.66308)
-];
-const MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
-
-// build/src/tinyYolov2/config.js
-const isNumber = (arg) => typeof arg === "number";
-function validateConfig(config2) {
-  if (!config2) {
-    throw new Error(`invalid config: ${config2}`);
-  }
-  if (typeof config2.withSeparableConvs !== "boolean") {
-    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config2.withSeparableConvs}`);
-  }
-  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) => 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(isNumber))) {
-    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`);
-  }
-}
-
-// build/src/tinyYolov2/leaky.js
-import {
-  add as add11,
-  mul as mul4,
-  relu as relu6,
-  scalar as scalar7,
-  sub as sub4,
-  tidy as tidy25
-} from "@tensorflow/tfjs";
-function leaky(x) {
-  return tidy25(() => {
-    const min = mul4(x, scalar7(0.10000000149011612));
-    return add11(relu6(sub4(x, min)), min);
-  });
-}
-
-// build/src/tinyYolov2/convWithBatchNorm.js
-import {
-  add as add12,
-  conv2d as conv2d6,
-  mul as mul5,
-  pad,
-  sub as sub5,
-  tidy as tidy26
-} from "@tensorflow/tfjs";
-function convWithBatchNorm(x, params) {
-  return tidy26(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = conv2d6(out, params.conv.filters, [1, 1], "valid");
-    out = sub5(out, params.bn.sub);
-    out = mul5(out, params.bn.truediv);
-    out = add12(out, params.conv.bias);
-    return leaky(out);
-  });
-}
-
-// build/src/tinyYolov2/depthwiseSeparableConv.js
-import {
-  add as add13,
-  pad as pad2,
-  separableConv2d as separableConv2d2,
-  tidy as tidy27
-} from "@tensorflow/tfjs";
-function depthwiseSeparableConv3(x, params) {
-  return tidy27(() => {
-    let out = pad2(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = separableConv2d2(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = add13(out, params.bias);
-    return leaky(out);
-  });
-}
-
-// build/src/tinyYolov2/extractParams.js
-import {
-  tensor1d as tensor1d6
-} from "@tensorflow/tfjs";
-function extractorsFactory9(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  function extractBatchNormParams(size, mappedPrefix) {
-    const sub6 = tensor1d6(extractWeights(size));
-    const truediv = tensor1d6(extractWeights(size));
-    paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
-    return {sub: sub6, truediv};
-  }
-  function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
-    const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
-    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);
-    return {conv: conv3, bn};
-  }
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  return {
-    extractConvParams,
-    extractConvWithBatchNormParams,
-    extractSeparableConvParams
-  };
-}
-function extractParams13(weights, config2, boxEncodingSize, filterSizes) {
-  const {extractWeights, getRemainingWeights} = extractWeightsFactory(weights);
-  const paramMappings = [];
-  const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory9(extractWeights, paramMappings);
-  let params;
-  if (config2.withSeparableConvs) {
-    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-    const conv0 = config2.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0");
-    const conv1 = extractSeparableConvParams(s1, s2, "conv1");
-    const conv22 = extractSeparableConvParams(s2, s3, "conv2");
-    const conv3 = extractSeparableConvParams(s3, s4, "conv3");
-    const conv4 = extractSeparableConvParams(s4, s5, "conv4");
-    const conv5 = extractSeparableConvParams(s5, s6, "conv5");
-    const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0;
-    const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0;
-    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8");
-    params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-  } else {
-    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-    const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0");
-    const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1");
-    const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2");
-    const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3");
-    const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4");
-    const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5");
-    const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6");
-    const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7");
-    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8");
-    params = {conv0, conv1, conv2: conv22, conv3, conv4, conv5, conv6, conv7, conv8};
-  }
-  if (getRemainingWeights().length !== 0) {
-    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-  }
-  return {params, paramMappings};
-}
-
-// build/src/tinyYolov2/extractParamsFromWeigthMap.js
-function extractorsFactory10(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractBatchNormParams(prefix) {
-    const sub6 = extractWeightEntry(`${prefix}/sub`, 1);
-    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-    return {sub: sub6, truediv};
-  }
-  function extractConvParams(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  }
-  function extractConvWithBatchNormParams(prefix) {
-    const conv3 = extractConvParams(`${prefix}/conv`);
-    const bn = extractBatchNormParams(`${prefix}/bn`);
-    return {conv: conv3, bn};
-  }
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  return {
-    extractConvParams,
-    extractConvWithBatchNormParams,
-    extractSeparableConvParams
-  };
-}
-function extractParamsFromWeigthMap13(weightMap, config2) {
-  const paramMappings = [];
-  const {extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams} = extractorsFactory10(weightMap, paramMappings);
-  let params;
-  if (config2.withSeparableConvs) {
-    const numFilters = config2.filterSizes && config2.filterSizes.length || 9;
-    params = {
-      conv0: config2.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"),
-      conv1: extractSeparableConvParams("conv1"),
-      conv2: extractSeparableConvParams("conv2"),
-      conv3: extractSeparableConvParams("conv3"),
-      conv4: extractSeparableConvParams("conv4"),
-      conv5: extractSeparableConvParams("conv5"),
-      conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0,
-      conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0,
-      conv8: extractConvParams("conv8")
-    };
-  } else {
-    params = {
-      conv0: extractConvWithBatchNormParams("conv0"),
-      conv1: extractConvWithBatchNormParams("conv1"),
-      conv2: extractConvWithBatchNormParams("conv2"),
-      conv3: extractConvWithBatchNormParams("conv3"),
-      conv4: extractConvWithBatchNormParams("conv4"),
-      conv5: extractConvWithBatchNormParams("conv5"),
-      conv6: extractConvWithBatchNormParams("conv6"),
-      conv7: extractConvWithBatchNormParams("conv7"),
-      conv8: extractConvParams("conv8")
-    };
-  }
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// build/src/tinyYolov2/TinyYolov2Options.js
-var TinyYolov2SizeType;
-(function(TinyYolov2SizeType2) {
-  TinyYolov2SizeType2[TinyYolov2SizeType2["XS"] = 224] = "XS";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["SM"] = 320] = "SM";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["MD"] = 416] = "MD";
-  TinyYolov2SizeType2[TinyYolov2SizeType2["LG"] = 608] = "LG";
-})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
-class TinyYolov2Options {
-  constructor({inputSize, scoreThreshold} = {}) {
-    this._name = "TinyYolov2Options";
-    this._inputSize = inputSize || 416;
-    this._scoreThreshold = scoreThreshold || 0.5;
-    if (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;
-  }
-}
-
-// 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");
-    validateConfig(config2);
-    this._config = config2;
-  }
-  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(x, params) {
-    let out = convWithBatchNorm(x, params.conv0);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv1);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv2);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv3);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv4);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = convWithBatchNorm(out, params.conv5);
-    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 = maxPool3(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv1);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv2);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv3);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv4);
-    out = maxPool3(out, [2, 2], [2, 2], "same");
-    out = depthwiseSeparableConv3(out, params.conv5);
-    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);
-  }
-  forwardInput(input, inputSize) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyYolov2 - load model before inference");
-    }
-    return tidy28(() => {
-      let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32");
-      batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
-      batchTensor = batchTensor.div(scalar8(256));
-      return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
-    });
-  }
-  async forward(input, inputSize) {
-    return await this.forwardInput(await toNetInput(input), inputSize);
-  }
-  async detect(input, forwardParams = {}) {
-    const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
-    const netInput = await toNetInput(input);
-    const out = await this.forwardInput(netInput, inputSize);
-    const out0 = tidy28(() => unstack6(out)[0].expandDims());
-    const inputDimensions = {
-      width: netInput.getInputWidth(0),
-      height: netInput.getInputHeight(0)
-    };
-    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
-    out.dispose();
-    out0.dispose();
-    const boxes = results.map((res) => res.box);
-    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 = 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;
-  }
-  getDefaultModelName() {
-    return "";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return extractParamsFromWeigthMap13(weightMap, this.config);
-  }
-  extractParams(weights) {
-    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;
-    const numFilters = filterSizes ? filterSizes.length : void 0;
-    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
-      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);
-    }
-    return extractParams13(weights, this.config, this.boxEncodingSize, filterSizes);
-  }
-  async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) {
-    const {width, height} = inputBlobDimensions;
-    const inputSize = Math.max(width, height);
-    const correctionFactorX = inputSize / width;
-    const correctionFactorY = inputSize / height;
-    const numCells = outputTensor.shape[1];
-    const numBoxes = this.config.anchors.length;
-    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 ? softmax3(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar8(0);
-      return [boxes, scores, classScores];
-    });
-    const results = [];
-    const scoresData = await scoresTensor.array();
-    const boxesData = await boxesTensor.array();
-    for (let row = 0; row < numCells; row++) {
-      for (let col = 0; col < numCells; col++) {
-        for (let anchor = 0; anchor < numBoxes; anchor++) {
-          const score = sigmoid(scoresData[row][col][anchor][0]);
-          if (!scoreThreshold || score > scoreThreshold) {
-            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({
-              box: new BoundingBox(x, y, x + width2, y + height2),
-              score,
-              classScore: score * classScore,
-              label,
-              ...pos
-            });
-          }
-        }
-      }
-    }
-    boxesTensor.dispose();
-    scoresTensor.dispose();
-    classScoresTensor.dispose();
-    return results;
-  }
-  async extractPredictedClass(classesTensor, pos) {
-    const {row, col, anchor} = pos;
-    const classesData = await classesTensor.array();
-    return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
-      classScore,
-      label
-    })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr);
-  }
-}
-TinyYolov2Base.DEFAULT_FILTER_SIZES = [
-  3,
-  16,
-  32,
-  64,
-  128,
-  256,
-  512,
-  1024,
-  1024
-];
-
-// build/src/tinyYolov2/TinyYolov2.js
-class TinyYolov2 extends TinyYolov2Base {
-  constructor(withSeparableConvs = true) {
-    const config2 = Object.assign({}, {
-      withSeparableConvs,
-      iouThreshold: IOU_THRESHOLD,
-      classes: ["face"]
-    }, withSeparableConvs ? {
-      anchors: BOX_ANCHORS_SEPARABLE,
-      meanRgb: MEAN_RGB_SEPARABLE
-    } : {
-      anchors: BOX_ANCHORS,
-      withClassScores: true
-    });
-    super(config2);
-  }
-  get withSeparableConvs() {
-    return this.config.withSeparableConvs;
-  }
-  get anchors() {
-    return this.config.anchors;
-  }
-  async locateFaces(input, forwardParams) {
-    const objectDetections = await this.detect(input, forwardParams);
-    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-  }
-  getDefaultModelName() {
-    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return super.extractParamsFromWeigthMap(weightMap);
-  }
-}
-
-// build/src/tinyYolov2/types.js
-
-// build/src/tinyYolov2/index.js
-function createTinyYolov2(weights, withSeparableConvs = true) {
-  const net = new TinyYolov2(withSeparableConvs);
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/tinyFaceDetector/TinyFaceDetectorOptions.js
-class TinyFaceDetectorOptions extends TinyYolov2Options {
-  constructor() {
-    super(...arguments);
-    this._name = "TinyFaceDetectorOptions";
-  }
-}
-
-// build/src/globalApi/ComposableTask.js
-class ComposableTask {
-  async then(onfulfilled) {
-    return onfulfilled(await this.run());
-  }
-  async run() {
-    throw new Error("ComposableTask - run is not implemented");
-  }
-}
-
-// 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 Tensor4 ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes));
-  const results = await computeResults(faces);
-  faces.forEach((f) => f instanceof Tensor4 && f.dispose());
-  return results;
-}
-async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
-  return extractAllFacesAndComputeResults([parentResult], input, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment);
-}
-
-// build/src/tinyFaceDetector/const.js
-const IOU_THRESHOLD2 = 0.4;
-const BOX_ANCHORS2 = [
-  new Point(1.603231, 2.094468),
-  new Point(6.041143, 7.080126),
-  new Point(2.882459, 3.518061),
-  new Point(4.266906, 5.178857),
-  new Point(9.041765, 10.66308)
-];
-const MEAN_RGB = [117.001, 114.697, 97.404];
-
-// build/src/tinyFaceDetector/TinyFaceDetector.js
-class TinyFaceDetector extends TinyYolov2Base {
-  constructor() {
-    const config2 = {
-      withSeparableConvs: true,
-      iouThreshold: IOU_THRESHOLD2,
-      classes: ["face"],
-      anchors: BOX_ANCHORS2,
-      meanRgb: MEAN_RGB,
-      isFirstLayerConv2d: true,
-      filterSizes: [3, 16, 32, 64, 128, 256, 512]
-    };
-    super(config2);
-  }
-  get anchors() {
-    return this.config.anchors;
-  }
-  async locateFaces(input, forwardParams) {
-    const objectDetections = await this.detect(input, forwardParams);
-    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
-  }
-  getDefaultModelName() {
-    return "tiny_face_detector_model";
-  }
-  extractParamsFromWeigthMap(weightMap) {
-    return super.extractParamsFromWeigthMap(weightMap);
-  }
-}
-
-// build/src/globalApi/nets.js
-const nets = {
-  ssdMobilenetv1: new SsdMobilenetv1(),
-  tinyFaceDetector: new TinyFaceDetector(),
-  tinyYolov2: new TinyYolov2(),
-  faceLandmark68Net: new FaceLandmark68Net(),
-  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
-  faceRecognitionNet: new FaceRecognitionNet(),
-  faceExpressionNet: new FaceExpressionNet(),
-  ageGenderNet: new AgeGenderNet()
-};
-const ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-const tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-const tinyYolov23 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-const detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-const detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-const computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-const recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-const predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-const loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-const loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-const loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-const loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-const loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-const loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-const loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-const loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-const loadFaceDetectionModel = loadSsdMobilenetv1Model;
-const locateFaces = ssdMobilenetv1;
-const detectLandmarks = detectFaceLandmarks;
-
-// build/src/globalApi/PredictFaceExpressionsTask.js
-class PredictFaceExpressionsTaskBase extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-}
-class PredictAllFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const faceExpressionsByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.faceExpressionNet.predictExpressions(face))), this.extractedFaces);
-    return parentResults.map((parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]));
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderTask(this, this.input);
-  }
-}
-class PredictSingleFaceExpressionsTask extends PredictFaceExpressionsTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const faceExpressions = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceExpressionNet.predictExpressions(face), this.extractedFaces);
-    return extendWithFaceExpressions(parentResult, faceExpressions);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderTask(this, this.input);
-  }
-}
-class PredictAllFaceExpressionsWithFaceAlignmentTask extends PredictAllFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class PredictSingleFaceExpressionsWithFaceAlignmentTask extends PredictSingleFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/PredictAgeAndGenderTask.js
-class PredictAgeAndGenderTaskBase extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-}
-class PredictAllAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const ageAndGenderByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => await Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), this.extractedFaces);
-    return parentResults.map((parentResult, i) => {
-      const {age, gender, genderProbability} = ageAndGenderByFace[i];
-      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-    });
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsTask(this, this.input);
-  }
-}
-class PredictSingleAgeAndGenderTask extends PredictAgeAndGenderTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const {age, gender, genderProbability} = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.ageGenderNet.predictAgeAndGender(face), this.extractedFaces);
-    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsTask(this, this.input);
-  }
-}
-class PredictAllAgeAndGenderWithFaceAlignmentTask extends PredictAllAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class PredictSingleAgeAndGenderWithFaceAlignmentTask extends PredictSingleAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/ComputeFaceDescriptorsTasks.js
-class ComputeFaceDescriptorsTaskBase extends ComposableTask {
-  constructor(parentTask, input) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-  }
-}
-class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const descriptors = await extractAllFacesAndComputeResults(parentResults, this.input, (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), null, (parentResult) => parentResult.landmarks.align(null, {useDlibAlignment: true}));
-    return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-}
-class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const descriptor = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), null, (parentResult2) => parentResult2.landmarks.align(null, {useDlibAlignment: true}));
-    return extendWithFaceDescriptor(parentResult, descriptor);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/DetectFaceLandmarksTasks.js
-import {
-  Tensor as Tensor5
-} from "@tensorflow/tfjs";
-class DetectFaceLandmarksTaskBase extends ComposableTask {
-  constructor(parentTask, input, useTinyLandmarkNet) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.useTinyLandmarkNet = useTinyLandmarkNet;
-  }
-  get landmarkNet() {
-    return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net;
-  }
-}
-class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-  async run() {
-    const parentResults = await this.parentTask;
-    const detections = parentResults.map((res) => res.detection);
-    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 Tensor5 && f.dispose());
-    return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-}
-class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-  async run() {
-    const parentResult = await this.parentTask;
-    if (!parentResult) {
-      return;
-    }
-    const {detection} = parentResult;
-    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 Tensor5 && f.dispose());
-    return extendWithFaceLandmarks(parentResult, landmarks);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-}
-
-// build/src/globalApi/DetectFacesTasks.js
-class DetectFacesTaskBase extends ComposableTask {
-  constructor(input, options = new SsdMobilenetv1Options()) {
-    super();
-    this.input = input;
-    this.options = options;
-  }
-}
-class DetectAllFacesTask extends DetectFacesTaskBase {
-  async run() {
-    const {input, options} = this;
-    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions ? (input2) => nets.tinyFaceDetector.locateFaces(input2, options) : options instanceof SsdMobilenetv1Options ? (input2) => nets.ssdMobilenetv1.locateFaces(input2, options) : options instanceof TinyYolov2Options ? (input2) => nets.tinyYolov2.locateFaces(input2, options) : null;
-    if (!faceDetectionFunction) {
-      throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");
-    }
-    return faceDetectionFunction(input);
-  }
-  runAndExtendWithFaceDetections() {
-    return new Promise(async (res) => {
-      const detections = await this.run();
-      return res(detections.map((detection) => extendWithFaceDetection({}, detection)));
-    });
-  }
-  withFaceLandmarks(useTinyLandmarkNet = false) {
-    return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
-  }
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
-  }
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);
-  }
-}
-class DetectSingleFaceTask extends DetectFacesTaskBase {
-  async run() {
-    const faceDetections = await new DetectAllFacesTask(this.input, this.options);
-    let faceDetectionWithHighestScore = faceDetections[0];
-    faceDetections.forEach((faceDetection) => {
-      if (faceDetection.score > faceDetectionWithHighestScore.score) {
-        faceDetectionWithHighestScore = faceDetection;
-      }
-    });
-    return faceDetectionWithHighestScore;
-  }
-  runAndExtendWithFaceDetection() {
-    return new Promise(async (res) => {
-      const detection = await this.run();
-      return res(detection ? extendWithFaceDetection({}, detection) : void 0);
-    });
-  }
-  withFaceLandmarks(useTinyLandmarkNet = false) {
-    return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
-  }
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);
-  }
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);
-  }
-}
-
-// build/src/globalApi/detectFaces.js
-function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-  return new DetectSingleFaceTask(input, options);
-}
-function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-  return new DetectAllFacesTask(input, options);
-}
-
-// build/src/globalApi/allFaces.js
-async function allFacesSsdMobilenetv1(input, minConfidence) {
-  console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead");
-  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? {minConfidence} : {})).withFaceLandmarks().withFaceDescriptors();
-}
-async function allFacesTinyYolov2(input, forwardParams = {}) {
-  console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead");
-  return await detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors();
-}
-const allFaces = allFacesSsdMobilenetv1;
-
-// build/src/euclideanDistance.js
-function euclideanDistance(arr1, arr2) {
-  if (arr1.length !== arr2.length)
-    throw new Error("euclideanDistance: arr1.length !== arr2.length");
-  const desc1 = Array.from(arr1);
-  const desc2 = Array.from(arr2);
-  return Math.sqrt(desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + Math.pow(diff, 2), 0));
-}
-
-// build/src/globalApi/FaceMatcher.js
-class FaceMatcher {
-  constructor(inputs, distanceThreshold = 0.6) {
-    this._distanceThreshold = distanceThreshold;
-    const inputArray = Array.isArray(inputs) ? inputs : [inputs];
-    if (!inputArray.length) {
-      throw new Error(`FaceRecognizer.constructor - expected atleast one input`);
-    }
-    let count = 1;
-    const createUniqueLabel = () => `person ${count++}`;
-    this._labeledDescriptors = inputArray.map((desc) => {
-      if (desc instanceof LabeledFaceDescriptors) {
-        return desc;
-      }
-      if (desc instanceof Float32Array) {
-        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
-      }
-      if (desc.descriptor && desc.descriptor instanceof Float32Array) {
-        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) {
-    return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);
-  }
-  matchDescriptor(queryDescriptor) {
-    return this.labeledDescriptors.map(({descriptors, label}) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr);
-  }
-  findBestMatch(queryDescriptor) {
-    const bestMatch = this.matchDescriptor(queryDescriptor);
-    return bestMatch.distance < this.distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance);
-  }
-  toJSON() {
-    return {
-      distanceThreshold: this.distanceThreshold,
-      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())
-    };
-  }
-  static fromJSON(json) {
-    const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld));
-    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);
-  }
-}
-
-// build/src/globalApi/types.js
-
-// build/src/globalApi/index.js
-
-// build/src/tinyFaceDetector/index.js
-function createTinyFaceDetector(weights) {
-  const net = new TinyFaceDetector();
-  net.extractWeights(weights);
-  return net;
-}
-
-// build/src/resizeResults.js
-function resizeResults(results, dimensions) {
-  const {width, height} = new Dimensions(dimensions.width, dimensions.height);
-  if (width <= 0 || height <= 0) {
-    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width, height})}`);
-  }
-  if (Array.isArray(results)) {
-    return results.map((obj) => resizeResults(obj, {width, height}));
-  }
-  if (isWithFaceLandmarks(results)) {
-    const resizedDetection = results.detection.forSize(width, height);
-    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
-    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
-  }
-  if (isWithFaceDetection(results)) {
-    return extendWithFaceDetection(results, results.detection.forSize(width, height));
-  }
-  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
-    return results.forSize(width, height);
-  }
-  return results;
-}
-
-// build/package.json
-var version = "0.5.3";
-
-// build/src/index.js
-import * as tf42 from "@tensorflow/tfjs";
-const node = typeof process !== "undefined" ? process.version : false;
-const browser3 = typeof navigator !== "undefined" ? navigator.userAgent : false;
-const version2 = {faceapi: version, node, browser: browser3};
-export {
-  AgeGenderNet,
-  BoundingBox,
-  Box,
-  ComposableTask,
-  ComputeAllFaceDescriptorsTask,
-  ComputeFaceDescriptorsTaskBase,
-  ComputeSingleFaceDescriptorTask,
-  DetectAllFaceLandmarksTask,
-  DetectAllFacesTask,
-  DetectFaceLandmarksTaskBase,
-  DetectFacesTaskBase,
-  DetectSingleFaceLandmarksTask,
-  DetectSingleFaceTask,
-  Dimensions,
-  FACE_EXPRESSION_LABELS,
-  FaceDetection,
-  FaceDetectionNet,
-  FaceExpressionNet,
-  FaceExpressions,
-  FaceLandmark68Net,
-  FaceLandmark68TinyNet,
-  FaceLandmarkNet,
-  FaceLandmarks,
-  FaceLandmarks5,
-  FaceLandmarks68,
-  FaceMatch,
-  FaceMatcher,
-  FaceRecognitionNet,
-  Gender,
-  LabeledBox,
-  LabeledFaceDescriptors,
-  NetInput,
-  NeuralNetwork,
-  ObjectDetection,
-  Point,
-  PredictedBox,
-  Rect,
-  SsdMobilenetv1,
-  SsdMobilenetv1Options,
-  TinyFaceDetector,
-  TinyFaceDetectorOptions,
-  TinyYolov2,
-  TinyYolov2Options,
-  TinyYolov2SizeType,
-  allFaces,
-  allFacesSsdMobilenetv1,
-  allFacesTinyYolov2,
-  awaitMediaLoaded,
-  bufferToImage,
-  computeFaceDescriptor,
-  createCanvas,
-  createCanvasFromMedia,
-  createFaceDetectionNet,
-  createFaceRecognitionNet,
-  createSsdMobilenetv1,
-  createTinyFaceDetector,
-  createTinyYolov2,
-  detectAllFaces,
-  detectFaceLandmarks,
-  detectFaceLandmarksTiny,
-  detectLandmarks,
-  detectSingleFace,
-  draw_exports as draw,
-  env,
-  euclideanDistance,
-  extendWithAge,
-  extendWithFaceDescriptor,
-  extendWithFaceDetection,
-  extendWithFaceExpressions,
-  extendWithFaceLandmarks,
-  extendWithGender,
-  extractFaceTensors,
-  extractFaces,
-  fetchImage,
-  fetchJson,
-  fetchNetWeights,
-  fetchOrThrow,
-  getContext2dOrThrow,
-  getMediaDimensions,
-  imageTensorToCanvas,
-  imageToSquare,
-  inverseSigmoid,
-  iou,
-  isMediaElement,
-  isMediaLoaded,
-  isWithAge,
-  isWithFaceDetection,
-  isWithFaceExpressions,
-  isWithFaceLandmarks,
-  isWithGender,
-  loadAgeGenderModel,
-  loadFaceDetectionModel,
-  loadFaceExpressionModel,
-  loadFaceLandmarkModel,
-  loadFaceLandmarkTinyModel,
-  loadFaceRecognitionModel,
-  loadSsdMobilenetv1Model,
-  loadTinyFaceDetectorModel,
-  loadTinyYolov2Model,
-  loadWeightMap,
-  locateFaces,
-  matchDimensions,
-  minBbox,
-  nets,
-  nonMaxSuppression,
-  normalize,
-  padToSquare,
-  predictAgeAndGender,
-  recognizeFaceExpressions,
-  resizeResults,
-  resolveInput,
-  shuffleArray,
-  sigmoid,
-  ssdMobilenetv1,
-  tf42 as tf,
-  tinyFaceDetector,
-  tinyYolov23 as tinyYolov2,
-  toNetInput,
-  utils_exports as utils,
-  validateConfig,
-  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
deleted file mode 100644
index ce37833..0000000
--- a/dist/face-api.node.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["build/src/env/../../../src/env/isNodejs.ts", "build/src/draw/../../../src/draw/drawContour.ts", "build/src/classes/../../../src/classes/Dimensions.ts", "build/src/utils/../../../src/utils/index.ts", "build/src/classes/../../../src/classes/Point.ts", "build/src/classes/../../../src/classes/Box.ts", "build/src/classes/../../../src/classes/BoundingBox.ts", "build/src/classes/../../../src/classes/ObjectDetection.ts", "build/src/classes/../../../src/classes/FaceDetection.ts", "build/src/ops/../../../src/ops/iou.ts", "build/src/ops/../../../src/ops/minBbox.ts", "build/src/ops/../../../src/ops/nonMaxSuppression.ts", "build/src/ops/../../../src/ops/normalize.ts", "build/src/ops/../../../src/ops/padToSquare.ts", "build/src/ops/../../../src/ops/shuffleArray.ts", "build/src/ops/../../../src/ops/index.ts", "build/src/classes/../../../src/classes/Rect.ts", "build/src/classes/../../../src/classes/FaceLandmarks.ts", "build/src/classes/../../../src/classes/FaceLandmarks5.ts", "build/src/classes/../../../src/classes/FaceLandmarks68.ts", "build/src/classes/../../../src/classes/FaceMatch.ts", "build/src/classes/../../../src/classes/LabeledBox.ts", "build/src/classes/../../../src/classes/LabeledFaceDescriptors.ts", "build/src/classes/../../../src/classes/PredictedBox.ts", "build/src/factories/../../../src/factories/WithFaceDetection.ts", "build/src/env/../../../src/env/createBrowserEnv.ts", "build/src/env/../../../src/env/createFileSystem.ts", "build/src/env/../../../src/env/createNodejsEnv.ts", "build/src/env/../../../src/env/isBrowser.ts", "build/src/env/../../../src/env/index.ts", "build/src/dom/../../../src/dom/resolveInput.ts", "build/src/dom/../../../src/dom/getContext2dOrThrow.ts", "build/src/draw/../../../src/draw/DrawTextField.ts", "build/src/draw/../../../src/draw/DrawBox.ts", "build/src/draw/../../../src/draw/drawDetections.ts", "build/src/dom/../../../src/dom/isMediaLoaded.ts", "build/src/dom/../../../src/dom/awaitMediaLoaded.ts", "build/src/dom/../../../src/dom/bufferToImage.ts", "build/src/dom/../../../src/dom/getMediaDimensions.ts", "build/src/dom/../../../src/dom/createCanvas.ts", "build/src/dom/../../../src/dom/imageTensorToCanvas.ts", "build/src/dom/../../../src/dom/isMediaElement.ts", "build/src/dom/../../../src/dom/imageToSquare.ts", "build/src/dom/../../../src/dom/NetInput.ts", "build/src/dom/../../../src/dom/toNetInput.ts", "build/src/dom/../../../src/dom/extractFaces.ts", "build/src/dom/../../../src/dom/extractFaceTensors.ts", "build/src/dom/../../../src/dom/fetchOrThrow.ts", "build/src/dom/../../../src/dom/fetchImage.ts", "build/src/dom/../../../src/dom/fetchJson.ts", "build/src/dom/../../../src/dom/fetchNetWeights.ts", "build/src/common/../../../src/common/getModelUris.ts", "build/src/dom/../../../src/dom/loadWeightMap.ts", "build/src/dom/../../../src/dom/matchDimensions.ts", "build/src/../../src/NeuralNetwork.ts", "build/src/common/../../../src/common/depthwiseSeparableConv.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/denseBlock.ts", "build/src/common/../../../src/common/convLayer.ts", "build/src/common/../../../src/common/disposeUnusedWeightTensors.ts", "build/src/common/../../../src/common/extractConvParamsFactory.ts", "build/src/common/../../../src/common/extractFCParamsFactory.ts", "build/src/common/../../../src/common/types.ts", "build/src/common/../../../src/common/extractSeparableConvParamsFactory.ts", "build/src/common/../../../src/common/extractWeightEntryFactory.ts", "build/src/common/../../../src/common/extractWeightsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractorsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParams.ts", "build/src/common/../../../src/common/loadConvParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/loadParamsFactory.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "build/src/common/../../../src/common/fullyConnectedLayer.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParams.ts", "build/src/faceProcessor/../../../src/faceProcessor/extractParamsFromWeigthMap.ts", "build/src/faceProcessor/../../../src/faceProcessor/util.ts", "build/src/faceProcessor/../../../src/faceProcessor/FaceProcessor.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressions.ts", "build/src/faceExpressionNet/../../../src/faceExpressionNet/FaceExpressionNet.ts", "build/src/factories/../../../src/factories/WithFaceExpressions.ts", "build/src/draw/../../../src/draw/drawFaceExpressions.ts", "build/src/factories/../../../src/factories/WithFaceLandmarks.ts", "build/src/draw/../../../src/draw/DrawFaceLandmarks.ts", "build/src/draw/../../../src/draw/index.ts", "build/src/xception/../../../src/xception/extractParams.ts", "build/src/xception/../../../src/xception/extractParamsFromWeigthMap.ts", "build/src/xception/../../../src/xception/TinyXception.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParams.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/extractParamsFromWeigthMap.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/types.ts", "build/src/ageGenderNet/../../../src/ageGenderNet/AgeGenderNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68Net.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/extractParamsTiny.ts", "build/src/faceFeatureExtractor/../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "build/src/faceLandmarkNet/../../../src/faceLandmarkNet/index.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/scaleLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/convLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParams.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/residualLayer.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/FaceRecognitionNet.ts", "build/src/faceRecognitionNet/../../../src/faceRecognitionNet/index.ts", "build/src/factories/../../../src/factories/WithFaceDescriptor.ts", "build/src/factories/../../../src/factories/WithAge.ts", "build/src/factories/../../../src/factories/WithGender.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParams.ts", "build/src/ssdMobilenetv1/../../../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "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", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParams.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/extractParamsFromWeigthMap.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Options.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2Base.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/TinyYolov2.ts", "build/src/tinyYolov2/../../../src/tinyYolov2/index.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "build/src/globalApi/../../../src/globalApi/ComposableTask.ts", "build/src/globalApi/../../../src/globalApi/extractFacesAndComputeResults.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/const.ts", "build/src/tinyFaceDetector/../../../src/tinyFaceDetector/TinyFaceDetector.ts", "build/src/globalApi/../../../src/globalApi/nets.ts", "build/src/globalApi/../../../src/globalApi/PredictFaceExpressionsTask.ts", "build/src/globalApi/../../../src/globalApi/PredictAgeAndGenderTask.ts", "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", "build/src/../../src/index.ts"],
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-  "names": []
-}
diff --git a/example/index.html b/example/index.html
index bcbf4fd..7d19d07 100644
--- a/example/index.html
+++ b/example/index.html
@@ -5,8 +5,8 @@
     <meta http-equiv="content-type">
     <meta content="text/html">
     <meta charset="UTF-8">
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.3.0/tf.es2017.js"></script>
-    <script src="https://vladmandic.github.io/face-api/dist/face-api.js"></script>
+    <!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.6.0/tf.min.js"></script> -->
+    <script src="../dist/face-api.js"></script>
     <style>
       body { font-family: monospace; background: black; color: white; font-size: 16px; line-height: 22px; margin: 0; }
     </style>
@@ -27,7 +27,7 @@
       // helper function to pretty-print json object to string
       function str(json) {
         let text = '<font color="lightblue">';
-        text += JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ');
+        text += json ? JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ') : '';
         text += '</font>';
         return text;
       }
@@ -120,11 +120,15 @@
       async function main() {
         // initialize tfjs
         log('FaceAPI Test');
+        window.tf = faceapi.tf;
         await faceapi.tf.setBackend('webgl');
         await faceapi.tf.enableProdMode();
         await faceapi.tf.ENV.set('DEBUG', false);
+        await faceapi.tf.ready();
+
         // check version
-        log(`Version: ${str(faceapi.version)}`);
+        log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`);
+        log(`Flags: ${JSON.stringify(faceapi.tf.ENV.flags)}`);
 
         // load face-api models
         log('Loading FaceAPI models');
diff --git a/package-lock.json b/package-lock.json
index 36399ca..ab90c4a 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -77,9 +77,9 @@
       "integrity": "sha512-nU9WNSGpEU6GzKo5bvJBMa/OZRe1bR5Z2W6T0XiEY8CBiPNS+oJFJNm0NY8kQj/WnDS0Hfue38P46q7gV/9XMA=="
     },
     "@types/node": {
-      "version": "14.11.7",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.11.7.tgz",
-      "integrity": "sha512-hSEXknS4KiayUdZ7401J/T6eykXHJkDEipnyQMJ4/GstK4kWjbHnwXlcpvIWfPKiEH1JU96DkbzJ1nHRmpmKLw=="
+      "version": "14.11.8",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.11.8.tgz",
+      "integrity": "sha512-KPcKqKm5UKDkaYPTuXSx8wEP7vE9GnuaXIZKijwRYcePpZFDVuy2a57LarFKiORbHOuTOOwYzxVxcUzsh2P2Pw=="
     },
     "@types/node-fetch": {
       "version": "2.5.7",
@@ -436,15 +436,15 @@
       }
     },
     "tslib": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.0.2.tgz",
-      "integrity": "sha512-wAH28hcEKwna96/UacuWaVspVLkg4x1aDM9JlzqaQTOFczCktkVAb5fmXChgandR1EraDPs2w8P+ozM+oafwxg==",
+      "version": "2.0.3",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.0.3.tgz",
+      "integrity": "sha512-uZtkfKblCEQtZKBF6EBXVZeQNl82yqtDQdv+eck8u7tdPxjLu2/lp5/uPW+um2tpuxINHWy3GhiccY7QgEaVHQ==",
       "dev": true
     },
     "typescript": {
-      "version": "4.1.0-dev.20201008",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.0-dev.20201008.tgz",
-      "integrity": "sha512-+g2TfvYueD60iu2YXz5czsDRdY4M/h9RvmwrorTTNmnTQaUOeb7spT4EyHa1g5TjhqHa/8+lCgCdPkuEQqg9WA==",
+      "version": "4.1.0-dev.20201011",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.0-dev.20201011.tgz",
+      "integrity": "sha512-+Lfj2Q+dXFAJbfB1jQQRlhTssgl0CQVo8a+CNzlNXsnC1cN1yoH40wkEjWd6uB8NGkamxKda1xrEb2miJtpP5g==",
       "dev": true
     },
     "wrap-ansi": {
diff --git a/package.json b/package.json
index 98216cb..71cae06 100644
--- a/package.json
+++ b/package.json
@@ -1,23 +1,18 @@
 {
   "name": "@vladmandic/face-api",
-  "version": "0.5.3",
+  "version": "0.6.0",
   "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
   "main": "build/src/index.js",
-  "browser": "dist/face-api.esm.js",
-  "typings": "./build/src/index.d.ts",
+  "browser": "dist/face-api.js",
+  "typings": "build/src/index.d.ts",
   "engines": {
     "node": ">=12.0.0"
   },
   "type": "module",
   "scripts": {
-    "compile": "tsc",
-    "clean": "rimraf build/ dist/",
-    "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"
+    "build-esm": "esbuild --bundle --format=esm --target=esnext --platform=browser --sourcemap --outfile=./dist/face-api.esm.js --external:@tensorflow/tfjs --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "build-iife": "esbuild --bundle --format=iife --target=esnext --platform=browser --sourcemap --outfile=./dist/face-api.js --global-name=faceapi --minify --log-level=error --tsconfig=./tsconfig.json build/src/index.js",
+    "build": "rimraf build/ dist/ && tsc && npm run build-esm && npm run build-iife"
   },
   "keywords": [
     "tensorflow",
@@ -45,11 +40,11 @@
     "@tensorflow/tfjs": "^2.6.0"
   },
   "devDependencies": {
-    "@types/node": "^14.11.7",
+    "@types/node": "^14.11.8",
     "esbuild": "^0.6.34",
     "rimraf": "^3.0.2",
     "ts-node": "^9.0.0",
-    "tslib": "^2.0.2",
-    "typescript": "^4.1.0-dev.20201008"
+    "tslib": "^2.0.3",
+    "typescript": "^4.1.0-dev.20201011"
   }
 }
diff --git a/src/NeuralNetwork.ts b/src/NeuralNetwork.ts
index b6cfd3a..d433741 100644
--- a/src/NeuralNetwork.ts
+++ b/src/NeuralNetwork.ts
@@ -114,7 +114,6 @@ export abstract class NeuralNetwork<TNetParams> {
       filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))
     )
     const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
-
     const manifest = JSON.parse((await readFile(manifestUri)).toString())
     const weightMap = await loadWeights(manifest, modelBaseUri)
 
diff --git a/src/Platform.ts b/src/Platform.ts
new file mode 100644
index 0000000..742d73f
--- /dev/null
+++ b/src/Platform.ts
@@ -0,0 +1,25 @@
+export class PlatformBrowser {
+  private textEncoder: TextEncoder;
+
+  fetch(path: string, init?: RequestInit): Promise<Response> {
+    return fetch(path, init);
+  }
+
+  now(): number {
+    return performance.now();
+  }
+
+  encode(text: string, encoding: string): Uint8Array {
+    if (encoding !== 'utf-8' && encoding !== 'utf8') {
+      throw new Error(
+          `Browser's encoder only supports utf-8, but got ${encoding}`);
+    }
+    if (this.textEncoder == null) {
+      this.textEncoder = new TextEncoder();
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes: Uint8Array, encoding: string): string {
+    return new TextDecoder(encoding).decode(bytes);
+  }
+}
diff --git a/src/index.ts b/src/index.ts
index b540da3..1dfacdb 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -24,6 +24,9 @@ export * from './NeuralNetwork';
 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
+const node = (typeof process !== 'undefined');
+const browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');
 export const version = { faceapi: pkg.version, node, browser };
+
+import {PlatformBrowser} from './Platform';
+if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);
diff --git a/tsconfig.json b/tsconfig.json
index f3bec64..a07a2d1 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -1,7 +1,7 @@
 {
   "compilerOptions": {
     "lib": ["es2018", "dom"],
-    "module": "es2020",
+    "module": "esnext",
     "moduleResolution": "node",
     "outDir": "build",
     "target": "es2018",